Datasets:

infinityofspace

/

python_codestyles-mixed1-500

like 0

"""simple docstring""" import json import os import unittest from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, require_torch from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class A_ (lowercase__ ,unittest.TestCase ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : Optional[int] = LEDTokenizer SCREAMING_SNAKE_CASE__ : Dict = LEDTokenizerFast SCREAMING_SNAKE_CASE__ : List[str] = True def UpperCamelCase__ ( self ): """simple docstring""" super().setUp() UpperCAmelCase_ : List[Any] = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", ] UpperCAmelCase_ : Union[str, Any] = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) ) UpperCAmelCase_ : Optional[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] UpperCAmelCase_ : Union[str, Any] = {"unk_token": "<unk>"} UpperCAmelCase_ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] ) UpperCAmelCase_ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file , "w" , encoding="utf-8" ) as fp: fp.write(json.dumps(lowercase_ ) + "\n" ) with open(self.merges_file , "w" , encoding="utf-8" ) as fp: fp.write("\n".join(lowercase_ ) ) def UpperCamelCase__ ( self , **lowercase_ ): """simple docstring""" kwargs.update(self.special_tokens_map ) return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ ) def UpperCamelCase__ ( self , **lowercase_ ): """simple docstring""" kwargs.update(self.special_tokens_map ) return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **lowercase_ ) def UpperCamelCase__ ( self , lowercase_ ): """simple docstring""" return "lower newer", "lower newer" @cached_property def UpperCamelCase__ ( self ): """simple docstring""" return LEDTokenizer.from_pretrained("allenai/led-base-16384" ) @cached_property def UpperCamelCase__ ( self ): """simple docstring""" return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" ) @require_torch def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : int = ["A long paragraph for summarization.", "Another paragraph for summarization."] UpperCAmelCase_ : Dict = [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2] for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: UpperCAmelCase_ : int = tokenizer(lowercase_ , max_length=len(lowercase_ ) , padding=lowercase_ , return_tensors="pt" ) self.assertIsInstance(lowercase_ , lowercase_ ) self.assertEqual((2, 9) , batch.input_ids.shape ) self.assertEqual((2, 9) , batch.attention_mask.shape ) UpperCAmelCase_ : str = batch.input_ids.tolist()[0] self.assertListEqual(lowercase_ , lowercase_ ) @require_torch def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Optional[int] = ["A long paragraph for summarization.", "Another paragraph for summarization."] for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: UpperCAmelCase_ : str = tokenizer(lowercase_ , padding=lowercase_ , return_tensors="pt" ) self.assertIn("input_ids" , lowercase_ ) self.assertIn("attention_mask" , lowercase_ ) self.assertNotIn("labels" , lowercase_ ) self.assertNotIn("decoder_attention_mask" , lowercase_ ) @require_torch def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Optional[Any] = [ "Summary of the text.", "Another summary.", ] for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: UpperCAmelCase_ : List[Any] = tokenizer(text_target=lowercase_ , max_length=32 , padding="max_length" , return_tensors="pt" ) self.assertEqual(32 , targets["input_ids"].shape[1] ) @require_torch def UpperCamelCase__ ( self ): """simple docstring""" for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: UpperCAmelCase_ : Tuple = tokenizer( ["I am a small frog" * 1024, "I am a small frog"] , padding=lowercase_ , truncation=lowercase_ , return_tensors="pt" ) self.assertIsInstance(lowercase_ , lowercase_ ) self.assertEqual(batch.input_ids.shape , (2, 5122) ) @require_torch def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : List[Any] = ["A long paragraph for summarization."] UpperCAmelCase_ : Optional[int] = [ "Summary of the text.", ] for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: UpperCAmelCase_ : Optional[Any] = tokenizer(lowercase_ , return_tensors="pt" ) UpperCAmelCase_ : List[str] = tokenizer(text_target=lowercase_ , return_tensors="pt" ) UpperCAmelCase_ : Optional[Any] = inputs["input_ids"] UpperCAmelCase_ : int = targets["input_ids"] self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() ) self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() ) self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() ) self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() ) @require_torch def UpperCamelCase__ ( self ): """simple docstring""" for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]: UpperCAmelCase_ : Dict = ["Summary of the text.", "Another summary."] UpperCAmelCase_ : Optional[Any] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]] UpperCAmelCase_ : Optional[Any] = tokenizer(lowercase_ , padding=lowercase_ ) UpperCAmelCase_ : Tuple = [[0] * len(lowercase_ ) for x in encoded_output["input_ids"]] UpperCAmelCase_ : Optional[Any] = tokenizer.pad(lowercase_ ) self.assertSequenceEqual(outputs["global_attention_mask"] , lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" pass def UpperCamelCase__ ( self ): """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): UpperCAmelCase_ : Any = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ ) UpperCAmelCase_ : str = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ ) UpperCAmelCase_ : Union[str, Any] = "A, <mask> AllenNLP sentence." UpperCAmelCase_ : List[Any] = tokenizer_r.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ ) UpperCAmelCase_ : Dict = tokenizer_p.encode_plus(lowercase_ , add_special_tokens=lowercase_ , return_token_type_ids=lowercase_ ) self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) ) self.assertEqual( sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , ) UpperCAmelCase_ : Optional[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] ) UpperCAmelCase_ : Optional[Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] ) self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] ) self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] ) self.assertSequenceEqual( lowercase_ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] ) self.assertSequenceEqual( lowercase_ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )

import argparse from argparse import Namespace import torch from torch import nn from transformers import XGLMConfig, XGLMForCausalLM def __UpperCamelCase ( _A ): lowerCAmelCase_ = [ '''decoder.version''', '''decoder.output_projection.weight''', '''_float_tensor''', '''decoder.embed_positions._float_tensor''', ] for k in ignore_keys: state_dict.pop(_A , _A ) def __UpperCamelCase ( _A ): lowerCAmelCase_ , lowerCAmelCase_ = emb.weight.shape lowerCAmelCase_ = nn.Linear(_A , _A , bias=_A ) lowerCAmelCase_ = emb.weight.data return lin_layer def __UpperCamelCase ( _A ): lowerCAmelCase_ = torch.load(_A , map_location='''cpu''' ) lowerCAmelCase_ = Namespace(**checkpoint['''cfg''']['''model'''] ) lowerCAmelCase_ = checkpoint['''model'''] remove_ignore_keys_(_A ) lowerCAmelCase_ = state_dict['''decoder.embed_tokens.weight'''].shape[0] lowerCAmelCase_ = {key.replace('''decoder''' , '''model''' ): val for key, val in state_dict.items()} lowerCAmelCase_ = XGLMConfig( vocab_size=_A , max_position_embeddings=args.max_target_positions , num_layers=args.decoder_layers , attention_heads=args.decoder_attention_heads , ffn_dim=args.decoder_ffn_embed_dim , d_model=args.decoder_embed_dim , layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''gelu''' , scale_embedding=not args.no_scale_embedding , tie_word_embeddings=args.share_decoder_input_output_embed , ) lowerCAmelCase_ = XGLMForCausalLM(_A ) lowerCAmelCase_ = model.load_state_dict(_A , strict=_A ) print(_A ) lowerCAmelCase_ = make_linear_from_emb(model.model.embed_tokens ) return model if __name__ == "__main__": _A = argparse.ArgumentParser() # Required parameters parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''') parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''') _A = parser.parse_args() _A = convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path) model.save_pretrained(args.pytorch_dump_folder_path)

'''simple docstring''' from __future__ import annotations import pandas as pd def lowercase_ ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int ): """simple docstring""" __UpperCAmelCase : List[str] = [0] * no_of_processes __UpperCAmelCase : Optional[Any] = [0] * no_of_processes # Copy the burst time into remaining_time[] for i in range(lowerCAmelCase__ ): __UpperCAmelCase : Union[str, Any] = burst_time[i] __UpperCAmelCase : Optional[int] = 0 __UpperCAmelCase : int = 0 __UpperCAmelCase : Tuple = 999999999 __UpperCAmelCase : List[str] = 0 __UpperCAmelCase : Optional[int] = False # Process until all processes are completed while complete != no_of_processes: for j in range(lowerCAmelCase__ ): if arrival_time[j] <= increment_time and remaining_time[j] > 0: if remaining_time[j] < minm: __UpperCAmelCase : int = remaining_time[j] __UpperCAmelCase : Tuple = j __UpperCAmelCase : Union[str, Any] = True if not check: increment_time += 1 continue remaining_time[short] -= 1 __UpperCAmelCase : Optional[int] = remaining_time[short] if minm == 0: __UpperCAmelCase : List[str] = 999999999 if remaining_time[short] == 0: complete += 1 __UpperCAmelCase : Tuple = False # Find finish time of current process __UpperCAmelCase : List[Any] = increment_time + 1 # Calculate waiting time __UpperCAmelCase : str = finish_time - arrival_time[short] __UpperCAmelCase : Any = finar - burst_time[short] if waiting_time[short] < 0: __UpperCAmelCase : List[str] = 0 # Increment time increment_time += 1 return waiting_time def lowercase_ ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : list[int] ): """simple docstring""" __UpperCAmelCase : int = [0] * no_of_processes for i in range(lowerCAmelCase__ ): __UpperCAmelCase : List[str] = burst_time[i] + waiting_time[i] return turn_around_time def lowercase_ ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int ): """simple docstring""" __UpperCAmelCase : Any = 0 __UpperCAmelCase : Tuple = 0 for i in range(lowerCAmelCase__ ): __UpperCAmelCase : Optional[int] = total_waiting_time + waiting_time[i] __UpperCAmelCase : Dict = total_turn_around_time + turn_around_time[i] print(f'Average waiting time = {total_waiting_time / no_of_processes:.5f}' ) print("""Average turn around time =""" , total_turn_around_time / no_of_processes ) if __name__ == "__main__": print('''Enter how many process you want to analyze''') _UpperCamelCase = int(input()) _UpperCamelCase = [0] * no_of_processes _UpperCamelCase = [0] * no_of_processes _UpperCamelCase = list(range(1, no_of_processes + 1)) for i in range(no_of_processes): print('''Enter the arrival time and burst time for process:--''' + str(i + 1)) _UpperCamelCase , _UpperCamelCase = map(int, input().split()) _UpperCamelCase = calculate_waitingtime(arrival_time, burst_time, no_of_processes) _UpperCamelCase = burst_time _UpperCamelCase = no_of_processes _UpperCamelCase = waiting_time _UpperCamelCase = calculate_turnaroundtime(bt, n, wt) calculate_average_times(waiting_time, turn_around_time, no_of_processes) _UpperCamelCase = pd.DataFrame( list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)), columns=[ '''Process''', '''BurstTime''', '''ArrivalTime''', '''WaitingTime''', '''TurnAroundTime''', ], ) # Printing the dataFrame pd.set_option('''display.max_rows''', fcfs.shape[0] + 1) print(fcfs)

'''simple docstring''' import unittest from transformers import ( MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TextClassificationPipeline, pipeline, ) from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow from .test_pipelines_common import ANY # These 2 model types require different inputs than those of the usual text models. _UpperCamelCase = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''} @is_pipeline_test class _A ( unittest.TestCase ): _SCREAMING_SNAKE_CASE : Optional[int] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING _SCREAMING_SNAKE_CASE : int = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING if model_mapping is not None: _SCREAMING_SNAKE_CASE : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP} if tf_model_mapping is not None: _SCREAMING_SNAKE_CASE : Union[str, Any] = { config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP } @require_torch def __A ( self ) -> Tuple: '''simple docstring''' __UpperCAmelCase : int = pipeline( task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" ) __UpperCAmelCase : List[Any] = text_classifier("""This is great !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] ) __UpperCAmelCase : int = text_classifier("""This is great !""" , top_k=2 ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}] ) __UpperCAmelCase : Optional[int] = text_classifier(["""This is great !""", """This is bad"""] , top_k=2 ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [ [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}], [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}], ] , ) __UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" , top_k=1 ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] ) # Legacy behavior __UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" , return_all_scores=__UpperCAmelCase ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] ) __UpperCAmelCase : Dict = text_classifier("""This is great !""" , return_all_scores=__UpperCAmelCase ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]] ) __UpperCAmelCase : str = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=__UpperCAmelCase ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [ [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}], [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}], ] , ) __UpperCAmelCase : Any = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=__UpperCAmelCase ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [ {"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_0""", """score""": 0.504}, ] , ) @require_torch def __A ( self ) -> Dict: '''simple docstring''' import torch __UpperCAmelCase : Any = pipeline( task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" , device=torch.device("""cpu""" ) , ) __UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] ) @require_tf def __A ( self ) -> Any: '''simple docstring''' __UpperCAmelCase : Any = pipeline( task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""tf""" ) __UpperCAmelCase : int = text_classifier("""This is great !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """LABEL_0""", """score""": 0.504}] ) @slow @require_torch def __A ( self ) -> List[str]: '''simple docstring''' __UpperCAmelCase : int = pipeline("""text-classification""" ) __UpperCAmelCase : int = text_classifier("""This is great !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 1.0}] ) __UpperCAmelCase : Union[str, Any] = text_classifier("""This is bad !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] ) __UpperCAmelCase : Any = text_classifier("""Birds are a type of animal""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 0.988}] ) @slow @require_tf def __A ( self ) -> Optional[Any]: '''simple docstring''' __UpperCAmelCase : str = pipeline("""text-classification""" , framework="""tf""" ) __UpperCAmelCase : Union[str, Any] = text_classifier("""This is great !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 1.0}] ) __UpperCAmelCase : int = text_classifier("""This is bad !""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] ) __UpperCAmelCase : str = text_classifier("""Birds are a type of animal""" ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": """POSITIVE""", """score""": 0.988}] ) def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Any: '''simple docstring''' __UpperCAmelCase : Any = TextClassificationPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase ) return text_classifier, ["HuggingFace is in", "This is another test"] def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]: '''simple docstring''' __UpperCAmelCase : int = text_classifier.model # Small inputs because BartTokenizer tiny has maximum position embeddings = 22 __UpperCAmelCase : Union[str, Any] = """HuggingFace is in""" __UpperCAmelCase : Any = text_classifier(__UpperCAmelCase ) self.assertEqual(nested_simplify(__UpperCAmelCase ) , [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] ) self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() ) __UpperCAmelCase : Optional[int] = ["""HuggingFace is in """, """Paris is in France"""] __UpperCAmelCase : Any = text_classifier(__UpperCAmelCase ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}, {"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] , ) self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() ) self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values() ) # Forcing to get all results with `top_k=None` # This is NOT the legacy format __UpperCAmelCase : Any = text_classifier(__UpperCAmelCase , top_k=__UpperCAmelCase ) __UpperCAmelCase : Any = len(model.config.idalabel.values() ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [[{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] * N, [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] * N] , ) __UpperCAmelCase : str = {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""} __UpperCAmelCase : Optional[int] = text_classifier(__UpperCAmelCase ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , {"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )} , ) self.assertTrue(outputs["""label"""] in model.config.idalabel.values() ) # This might be used a text pair, but tokenizer + pipe interaction # makes it hard to understand that it's not using the pair properly # https://github.com/huggingface/transformers/issues/17305 # We disabled this usage instead as it was outputting wrong outputs. __UpperCAmelCase : Union[str, Any] = [["""HuggingFace is in """, """Paris is in France"""]] with self.assertRaises(__UpperCAmelCase ): text_classifier(__UpperCAmelCase ) # This used to be valid for doing text pairs # We're keeping it working because of backward compatibility __UpperCAmelCase : Tuple = text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]] ) self.assertEqual( nested_simplify(__UpperCAmelCase ) , [{"""label""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase )}] , ) self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )

"""simple docstring""" import argparse import torch from safetensors.torch import load_file from diffusers import StableDiffusionPipeline def A ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ): '''simple docstring''' SCREAMING_SNAKE_CASE__ = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE , torch_dtype=torch.floataa ) # load LoRA weight from .safetensors SCREAMING_SNAKE_CASE__ = load_file(_SCREAMING_SNAKE_CASE ) SCREAMING_SNAKE_CASE__ = [] # directly update weight in diffusers model for key in state_dict: # it is suggested to print out the key, it usually will be something like below # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" # as we have set the alpha beforehand, so just skip if ".alpha" in key or key in visited: continue if "text" in key: SCREAMING_SNAKE_CASE__ = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" ) SCREAMING_SNAKE_CASE__ = pipeline.text_encoder else: SCREAMING_SNAKE_CASE__ = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" ) SCREAMING_SNAKE_CASE__ = pipeline.unet # find the target layer SCREAMING_SNAKE_CASE__ = layer_infos.pop(0 ) while len(_SCREAMING_SNAKE_CASE ) > -1: try: SCREAMING_SNAKE_CASE__ = curr_layer.__getattr__(_SCREAMING_SNAKE_CASE ) if len(_SCREAMING_SNAKE_CASE ) > 0: SCREAMING_SNAKE_CASE__ = layer_infos.pop(0 ) elif len(_SCREAMING_SNAKE_CASE ) == 0: break except Exception: if len(_SCREAMING_SNAKE_CASE ) > 0: temp_name += "_" + layer_infos.pop(0 ) else: SCREAMING_SNAKE_CASE__ = layer_infos.pop(0 ) SCREAMING_SNAKE_CASE__ = [] if "lora_down" in key: pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) ) pair_keys.append(_SCREAMING_SNAKE_CASE ) else: pair_keys.append(_SCREAMING_SNAKE_CASE ) pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) ) # update weight if len(state_dict[pair_keys[0]].shape ) == 4: SCREAMING_SNAKE_CASE__ = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa ) SCREAMING_SNAKE_CASE__ = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa ) curr_layer.weight.data += alpha * torch.mm(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).unsqueeze(2 ).unsqueeze(3 ) else: SCREAMING_SNAKE_CASE__ = state_dict[pair_keys[0]].to(torch.floataa ) SCREAMING_SNAKE_CASE__ = state_dict[pair_keys[1]].to(torch.floataa ) curr_layer.weight.data += alpha * torch.mm(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) # update visited list for item in pair_keys: visited.append(_SCREAMING_SNAKE_CASE ) return pipeline if __name__ == "__main__": A_ : Tuple = argparse.ArgumentParser() parser.add_argument( "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format." ) parser.add_argument( "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." ) parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") parser.add_argument( "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors" ) parser.add_argument( "--lora_prefix_text_encoder", default="lora_te", type=str, help="The prefix of text encoder weight in safetensors", ) parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW") parser.add_argument( "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not." ) parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") A_ : Dict = parser.parse_args() A_ : List[str] = args.base_model_path A_ : List[str] = args.checkpoint_path A_ : Tuple = args.dump_path A_ : Union[str, Any] = args.lora_prefix_unet A_ : Optional[int] = args.lora_prefix_text_encoder A_ : Any = args.alpha A_ : int = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha) A_ : int = pipe.to(args.device) pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

'''simple docstring''' import os import tempfile import unittest from pathlib import Path from transformers import AutoConfig, is_torch_available from transformers.testing_utils import require_torch, torch_device if is_torch_available(): from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments @require_torch class _lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' def lowercase (self , UpperCAmelCase ) -> Union[str, Any]: for model_result in results.values(): for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ): _snake_case = model_result["""result"""][batch_size][sequence_length] self.assertIsNotNone(UpperCAmelCase ) def lowercase (self ) -> Optional[int]: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) def lowercase (self ) -> Dict: _snake_case = """sgugger/tiny-distilbert-classification""" _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , only_pretrain_model=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) def lowercase (self ) -> Optional[Any]: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , torchscript=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def lowercase (self ) -> Optional[int]: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , fpaa=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) def lowercase (self ) -> Union[str, Any]: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = AutoConfig.from_pretrained(UpperCAmelCase ) # set architectures equal to `None` _snake_case = None _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) def lowercase (self ) -> Optional[int]: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_train_result ) self.check_results_dict_not_empty(results.memory_train_result ) @unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" ) def lowercase (self ) -> Tuple: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=UpperCAmelCase , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_train_result ) self.check_results_dict_not_empty(results.memory_train_result ) def lowercase (self ) -> Union[str, Any]: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = AutoConfig.from_pretrained(UpperCAmelCase ) _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) def lowercase (self ) -> Dict: _snake_case = """sshleifer/tinier_bart""" _snake_case = AutoConfig.from_pretrained(UpperCAmelCase ) _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_inference_result ) self.check_results_dict_not_empty(results.memory_inference_result ) def lowercase (self ) -> Any: _snake_case = """sshleifer/tiny-gpt2""" _snake_case = AutoConfig.from_pretrained(UpperCAmelCase ) _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_train_result ) self.check_results_dict_not_empty(results.memory_train_result ) def lowercase (self ) -> int: _snake_case = """sshleifer/tinier_bart""" _snake_case = AutoConfig.from_pretrained(UpperCAmelCase ) _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] ) _snake_case = benchmark.run() self.check_results_dict_not_empty(results.time_train_result ) self.check_results_dict_not_empty(results.memory_train_result ) def lowercase (self ) -> str: _snake_case = """sshleifer/tiny-gpt2""" with tempfile.TemporaryDirectory() as tmp_dir: _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , save_to_csv=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(UpperCAmelCase , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(UpperCAmelCase , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(UpperCAmelCase , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(UpperCAmelCase , """train_time.csv""" ) , env_info_csv_file=os.path.join(UpperCAmelCase , """env.csv""" ) , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) benchmark.run() self.assertTrue(Path(os.path.join(UpperCAmelCase , """inf_time.csv""" ) ).exists() ) self.assertTrue(Path(os.path.join(UpperCAmelCase , """train_time.csv""" ) ).exists() ) self.assertTrue(Path(os.path.join(UpperCAmelCase , """inf_mem.csv""" ) ).exists() ) self.assertTrue(Path(os.path.join(UpperCAmelCase , """train_mem.csv""" ) ).exists() ) self.assertTrue(Path(os.path.join(UpperCAmelCase , """env.csv""" ) ).exists() ) def lowercase (self ) -> int: _snake_case = """sshleifer/tiny-gpt2""" def _check_summary_is_not_empty(UpperCAmelCase ): self.assertTrue(hasattr(UpperCAmelCase , """sequential""" ) ) self.assertTrue(hasattr(UpperCAmelCase , """cumulative""" ) ) self.assertTrue(hasattr(UpperCAmelCase , """current""" ) ) self.assertTrue(hasattr(UpperCAmelCase , """total""" ) ) with tempfile.TemporaryDirectory() as tmp_dir: _snake_case = PyTorchBenchmarkArguments( models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(UpperCAmelCase , """log.txt""" ) , log_print=UpperCAmelCase , trace_memory_line_by_line=UpperCAmelCase , multi_process=UpperCAmelCase , ) _snake_case = PyTorchBenchmark(UpperCAmelCase ) _snake_case = benchmark.run() _check_summary_is_not_empty(result.inference_summary ) _check_summary_is_not_empty(result.train_summary ) self.assertTrue(Path(os.path.join(UpperCAmelCase , """log.txt""" ) ).exists() )

import inspect import unittest import numpy as np from tests.test_modeling_common import floats_tensor from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel if is_vision_available(): from transformers import MaskFormerImageProcessor if is_vision_available(): from PIL import Image class __snake_case : def __init__( self : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Any=2 , _UpperCAmelCase : Optional[int]=True , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : List[str]=10 , _UpperCAmelCase : List[Any]=3 , _UpperCAmelCase : Union[str, Any]=32 * 4 , _UpperCAmelCase : List[Any]=32 * 6 , _UpperCAmelCase : int=4 , _UpperCAmelCase : Optional[int]=32 , ) -> Dict: '''simple docstring''' _lowerCAmelCase : Union[str, Any] = parent _lowerCAmelCase : Tuple = batch_size _lowerCAmelCase : List[str] = is_training _lowerCAmelCase : str = use_auxiliary_loss _lowerCAmelCase : Dict = num_queries _lowerCAmelCase : List[str] = num_channels _lowerCAmelCase : str = min_size _lowerCAmelCase : List[Any] = max_size _lowerCAmelCase : str = num_labels _lowerCAmelCase : Tuple = mask_feature_size def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> str: '''simple docstring''' _lowerCAmelCase : Any = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to( _UpperCAmelCase ) _lowerCAmelCase : Dict = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_UpperCAmelCase ) _lowerCAmelCase : Any = ( torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_UpperCAmelCase ) > 0.5 ).float() _lowerCAmelCase : Union[str, Any] = (torch.rand((self.batch_size, self.num_labels) , device=_UpperCAmelCase ) > 0.5).long() _lowerCAmelCase : List[Any] = self.get_config() return config, pixel_values, pixel_mask, mask_labels, class_labels def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict: '''simple docstring''' return MaskFormerConfig.from_backbone_and_decoder_configs( backbone_config=SwinConfig( depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig( decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , ) def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple: '''simple docstring''' _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : str = self.prepare_config_and_inputs() _lowerCAmelCase : List[str] = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask} return config, inputs_dict def SCREAMING_SNAKE_CASE ( self : Optional[int] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any ) -> Optional[Any]: '''simple docstring''' _lowerCAmelCase : List[Any] = output.encoder_hidden_states _lowerCAmelCase : int = output.pixel_decoder_hidden_states _lowerCAmelCase : List[str] = output.transformer_decoder_hidden_states self.parent.assertTrue(len(_UpperCAmelCase ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(_UpperCAmelCase ) , len(config.backbone_config.depths ) ) self.parent.assertTrue(len(_UpperCAmelCase ) , config.decoder_config.decoder_layers ) def SCREAMING_SNAKE_CASE ( self : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Tuple , _UpperCAmelCase : Dict , _UpperCAmelCase : str=False ) -> Dict: '''simple docstring''' with torch.no_grad(): _lowerCAmelCase : Union[str, Any] = MaskFormerModel(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() _lowerCAmelCase : Union[str, Any] = model(pixel_values=_UpperCAmelCase , pixel_mask=_UpperCAmelCase ) _lowerCAmelCase : List[str] = model(_UpperCAmelCase , output_hidden_states=_UpperCAmelCase ) # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the # encoder and pixel decoder self.parent.assertEqual( output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , ) # let's ensure the other two hidden state exists self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(output.encoder_last_hidden_state is not None ) if output_hidden_states: self.check_output_hidden_state(_UpperCAmelCase , _UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] ) -> Any: '''simple docstring''' _lowerCAmelCase : Tuple = MaskFormerForInstanceSegmentation(config=_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.eval() def comm_check_on_output(_UpperCAmelCase : List[str] ): # let's still check that all the required stuff is there self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None ) self.parent.assertTrue(result.encoder_last_hidden_state is not None ) # okay, now we need to check the logits shape # due to the encoder compression, masks have a //4 spatial size self.parent.assertEqual( result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , ) # + 1 for null class self.parent.assertEqual( result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) ) with torch.no_grad(): _lowerCAmelCase : List[str] = model(pixel_values=_UpperCAmelCase , pixel_mask=_UpperCAmelCase ) _lowerCAmelCase : str = model(_UpperCAmelCase ) comm_check_on_output(_UpperCAmelCase ) _lowerCAmelCase : Any = model( pixel_values=_UpperCAmelCase , pixel_mask=_UpperCAmelCase , mask_labels=_UpperCAmelCase , class_labels=_UpperCAmelCase ) comm_check_on_output(_UpperCAmelCase ) self.parent.assertTrue(result.loss is not None ) self.parent.assertEqual(result.loss.shape , torch.Size([1] ) ) @require_torch class __snake_case (_a , _a , unittest.TestCase ): lowerCAmelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else () lowerCAmelCase__ = ( {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation} if is_torch_available() else {} ) lowerCAmelCase__ = False lowerCAmelCase__ = False lowerCAmelCase__ = False lowerCAmelCase__ = False def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]: '''simple docstring''' _lowerCAmelCase : Optional[Any] = MaskFormerModelTester(self ) _lowerCAmelCase : Optional[Any] = ConfigTester(self , config_class=_UpperCAmelCase , has_text_modality=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict: '''simple docstring''' self.config_tester.run_common_tests() def SCREAMING_SNAKE_CASE ( self : str ) -> int: '''simple docstring''' _lowerCAmelCase , _lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskformer_model(_UpperCAmelCase , **_UpperCAmelCase , output_hidden_states=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]: '''simple docstring''' _lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_UpperCAmelCase ) @unittest.skip(reason="""MaskFormer does not use inputs_embeds""" ) def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]: '''simple docstring''' pass @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" ) def SCREAMING_SNAKE_CASE ( self : int ) -> Any: '''simple docstring''' pass @unittest.skip(reason="""MaskFormer is not a generative model""" ) def SCREAMING_SNAKE_CASE ( self : str ) -> Dict: '''simple docstring''' pass @unittest.skip(reason="""MaskFormer does not use token embeddings""" ) def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> str: '''simple docstring''' pass @require_torch_multi_gpu @unittest.skip( reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" ) def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int: '''simple docstring''' pass @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" ) def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Any: '''simple docstring''' pass def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict: '''simple docstring''' _lowerCAmelCase , _lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCAmelCase : Any = model_class(_UpperCAmelCase ) _lowerCAmelCase : Tuple = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic _lowerCAmelCase : Union[str, Any] = [*signature.parameters.keys()] _lowerCAmelCase : str = ["""pixel_values"""] self.assertListEqual(arg_names[:1] , _UpperCAmelCase ) @slow def SCREAMING_SNAKE_CASE ( self : str ) -> str: '''simple docstring''' for model_name in ["facebook/maskformer-swin-small-coco"]: _lowerCAmelCase : str = MaskFormerModel.from_pretrained(_UpperCAmelCase ) self.assertIsNotNone(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple: '''simple docstring''' _lowerCAmelCase : str = (self.model_tester.min_size,) * 2 _lowerCAmelCase : List[Any] = { """pixel_values""": torch.randn((2, 3, *size) , device=_UpperCAmelCase ), """mask_labels""": torch.randn((2, 10, *size) , device=_UpperCAmelCase ), """class_labels""": torch.zeros(2 , 10 , device=_UpperCAmelCase ).long(), } _lowerCAmelCase : int = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_UpperCAmelCase ) _lowerCAmelCase : List[str] = model(**_UpperCAmelCase ) self.assertTrue(outputs.loss is not None ) def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]: '''simple docstring''' _lowerCAmelCase , _lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_maskformer_model(_UpperCAmelCase , **_UpperCAmelCase , output_hidden_states=_UpperCAmelCase ) def SCREAMING_SNAKE_CASE ( self : int ) -> str: '''simple docstring''' _lowerCAmelCase , _lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCAmelCase : Optional[Any] = model_class(_UpperCAmelCase ).to(_UpperCAmelCase ) _lowerCAmelCase : Dict = model(**_UpperCAmelCase , output_attentions=_UpperCAmelCase ) self.assertTrue(outputs.attentions is not None ) def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[Any]: '''simple docstring''' if not self.model_tester.is_training: return # only MaskFormerForInstanceSegmentation has the loss _lowerCAmelCase : Tuple = self.all_model_classes[1] _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs() _lowerCAmelCase : Optional[Any] = model_class(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.train() _lowerCAmelCase : Any = model(_UpperCAmelCase , mask_labels=_UpperCAmelCase , class_labels=_UpperCAmelCase ).loss loss.backward() def SCREAMING_SNAKE_CASE ( self : Any ) -> int: '''simple docstring''' _lowerCAmelCase : Dict = self.all_model_classes[1] _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs() _lowerCAmelCase : Union[str, Any] = True _lowerCAmelCase : str = True _lowerCAmelCase : List[str] = model_class(_UpperCAmelCase ) model.to(_UpperCAmelCase ) model.train() _lowerCAmelCase : Optional[Any] = model(_UpperCAmelCase , mask_labels=_UpperCAmelCase , class_labels=_UpperCAmelCase ) _lowerCAmelCase : Optional[int] = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() _lowerCAmelCase : Optional[int] = outputs.pixel_decoder_hidden_states[0] pixel_decoder_hidden_states.retain_grad() # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't _lowerCAmelCase : Optional[int] = outputs.transformer_decoder_hidden_states[0] transformer_decoder_hidden_states.retain_grad() _lowerCAmelCase : Optional[Any] = outputs.attentions[0] attentions.retain_grad() outputs.loss.backward(retain_graph=_UpperCAmelCase ) self.assertIsNotNone(encoder_hidden_states.grad ) self.assertIsNotNone(pixel_decoder_hidden_states.grad ) self.assertIsNotNone(transformer_decoder_hidden_states.grad ) self.assertIsNotNone(attentions.grad ) _lowerCamelCase : Union[str, Any] = 1e-4 def _UpperCAmelCase (): '''simple docstring''' _lowerCAmelCase : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_vision @slow class __snake_case (unittest.TestCase ): @cached_property def SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]: '''simple docstring''' return ( MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" ) if is_vision_available() else None ) def SCREAMING_SNAKE_CASE ( self : str ) -> Tuple: '''simple docstring''' _lowerCAmelCase : Tuple = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(_UpperCAmelCase ) _lowerCAmelCase : Union[str, Any] = self.default_image_processor _lowerCAmelCase : Optional[int] = prepare_img() _lowerCAmelCase : Optional[int] = image_processor(_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase ) _lowerCAmelCase : int = inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(_UpperCAmelCase , (1, 3, 800, 1088) ) with torch.no_grad(): _lowerCAmelCase : int = model(**_UpperCAmelCase ) _lowerCAmelCase : int = torch.tensor( [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]] ).to(_UpperCAmelCase ) self.assertTrue( torch.allclose( outputs.encoder_last_hidden_state[0, 0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) _lowerCAmelCase : Union[str, Any] = torch.tensor( [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]] ).to(_UpperCAmelCase ) self.assertTrue( torch.allclose( outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) _lowerCAmelCase : str = torch.tensor( [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]] ).to(_UpperCAmelCase ) self.assertTrue( torch.allclose( outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) def SCREAMING_SNAKE_CASE ( self : List[str] ) -> List[Any]: '''simple docstring''' _lowerCAmelCase : int = ( MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" ) .to(_UpperCAmelCase ) .eval() ) _lowerCAmelCase : Union[str, Any] = self.default_image_processor _lowerCAmelCase : int = prepare_img() _lowerCAmelCase : Any = image_processor(_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase ) _lowerCAmelCase : int = inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(_UpperCAmelCase , (1, 3, 800, 1088) ) with torch.no_grad(): _lowerCAmelCase : Any = model(**_UpperCAmelCase ) # masks_queries_logits _lowerCAmelCase : List[Any] = outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , ) _lowerCAmelCase : Dict = [ [-1.3737124, -1.7724937, -1.9364233], [-1.5977281, -1.9867939, -2.1523695], [-1.5795398, -1.9269832, -2.093942], ] _lowerCAmelCase : Dict = torch.tensor(_UpperCAmelCase ).to(_UpperCAmelCase ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) # class_queries_logits _lowerCAmelCase : Optional[Any] = outputs.class_queries_logits self.assertEqual( class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) ) _lowerCAmelCase : str = torch.tensor( [ [1.6_512E00, -5.2_572E00, -3.3_519E00], [3.6_169E-02, -5.9_025E00, -2.9_313E00], [1.0_766E-04, -7.7_630E00, -5.1_263E00], ] ).to(_UpperCAmelCase ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[Any]: '''simple docstring''' _lowerCAmelCase : Tuple = ( MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" ) .to(_UpperCAmelCase ) .eval() ) _lowerCAmelCase : Dict = self.default_image_processor _lowerCAmelCase : Any = prepare_img() _lowerCAmelCase : List[Any] = image_processor(_UpperCAmelCase , return_tensors="""pt""" ).to(_UpperCAmelCase ) _lowerCAmelCase : List[str] = inputs["""pixel_values"""].shape # check size is divisible by 32 self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 ) # check size self.assertEqual(_UpperCAmelCase , (1, 3, 800, 1088) ) with torch.no_grad(): _lowerCAmelCase : Optional[int] = model(**_UpperCAmelCase ) # masks_queries_logits _lowerCAmelCase : Union[str, Any] = outputs.masks_queries_logits self.assertEqual( masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , ) _lowerCAmelCase : int = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]] _lowerCAmelCase : str = torch.tensor(_UpperCAmelCase ).to(_UpperCAmelCase ) self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) # class_queries_logits _lowerCAmelCase : Optional[int] = outputs.class_queries_logits self.assertEqual( class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) ) _lowerCAmelCase : List[str] = torch.tensor( [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]] ).to(_UpperCAmelCase ) self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=_UpperCAmelCase ) ) def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]: '''simple docstring''' _lowerCAmelCase : Optional[int] = ( MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" ) .to(_UpperCAmelCase ) .eval() ) _lowerCAmelCase : int = self.default_image_processor _lowerCAmelCase : Union[str, Any] = image_processor( [np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , ) _lowerCAmelCase : str = inputs["""pixel_values"""].to(_UpperCAmelCase ) _lowerCAmelCase : Optional[Any] = [el.to(_UpperCAmelCase ) for el in inputs["""mask_labels"""]] _lowerCAmelCase : int = [el.to(_UpperCAmelCase ) for el in inputs["""class_labels"""]] with torch.no_grad(): _lowerCAmelCase : int = model(**_UpperCAmelCase ) self.assertTrue(outputs.loss is not None )

def _UpperCAmelCase (UpperCamelCase_ : str , UpperCamelCase_ : str ): '''simple docstring''' _lowerCAmelCase : str = len(UpperCamelCase_ ) + 1 _lowerCAmelCase : List[Any] = len(UpperCamelCase_ ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. _lowerCAmelCase : List[Any] = [[0 for i in range(UpperCamelCase_ )] for j in range(UpperCamelCase_ )] # since string of zero length match pattern of zero length _lowerCAmelCase : Optional[int] = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , UpperCamelCase_ ): _lowerCAmelCase : Optional[Any] = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , UpperCamelCase_ ): _lowerCAmelCase : Tuple = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , UpperCamelCase_ ): for j in range(1 , UpperCamelCase_ ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": _lowerCAmelCase : Dict = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: _lowerCAmelCase : List[str] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): _lowerCAmelCase : int = dp[i - 1][j] else: _lowerCAmelCase : List[str] = 0 else: _lowerCAmelCase : List[Any] = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") _lowerCamelCase : Any = "aab" _lowerCamelCase : List[str] = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(F'''{input_string} matches the given pattern {pattern}''') else: print(F'''{input_string} does not match with the given pattern {pattern}''')

"""simple docstring""" import tempfile import unittest from pathlib import Path from shutil import copyfile from transformers import MaMaaaTokenizer, is_torch_available from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_sentencepiece, require_tokenizers, require_torch, slow, ) from transformers.utils import is_sentencepiece_available if is_sentencepiece_available(): from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json from ...test_tokenization_common import TokenizerTesterMixin if is_sentencepiece_available(): __lowercase = get_tests_dir("""fixtures/test_sentencepiece.model""") if is_torch_available(): from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right __lowercase = 128022 __lowercase = 128028 @require_sentencepiece class _A ( _a ,unittest.TestCase ): """simple docstring""" UpperCAmelCase : List[Any] = MaMaaaTokenizer UpperCAmelCase : List[str] = False UpperCAmelCase : Tuple = False UpperCAmelCase : str = True def __snake_case ( self : int): super().setUp() a : Any = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"] a : Any = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase)))) a : List[str] = Path(self.tmpdirname) save_json(__UpperCAmelCase , save_dir / VOCAB_FILES_NAMES["vocab_file"]) if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists(): copyfile(__UpperCAmelCase , save_dir / VOCAB_FILES_NAMES["spm_file"]) a : Tuple = MaMaaaTokenizer.from_pretrained(self.tmpdirname) tokenizer.save_pretrained(self.tmpdirname) def __snake_case ( self : str , **__UpperCAmelCase : str): return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase) def __snake_case ( self : int , __UpperCAmelCase : List[Any]): return ( "This is a test", "This is a test", ) def __snake_case ( self : Tuple): a : int = "</s>" a : Dict = 0 self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase) , __UpperCAmelCase) self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase) , __UpperCAmelCase) def __snake_case ( self : List[str]): a : Tuple = self.get_tokenizer() a : Optional[Any] = list(tokenizer.get_vocab().keys()) self.assertEqual(vocab_keys[0] , "</s>") self.assertEqual(vocab_keys[1] , "<unk>") self.assertEqual(vocab_keys[-1] , "<s>") self.assertEqual(len(__UpperCAmelCase) , tokenizer.vocab_size + len(tokenizer.get_added_vocab())) @unittest.skip("Skip this test while all models are still to be uploaded.") def __snake_case ( self : str): pass def __snake_case ( self : Optional[int]): a : Tuple = self.get_tokenizer() a : Dict = tokenizer.tokenize("This is a test") self.assertListEqual(__UpperCAmelCase , ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(__UpperCAmelCase) , [2, 3, 4, 5, 6] , ) a : str = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]) self.assertListEqual(__UpperCAmelCase , ["▁This", "▁is", "▁a", "▁t", "est"]) a : Optional[Any] = tokenizer.convert_tokens_to_string(__UpperCAmelCase) self.assertEqual(__UpperCAmelCase , "This is a test") @slow def __snake_case ( self : Tuple): # fmt: off a : Optional[int] = {"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=__UpperCAmelCase , model_name="facebook/m2m100_418M" , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" , ) @require_torch @require_sentencepiece @require_tokenizers class _A ( unittest.TestCase ): """simple docstring""" UpperCAmelCase : Union[str, Any] = """facebook/m2m100_418M""" UpperCAmelCase : Union[str, Any] = [ """In my opinion, there are two levels of response from the French government.""", """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""", ] UpperCAmelCase : Dict = [ """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""", """L'affaire NSA souligne l'absence totale de débat sur le renseignement""", ] # fmt: off UpperCAmelCase : List[str] = [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2] @classmethod def __snake_case ( cls : List[str]): a : MaMaaaTokenizer = MaMaaaTokenizer.from_pretrained( cls.checkpoint_name , src_lang="en" , tgt_lang="fr") a : List[str] = 1 return cls def __snake_case ( self : Union[str, Any]): self.assertEqual(self.tokenizer.get_lang_id("ar") , 128006) self.assertEqual(self.tokenizer.get_lang_id("en") , 128022) self.assertEqual(self.tokenizer.get_lang_id("ro") , 128076) self.assertEqual(self.tokenizer.get_lang_id("mr") , 128063) def __snake_case ( self : Tuple): a : Union[str, Any] = self.tokenizer.get_vocab() self.assertEqual(len(__UpperCAmelCase) , self.tokenizer.vocab_size) self.assertEqual(vocab["<unk>"] , 3) self.assertIn(self.tokenizer.get_lang_token("en") , __UpperCAmelCase) def __snake_case ( self : List[Any]): a : int = "en" a : int = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0] self.assertListEqual(self.expected_src_tokens , __UpperCAmelCase) def __snake_case ( self : Any): self.assertIn(__UpperCAmelCase , self.tokenizer.all_special_ids) # fmt: off a : List[Any] = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2] # fmt: on a : str = self.tokenizer.decode(__UpperCAmelCase , skip_special_tokens=__UpperCAmelCase) a : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__UpperCAmelCase) self.assertEqual(__UpperCAmelCase , __UpperCAmelCase) self.assertNotIn(self.tokenizer.eos_token , __UpperCAmelCase) def __snake_case ( self : List[str]): a : Optional[int] = tempfile.mkdtemp() a : Union[str, Any] = self.tokenizer.lang_token_to_id self.tokenizer.save_pretrained(__UpperCAmelCase) a : int = MaMaaaTokenizer.from_pretrained(__UpperCAmelCase) self.assertDictEqual(new_tok.lang_token_to_id , __UpperCAmelCase) @require_torch def __snake_case ( self : Optional[int]): a : Dict = "en" a : List[str] = "fr" a : str = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__UpperCAmelCase , return_tensors="pt") a : Any = shift_tokens_right( batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id) for k in batch: a : str = batch[k].tolist() # batch = {k: v.tolist() for k,v in batch.items()} # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4 # batch.decoder_inputs_ids[0][0] == assert batch.input_ids[1][0] == EN_CODE assert batch.input_ids[1][-1] == 2 assert batch.labels[1][0] == FR_CODE assert batch.labels[1][-1] == 2 assert batch.decoder_input_ids[1][:2] == [2, FR_CODE] @require_torch def __snake_case ( self : Union[str, Any]): a : Dict = "mr" self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr")]) self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id]) a : List[Any] = "zh" self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh")]) self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id]) @require_torch def __snake_case ( self : Optional[int]): a : Any = "mr" self.tokenizer._switch_to_target_mode() self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr")]) self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id]) self.tokenizer._switch_to_input_mode() self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang)]) a : List[Any] = "zh" self.tokenizer._switch_to_target_mode() self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh")]) self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id]) self.tokenizer._switch_to_input_mode() self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang)]) @require_torch def __snake_case ( self : Any): a : str = self.tokenizer._build_translation_inputs("A test" , return_tensors="pt" , src_lang="en" , tgt_lang="ar") self.assertEqual( nested_simplify(__UpperCAmelCase) , { # en_XX, A, test, EOS "input_ids": [[128022, 58, 4183, 2]], "attention_mask": [[1, 1, 1, 1]], # ar_AR "forced_bos_token_id": 128006, } , )

"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class UpperCamelCase__: def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase=13 ,__UpperCAmelCase=7 ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=True ,__UpperCAmelCase=99 ,__UpperCAmelCase=32 ,__UpperCAmelCase=2 ,__UpperCAmelCase=4 ,__UpperCAmelCase=37 ,__UpperCAmelCase="gelu" ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=0.1 ,__UpperCAmelCase=5_12 ,__UpperCAmelCase=16 ,__UpperCAmelCase=2 ,__UpperCAmelCase=0.0_2 ,__UpperCAmelCase=3 ,__UpperCAmelCase=4 ,__UpperCAmelCase=None ,__UpperCAmelCase=0 ,) -> Dict: A__ = parent A__ = batch_size A__ = seq_length A__ = is_training A__ = use_input_mask A__ = use_token_type_ids A__ = use_labels A__ = vocab_size A__ = hidden_size A__ = num_hidden_layers A__ = num_attention_heads A__ = intermediate_size A__ = hidden_act A__ = hidden_dropout_prob A__ = attention_probs_dropout_prob A__ = max_position_embeddings A__ = type_vocab_size A__ = type_sequence_label_size A__ = initializer_range A__ = num_labels A__ = num_choices A__ = scope A__ = projection_dim def snake_case__ ( self ) -> Optional[Any]: A__ = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size ) A__ = None if self.use_input_mask: # follow test_modeling_tf_ctrl.py A__ = random_attention_mask([self.batch_size, self.seq_length] ) A__ = None if self.use_token_type_ids: A__ = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size ) A__ = None A__ = None A__ = None if self.use_labels: A__ = ids_tensor([self.batch_size] ,self.type_sequence_label_size ) A__ = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels ) A__ = ids_tensor([self.batch_size] ,self.num_choices ) A__ = BertConfig( vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=__UpperCAmelCase ,initializer_range=self.initializer_range ,) A__ = DPRConfig(projection_dim=self.projection_dim ,**config.to_dict() ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Tuple: A__ = TFDPRContextEncoder(config=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,attention_mask=__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ) self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.projection_dim or self.hidden_size) ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Union[str, Any]: A__ = TFDPRQuestionEncoder(config=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,attention_mask=__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,token_type_ids=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ) self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.projection_dim or self.hidden_size) ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> Optional[int]: A__ = TFDPRReader(config=__UpperCAmelCase ) A__ = model(__UpperCAmelCase ,attention_mask=__UpperCAmelCase ) self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) ) self.parent.assertEqual(result.relevance_logits.shape ,(self.batch_size,) ) def snake_case__ ( self ) -> int: A__ = self.prepare_config_and_inputs() ( ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ( A__ ) , ) = config_and_inputs A__ = {'input_ids': input_ids} return config, inputs_dict @require_tf class UpperCamelCase__( __A , __A , unittest.TestCase ): lowerCAmelCase__ : Optional[int] = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) lowerCAmelCase__ : List[str] = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {} lowerCAmelCase__ : Tuple = False lowerCAmelCase__ : Optional[int] = False lowerCAmelCase__ : List[str] = False lowerCAmelCase__ : int = False lowerCAmelCase__ : str = False def snake_case__ ( self ) -> str: A__ = TFDPRModelTester(self ) A__ = ConfigTester(self ,config_class=__UpperCAmelCase ,hidden_size=37 ) def snake_case__ ( self ) -> Optional[Any]: self.config_tester.run_common_tests() def snake_case__ ( self ) -> int: A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*__UpperCAmelCase ) def snake_case__ ( self ) -> Optional[Any]: A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*__UpperCAmelCase ) def snake_case__ ( self ) -> List[str]: A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*__UpperCAmelCase ) @slow def snake_case__ ( self ) -> int: for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRContextEncoder.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRContextEncoder.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRQuestionEncoder.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: A__ = TFDPRReader.from_pretrained(__UpperCAmelCase ) self.assertIsNotNone(__UpperCAmelCase ) @require_tf class UpperCamelCase__( unittest.TestCase ): @slow def snake_case__ ( self ) -> Optional[Any]: A__ = TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base' ) A__ = tf.constant( [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP] A__ = model(__UpperCAmelCase )[0] # embedding shape = (1, 768) # compare the actual values for a slice. A__ = tf.constant( [ [ 0.0_3_2_3_6_2_5_3, 0.1_2_7_5_3_3_3_5, 0.1_6_8_1_8_5_0_9, 0.0_0_2_7_9_7_8_6, 0.3_8_9_6_9_3_3, 0.2_4_2_6_4_9_4_5, 0.2_1_7_8_9_7_1, -0.0_2_3_3_5_2_2_7, -0.0_8_4_8_1_9_5_9, -0.1_4_3_2_4_1_1_7, ] ] ) self.assertTrue(numpy.allclose(output[:, :10].numpy() ,expected_slice.numpy() ,atol=1e-4 ) )

'''simple docstring''' import warnings from typing import Any, Dict, List, Optional, Union import numpy as np from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, logging lowercase__ : List[str] = logging.get_logger(__name__) class SCREAMING_SNAKE_CASE (a__ ): lowerCAmelCase = ['''input_values''', '''attention_mask'''] def __init__( self , _UpperCAmelCase = 1 , _UpperCAmelCase = 1_6000 , _UpperCAmelCase = 0.0 , _UpperCAmelCase = False , _UpperCAmelCase = 80 , _UpperCAmelCase = 16 , _UpperCAmelCase = 64 , _UpperCAmelCase = "hann_window" , _UpperCAmelCase = 1.0 , _UpperCAmelCase = 80 , _UpperCAmelCase = 7600 , _UpperCAmelCase = 1e-1_0 , _UpperCAmelCase = 2 , _UpperCAmelCase = True , **_UpperCAmelCase , ): '''simple docstring''' super().__init__(feature_size=_UpperCAmelCase , sampling_rate=_UpperCAmelCase , padding_value=_UpperCAmelCase , **_UpperCAmelCase) __A : Union[str, Any] = do_normalize __A : Dict = return_attention_mask __A : Any = num_mel_bins __A : Union[str, Any] = hop_length __A : List[str] = win_length __A : int = win_function __A : Any = frame_signal_scale __A : List[str] = fmin __A : Optional[Any] = fmax __A : Dict = mel_floor __A : Optional[Any] = reduction_factor __A : Optional[Any] = win_length * sampling_rate // 1000 __A : str = hop_length * sampling_rate // 1000 __A : Tuple = optimal_fft_length(self.sample_size) __A : str = (self.n_fft // 2) + 1 __A : Dict = window_function(window_length=self.sample_size , name=self.win_function , periodic=_UpperCAmelCase) __A : List[str] = mel_filter_bank( num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm='slaney' , mel_scale='slaney' , ) if frame_signal_scale != 1.0: warnings.warn( 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , ) if reduction_factor != 2.0: warnings.warn( 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers' , _UpperCAmelCase , ) @staticmethod # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0.0): '''simple docstring''' if attention_mask is not None: __A : List[Any] = np.array(_UpperCAmelCase , np.intaa) __A : int = [] for vector, length in zip(_UpperCAmelCase , attention_mask.sum(-1)): __A : Dict = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7) if length < normed_slice.shape[0]: __A : Dict = padding_value normed_input_values.append(_UpperCAmelCase) else: __A : List[Any] = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values] return normed_input_values def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , ): '''simple docstring''' __A : Tuple = spectrogram( _UpperCAmelCase , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel='log10' , ) return log_mel_spec.T def __call__( self , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , **_UpperCAmelCase , ): '''simple docstring''' if audio is None and audio_target is None: raise ValueError('You must provide either `audio` or `audio_target` values.') if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of' F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with' F' {self.sampling_rate} and not {sampling_rate}.') else: logger.warning( 'It is strongly recommended to pass the ``sampling_rate`` argument to this function. ' 'Failing to do so can result in silent errors that might be hard to debug.') if audio is not None: __A : Dict = self._process_audio( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) else: __A : Union[str, Any] = None if audio_target is not None: __A : Optional[int] = self._process_audio( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase , ) if inputs is None: return inputs_target else: __A : Any = inputs_target['input_values'] __A : Union[str, Any] = inputs_target.get('attention_mask') if decoder_attention_mask is not None: __A : List[str] = decoder_attention_mask return inputs def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase = False , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , **_UpperCAmelCase , ): '''simple docstring''' __A : Tuple = isinstance(_UpperCAmelCase , np.ndarray) and len(speech.shape) > 1 if is_batched_numpy and len(speech.shape) > 2: raise ValueError(F'Only mono-channel audio is supported for input to {self}') __A : Optional[int] = is_batched_numpy or ( isinstance(_UpperCAmelCase , (list, tuple)) and (isinstance(speech[0] , (np.ndarray, tuple, list))) ) if is_batched: __A : Dict = [np.asarray(_UpperCAmelCase , dtype=np.floataa) for speech in speech] elif not is_batched and not isinstance(_UpperCAmelCase , np.ndarray): __A : List[str] = np.asarray(_UpperCAmelCase , dtype=np.floataa) elif isinstance(_UpperCAmelCase , np.ndarray) and speech.dtype is np.dtype(np.floataa): __A : List[Any] = speech.astype(np.floataa) # always return batch if not is_batched: __A : Tuple = [speech] # needed to make pad() work on spectrogram inputs __A : Tuple = self.feature_size # convert into correct format for padding if is_target: __A : Dict = [self._extract_mel_features(_UpperCAmelCase) for waveform in speech] __A : Dict = BatchFeature({'input_values': features}) __A : Any = self.num_mel_bins else: __A : int = BatchFeature({'input_values': speech}) __A : Union[str, Any] = self.pad( _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , **_UpperCAmelCase , ) __A : Any = feature_size_hack # convert input values to correct format __A : Union[str, Any] = padded_inputs['input_values'] if not isinstance(input_values[0] , np.ndarray): __A : List[str] = [np.asarray(_UpperCAmelCase , dtype=np.floataa) for array in input_values] elif ( not isinstance(_UpperCAmelCase , np.ndarray) and isinstance(input_values[0] , np.ndarray) and input_values[0].dtype is np.dtype(np.floataa) ): __A : Any = [array.astype(np.floataa) for array in input_values] elif isinstance(_UpperCAmelCase , np.ndarray) and input_values.dtype is np.dtype(np.floataa): __A : List[Any] = input_values.astype(np.floataa) # convert attention_mask to correct format __A : int = padded_inputs.get('attention_mask') if attention_mask is not None: __A : Optional[int] = [np.asarray(_UpperCAmelCase , dtype=np.intaa) for array in attention_mask] # zero-mean and unit-variance normalization if not is_target and self.do_normalize: __A : Optional[int] = ( attention_mask if self._get_padding_strategies(_UpperCAmelCase , max_length=_UpperCAmelCase) is not PaddingStrategy.DO_NOT_PAD else None ) __A : str = self.zero_mean_unit_var_norm( padded_inputs['input_values'] , attention_mask=_UpperCAmelCase , padding_value=self.padding_value) if return_tensors is not None: __A : Dict = padded_inputs.convert_to_tensors(_UpperCAmelCase) return padded_inputs def SCREAMING_SNAKE_CASE ( self): '''simple docstring''' __A : Optional[int] = super().to_dict() # Don't serialize these as they are derived from the other properties. __A : str = ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs'] for name in names: if name in output: del output[name] return output

'''simple docstring''' import itertools import math def _lowerCAmelCase ( __snake_case : int ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(math.sqrt(__snake_case ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def _lowerCAmelCase ( ) -> List[Any]: __A : Optional[Any] = 2 while True: if is_prime(__snake_case ): yield num num += 1 def _lowerCAmelCase ( __snake_case : int = 1_00_01 ) -> int: return next(itertools.islice(prime_generator() , nth - 1 , __snake_case ) ) if __name__ == "__main__": print(f"""{solution() = }""")

"""simple docstring""" import argparse import shlex import runhouse as rh if __name__ == "__main__": # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access # setup instructions, if using on-demand hardware # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster # Throw an error if user passes both BYO and on-demand cluster args # Otherwise, use default values a__ : List[Any] = argparse.ArgumentParser() parser.add_argument('''--user''', type=str, default='''ubuntu''') parser.add_argument('''--host''', type=str, default='''localhost''') parser.add_argument('''--key_path''', type=str, default=None) parser.add_argument('''--instance''', type=str, default='''V100:1''') parser.add_argument('''--provider''', type=str, default='''cheapest''') parser.add_argument('''--use_spot''', type=bool, default=False) parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''') a__ , a__ : Optional[Any] = parser.parse_known_args() if args.host != "localhost": if args.instance != "V100:1" or args.provider != "cheapest": raise ValueError('''Cannot specify both BYO and on-demand cluster args''') a__ : List[str] = rh.cluster( name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path} ) else: a__ : Union[str, Any] = rh.cluster( name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot ) a__ : Any = args.example.rsplit('''/''', 1)[0] # Set up remote environment cluster.install_packages(['''pip:./''']) # Installs transformers from local source # Note transformers is copied into the home directory on the remote machine, so we can install from there cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"]) cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''']) # Run example. You can bypass the CLI wrapper and paste your own code here. cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"]) # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI): # from my_script... import train # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard'] # launch_train_gpu = rh.function(fn=train, # system=gpu, # reqs=reqs, # name='train_bert_glue') # # We can pass in arguments just like we would to a function: # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16 # stream_logs=True)

"""simple docstring""" import math import random def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ = False ): '''simple docstring''' if deriv: return value * (1 - value) return 1 / (1 + math.exp(-value )) # Initial Value a__ : Tuple = 0.02 def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ): '''simple docstring''' __SCREAMING_SNAKE_CASE = float(2 * (random.randint(1 , 100 )) - 1 ) for _ in range(lowerCAmelCase_ ): # Forward propagation __SCREAMING_SNAKE_CASE = sigmoid_function(INITIAL_VALUE * weight ) # How much did we miss? __SCREAMING_SNAKE_CASE = (expected / 100) - layer_a # Error delta __SCREAMING_SNAKE_CASE = layer_1_error * sigmoid_function(lowerCAmelCase_ , lowerCAmelCase_ ) # Update weight weight += INITIAL_VALUE * layer_1_delta return layer_a * 100 if __name__ == "__main__": import doctest doctest.testmod() a__ : List[str] = int(input('''Expected value: ''')) a__ : str = int(input('''Number of propagations: ''')) print(forward_propagation(expected, number_propagations))

'''simple docstring''' from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available lowerCAmelCase : Union[str, Any] = { 'configuration_autoformer': [ 'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AutoformerConfig', ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCAmelCase : List[Any] = [ 'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST', 'AutoformerForPrediction', 'AutoformerModel', 'AutoformerPreTrainedModel', ] if TYPE_CHECKING: from .configuration_autoformer import ( AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoformerConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_autoformer import ( AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, AutoformerForPrediction, AutoformerModel, AutoformerPreTrainedModel, ) else: import sys lowerCAmelCase : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring''' # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import torch from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer from .base import PipelineTool class SCREAMING_SNAKE_CASE__ ( snake_case_): lowerCAmelCase_ = """facebook/bart-large-mnli""" lowerCAmelCase_ = ( """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """ """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """ """It returns the most likely label in the list of provided `labels` for the input text.""" ) lowerCAmelCase_ = """text_classifier""" lowerCAmelCase_ = AutoTokenizer lowerCAmelCase_ = AutoModelForSequenceClassification lowerCAmelCase_ = ["""text""", ["""text"""]] lowerCAmelCase_ = ["""text"""] def UpperCAmelCase_ ( self )-> str: '''simple docstring''' super().setup() UpperCamelCase = self.model.config UpperCamelCase = -1 for idx, label in config.idalabel.items(): if label.lower().startswith('entail' ): UpperCamelCase = int(A_ ) if self.entailment_id == -1: raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.' ) def UpperCAmelCase_ ( self , A_ , A_ )-> Any: '''simple docstring''' UpperCamelCase = labels return self.pre_processor( [text] * len(A_ ) , [F'''This example is {label}''' for label in labels] , return_tensors='pt' , padding='max_length' , ) def UpperCAmelCase_ ( self , A_ )-> Union[str, Any]: '''simple docstring''' UpperCamelCase = outputs.logits UpperCamelCase = torch.argmax(logits[:, 2] ).item() return self._labels[label_id]

from __future__ import annotations import collections import pprint from pathlib import Path def UpperCamelCase ( __lowerCamelCase : str ): return "".join(sorted(__lowerCamelCase ) ) def UpperCamelCase ( __lowerCamelCase : str ): return word_by_signature[signature(__lowerCamelCase )] __lowerCamelCase = Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""") __lowerCamelCase = sorted({word.strip().lower() for word in data.splitlines()}) __lowerCamelCase = collections.defaultdict(list) for word in word_list: word_by_signature[signature(word)].append(word) if __name__ == "__main__": __lowerCamelCase = {word: anagram(word) for word in word_list if len(anagram(word)) > 1} with open("""anagrams.txt""", """w""") as file: file.write("""all_anagrams = \n """) file.write(pprint.pformat(all_anagrams))

import argparse import re from typing import Dict import torch from datasets import Audio, Dataset, load_dataset, load_metric from transformers import AutoFeatureExtractor, pipeline def UpperCamelCase ( __lowerCamelCase : Dataset , __lowerCamelCase : Dict[str, str] ): snake_case : int = args.log_outputs snake_case : Dict = "_".join(args.dataset.split("/" ) + [args.config, args.split] ) # load metric snake_case : List[str] = load_metric("wer" ) snake_case : Tuple = load_metric("cer" ) # compute metrics snake_case : List[Any] = wer.compute(references=result["target"] , predictions=result["prediction"] ) snake_case : int = cer.compute(references=result["target"] , predictions=result["prediction"] ) # print & log results snake_case : int = f"""WER: {wer_result}\nCER: {cer_result}""" print(__lowerCamelCase ) with open(f"""{dataset_id}_eval_results.txt""" , "w" ) as f: f.write(__lowerCamelCase ) # log all results in text file. Possibly interesting for analysis if log_outputs is not None: snake_case : int = f"""log_{dataset_id}_predictions.txt""" snake_case : List[Any] = f"""log_{dataset_id}_targets.txt""" with open(__lowerCamelCase , "w" ) as p, open(__lowerCamelCase , "w" ) as t: # mapping function to write output def write_to_file(__lowerCamelCase : str , __lowerCamelCase : Optional[int] ): p.write(f"""{i}""" + "\n" ) p.write(batch["prediction"] + "\n" ) t.write(f"""{i}""" + "\n" ) t.write(batch["target"] + "\n" ) result.map(__lowerCamelCase , with_indices=__lowerCamelCase ) def UpperCamelCase ( __lowerCamelCase : str ): snake_case : List[Any] = "[,?.!\-\;\:\"“%‘”�—’…–]" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training snake_case : List[Any] = re.sub(__lowerCamelCase , "" , text.lower() ) # In addition, we can normalize the target text, e.g. removing new lines characters etc... # note that order is important here! snake_case : Optional[Any] = ["\n\n", "\n", " ", " "] for t in token_sequences_to_ignore: snake_case : Dict = " ".join(text.split(__lowerCamelCase ) ) return text def UpperCamelCase ( __lowerCamelCase : int ): # load dataset snake_case : str = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__lowerCamelCase ) # for testing: only process the first two examples as a test # dataset = dataset.select(range(10)) # load processor snake_case : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id ) snake_case : Union[str, Any] = feature_extractor.sampling_rate # resample audio snake_case : Union[str, Any] = dataset.cast_column("audio" , Audio(sampling_rate=__lowerCamelCase ) ) # load eval pipeline if args.device is None: snake_case : List[str] = 0 if torch.cuda.is_available() else -1 snake_case : str = pipeline("automatic-speech-recognition" , model=args.model_id , device=args.device ) # map function to decode audio def map_to_pred(__lowerCamelCase : int ): snake_case : Dict = asr( batch["audio"]["array"] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s ) snake_case : str = prediction["text"] snake_case : Tuple = normalize_text(batch["sentence"] ) return batch # run inference on all examples snake_case : Dict = dataset.map(__lowerCamelCase , remove_columns=dataset.column_names ) # compute and log_results # do not change function below log_results(__lowerCamelCase , __lowerCamelCase ) if __name__ == "__main__": __lowerCamelCase = argparse.ArgumentParser() parser.add_argument( """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers""" ) parser.add_argument( """--dataset""", type=str, required=True, help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""", ) parser.add_argument( """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'` for Common Voice""" ) parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""") parser.add_argument( """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds.""" ) parser.add_argument( """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second.""" ) parser.add_argument( """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis.""" ) parser.add_argument( """--device""", type=int, default=None, help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""", ) __lowerCamelCase = parser.parse_args() main(args)

from typing import Optional from urllib.parse import quote import huggingface_hub as hfh from packaging import version def A ( a_ ,a_ ,a_ = None ) -> Optional[Any]: if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).release: # old versions of hfh don't url-encode the file path __UpperCamelCase : str =quote(__lowerCamelCase ) return hfh.hf_hub_url(__lowerCamelCase ,__lowerCamelCase ,repo_type='dataset' ,revision=__lowerCamelCase )

from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available, ) A_ :Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: A_ :Optional[Any] = ['''PLBartTokenizer'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: A_ :Optional[int] = [ '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''', '''PLBartForCausalLM''', '''PLBartForConditionalGeneration''', '''PLBartForSequenceClassification''', '''PLBartModel''', '''PLBartPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_plbart import PLBartTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_plbart import ( PLBART_PRETRAINED_MODEL_ARCHIVE_LIST, PLBartForCausalLM, PLBartForConditionalGeneration, PLBartForSequenceClassification, PLBartModel, PLBartPreTrainedModel, ) else: import sys A_ :Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure)

"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, is_vision_available, ) a : Optional[Any] = { '''configuration_layoutlmv3''': [ '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv3Config''', '''LayoutLMv3OnnxConfig''', ], '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''], '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a : Optional[Any] = ['''LayoutLMv3TokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a : int = [ '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''', '''LayoutLMv3ForQuestionAnswering''', '''LayoutLMv3ForSequenceClassification''', '''LayoutLMv3ForTokenClassification''', '''LayoutLMv3Model''', '''LayoutLMv3PreTrainedModel''', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a : List[Any] = [ '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFLayoutLMv3ForQuestionAnswering''', '''TFLayoutLMv3ForSequenceClassification''', '''TFLayoutLMv3ForTokenClassification''', '''TFLayoutLMv3Model''', '''TFLayoutLMv3PreTrainedModel''', ] try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a : Union[str, Any] = ['''LayoutLMv3FeatureExtractor'''] a : int = ['''LayoutLMv3ImageProcessor'''] if TYPE_CHECKING: from .configuration_layoutlmva import ( LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig, LayoutLMvaOnnxConfig, ) from .processing_layoutlmva import LayoutLMvaProcessor from .tokenization_layoutlmva import LayoutLMvaTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_layoutlmva import ( LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMvaForQuestionAnswering, LayoutLMvaForSequenceClassification, LayoutLMvaForTokenClassification, LayoutLMvaModel, LayoutLMvaPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_layoutlmva import ( TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMvaForQuestionAnswering, TFLayoutLMvaForSequenceClassification, TFLayoutLMvaForTokenClassification, TFLayoutLMvaModel, TFLayoutLMvaPreTrainedModel, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor from .image_processing_layoutlmva import LayoutLMvaImageProcessor else: import sys a : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

"""simple docstring""" from decimal import Decimal, getcontext from math import ceil, factorial def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->str: '''simple docstring''' if not isinstance(_lowercase , _lowercase ): raise TypeError("Undefined for non-integers" ) elif precision < 1: raise ValueError("Undefined for non-natural numbers" ) a : Tuple = precision a : str = ceil(precision / 14 ) a : List[Any] = 42_6880 * Decimal(1_0005 ).sqrt() a : Union[str, Any] = 1 a : Dict = 1359_1409 a : Optional[int] = Decimal(_lowercase ) for k in range(1 , _lowercase ): a : int = factorial(6 * k ) // (factorial(3 * k ) * factorial(_lowercase ) ** 3) linear_term += 5_4514_0134 exponential_term *= -26_2537_4126_4076_8000 partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term return str(constant_term / partial_sum )[:-1] if __name__ == "__main__": a : Optional[Any] = 50 print(F'''The first {n} digits of pi is: {pi(n)}''')

import argparse import json import math import os import time import traceback import zipfile from collections import Counter import requests def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ): __lowerCamelCase : Dict = None if token is not None: __lowerCamelCase : Tuple = {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'} __lowerCamelCase : int = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100' __lowerCamelCase : str = requests.get(lowerCAmelCase__ , headers=lowerCAmelCase__ ).json() __lowerCamelCase : Any = {} try: job_links.update({job['name']: job['html_url'] for job in result['jobs']} ) __lowerCamelCase : Dict = math.ceil((result['total_count'] - 100) / 100 ) for i in range(lowerCAmelCase__ ): __lowerCamelCase : List[Any] = requests.get(url + f'&page={i + 2}' , headers=lowerCAmelCase__ ).json() job_links.update({job['name']: job['html_url'] for job in result['jobs']} ) return job_links except Exception: print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' ) return {} def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ): __lowerCamelCase : List[Any] = None if token is not None: __lowerCamelCase : Dict = {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'} __lowerCamelCase : Dict = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100' __lowerCamelCase : int = requests.get(lowerCAmelCase__ , headers=lowerCAmelCase__ ).json() __lowerCamelCase : Optional[Any] = {} try: artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} ) __lowerCamelCase : Union[str, Any] = math.ceil((result['total_count'] - 100) / 100 ) for i in range(lowerCAmelCase__ ): __lowerCamelCase : int = requests.get(url + f'&page={i + 2}' , headers=lowerCAmelCase__ ).json() artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} ) return artifacts except Exception: print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' ) return {} def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): __lowerCamelCase : Any = None if token is not None: __lowerCamelCase : Optional[Any] = {'Accept': 'application/vnd.github+json', 'Authorization': f'Bearer {token}'} __lowerCamelCase : str = requests.get(lowerCAmelCase__ , headers=lowerCAmelCase__ , allow_redirects=lowerCAmelCase__ ) __lowerCamelCase : Any = result.headers['Location'] __lowerCamelCase : List[str] = requests.get(lowerCAmelCase__ , allow_redirects=lowerCAmelCase__ ) __lowerCamelCase : Optional[int] = os.path.join(lowerCAmelCase__ , f'{artifact_name}.zip' ) with open(lowerCAmelCase__ , 'wb' ) as fp: fp.write(response.content ) def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ): __lowerCamelCase : str = [] __lowerCamelCase : List[Any] = [] __lowerCamelCase : int = None with zipfile.ZipFile(lowerCAmelCase__ ) as z: for filename in z.namelist(): if not os.path.isdir(lowerCAmelCase__ ): # read the file if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]: with z.open(lowerCAmelCase__ ) as f: for line in f: __lowerCamelCase : Union[str, Any] = line.decode('UTF-8' ).strip() if filename == "failures_line.txt": try: # `error_line` is the place where `error` occurs __lowerCamelCase : int = line[: line.index(': ' )] __lowerCamelCase : Dict = line[line.index(': ' ) + len(': ' ) :] errors.append([error_line, error] ) except Exception: # skip un-related lines pass elif filename == "summary_short.txt" and line.startswith('FAILED ' ): # `test` is the test method that failed __lowerCamelCase : Optional[Any] = line[len('FAILED ' ) :] failed_tests.append(lowerCAmelCase__ ) elif filename == "job_name.txt": __lowerCamelCase : Optional[Any] = line if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ): raise ValueError( f'`errors` and `failed_tests` should have the same number of elements. Got {len(lowerCAmelCase__ )} for `errors` ' f'and {len(lowerCAmelCase__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some' ' problem.' ) __lowerCamelCase : int = None if job_name and job_links: __lowerCamelCase : Dict = job_links.get(lowerCAmelCase__ , lowerCAmelCase__ ) # A list with elements of the form (line of error, error, failed test) __lowerCamelCase : Optional[int] = [x + [y] + [job_link] for x, y in zip(lowerCAmelCase__ , lowerCAmelCase__ )] return result def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ): __lowerCamelCase : Union[str, Any] = [] __lowerCamelCase : Any = [os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) for p in os.listdir(lowerCAmelCase__ ) if p.endswith('.zip' )] for p in paths: errors.extend(get_errors_from_single_artifact(lowerCAmelCase__ , job_links=lowerCAmelCase__ ) ) return errors def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ): __lowerCamelCase : int = Counter() counter.update([x[1] for x in logs] ) __lowerCamelCase : List[Any] = counter.most_common() __lowerCamelCase : Any = {} for error, count in counts: if error_filter is None or error not in error_filter: __lowerCamelCase : Optional[Any] = {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]} __lowerCamelCase : Any = dict(sorted(r.items() , key=lambda SCREAMING_SNAKE_CASE__ : item[1]["count"] , reverse=lowerCAmelCase__ ) ) return r def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ): __lowerCamelCase : Union[str, Any] = test.split('::' )[0] if test.startswith('tests/models/' ): __lowerCamelCase : Union[str, Any] = test.split('/' )[2] else: __lowerCamelCase : Optional[Any] = None return test def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ): __lowerCamelCase : Any = [(x[0], x[1], get_model(x[2] )) for x in logs] __lowerCamelCase : List[str] = [x for x in logs if x[2] is not None] __lowerCamelCase : List[Any] = {x[2] for x in logs} __lowerCamelCase : str = {} for test in tests: __lowerCamelCase : Any = Counter() # count by errors in `test` counter.update([x[1] for x in logs if x[2] == test] ) __lowerCamelCase : Any = counter.most_common() __lowerCamelCase : Dict = {error: count for error, count in counts if (error_filter is None or error not in error_filter)} __lowerCamelCase : str = sum(error_counts.values() ) if n_errors > 0: __lowerCamelCase : Any = {'count': n_errors, 'errors': error_counts} __lowerCamelCase : List[str] = dict(sorted(r.items() , key=lambda SCREAMING_SNAKE_CASE__ : item[1]["count"] , reverse=lowerCAmelCase__ ) ) return r def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ): __lowerCamelCase : Union[str, Any] = '| no. | error | status |' __lowerCamelCase : List[Any] = '|-:|:-|:-|' __lowerCamelCase : Tuple = [header, sep] for error in reduced_by_error: __lowerCamelCase : int = reduced_by_error[error]['count'] __lowerCamelCase : List[Any] = f'| {count} | {error[:100]} | |' lines.append(lowerCAmelCase__ ) return "\n".join(lowerCAmelCase__ ) def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ): __lowerCamelCase : int = '| model | no. of errors | major error | count |' __lowerCamelCase : Optional[Any] = '|-:|-:|-:|-:|' __lowerCamelCase : str = [header, sep] for model in reduced_by_model: __lowerCamelCase : Dict = reduced_by_model[model]['count'] __lowerCamelCase , __lowerCamelCase : str = list(reduced_by_model[model]['errors'].items() )[0] __lowerCamelCase : Dict = f'| {model} | {count} | {error[:60]} | {_count} |' lines.append(lowerCAmelCase__ ) return "\n".join(lowerCAmelCase__ ) if __name__ == "__main__": lowercase_ = argparse.ArgumentParser() # Required parameters parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.') parser.add_argument( '--output_dir', type=str, required=True, help='Where to store the downloaded artifacts and other result files.', ) parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.') lowercase_ = parser.parse_args() os.makedirs(args.output_dir, exist_ok=True) lowercase_ = get_job_links(args.workflow_run_id, token=args.token) lowercase_ = {} # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee. # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`. if _job_links: for k, v in _job_links.items(): # This is how GitHub actions combine job names. if " / " in k: lowercase_ = k.find(' / ') lowercase_ = k[index + len(' / ') :] lowercase_ = v with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp: json.dump(job_links, fp, ensure_ascii=False, indent=4) lowercase_ = get_artifacts_links(args.workflow_run_id, token=args.token) with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp: json.dump(artifacts, fp, ensure_ascii=False, indent=4) for idx, (name, url) in enumerate(artifacts.items()): download_artifact(name, url, args.output_dir, args.token) # Be gentle to GitHub time.sleep(1) lowercase_ = get_all_errors(args.output_dir, job_links=job_links) # `e[1]` is the error lowercase_ = Counter() counter.update([e[1] for e in errors]) # print the top 30 most common test errors lowercase_ = counter.most_common(3_0) for item in most_common: print(item) with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp: json.dump(errors, fp, ensure_ascii=False, indent=4) lowercase_ = reduce_by_error(errors) lowercase_ = reduce_by_model(errors) lowercase_ = make_github_table(reduced_by_error) lowercase_ = make_github_table_per_model(reduced_by_model) with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp: fp.write(sa) with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp: fp.write(sa)

import argparse import json import os import torch from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer from transformers.tokenization_utils_base import AddedToken @torch.no_grad() def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): # Load configuration defined in the metadata file with open(SCREAMING_SNAKE_CASE__ ) as metadata_file: __lowerCamelCase : List[str] = json.load(SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : int = LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ , **metadata['model_config'] ) # Load in the weights from the checkpoint_path __lowerCamelCase : Union[str, Any] = torch.load(SCREAMING_SNAKE_CASE__ , map_location='cpu' ) # Load the entity vocab file __lowerCamelCase : List[Any] = load_entity_vocab(SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : Optional[int] = RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] ) # Add special tokens to the token vocabulary for downstream tasks __lowerCamelCase : str = AddedToken('<ent>' , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : Dict = AddedToken('<ent2>' , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} ) config.vocab_size += 2 print(f'Saving tokenizer to {pytorch_dump_folder_path}' ) tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ ) with open(os.path.join(SCREAMING_SNAKE_CASE__ , LukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : str = LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ) # Initialize the embeddings of the special tokens __lowerCamelCase : Union[str, Any] = state_dict['embeddings.word_embeddings.weight'] __lowerCamelCase : Tuple = word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 ) __lowerCamelCase : Any = word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 ) __lowerCamelCase : List[str] = torch.cat([word_emb, ent_emb, enta_emb] ) # Initialize the query layers of the entity-aware self-attention mechanism for layer_index in range(config.num_hidden_layers ): for matrix_name in ["query.weight", "query.bias"]: __lowerCamelCase : Optional[int] = f'encoder.layer.{layer_index}.attention.self.' __lowerCamelCase : Dict = state_dict[prefix + matrix_name] __lowerCamelCase : List[Any] = state_dict[prefix + matrix_name] __lowerCamelCase : Union[str, Any] = state_dict[prefix + matrix_name] # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks __lowerCamelCase : Optional[int] = state_dict['entity_embeddings.entity_embeddings.weight'] __lowerCamelCase : Union[str, Any] = entity_emb[entity_vocab['[MASK]']] __lowerCamelCase : Optional[Any] = LukeModel(config=SCREAMING_SNAKE_CASE__ ).eval() __lowerCamelCase , __lowerCamelCase : List[Any] = model.load_state_dict(SCREAMING_SNAKE_CASE__ , strict=SCREAMING_SNAKE_CASE__ ) if not (len(SCREAMING_SNAKE_CASE__ ) == 1 and missing_keys[0] == "embeddings.position_ids"): raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__ )}. Expected only missing embeddings.position_ids' ) if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )): raise ValueError( 'Unexpected keys' f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' ) # Check outputs __lowerCamelCase : Optional[Any] = LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ , task='entity_classification' ) __lowerCamelCase : Dict = ( 'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the' ' new world number one avoid a humiliating second- round exit at Wimbledon .' ) __lowerCamelCase : Union[str, Any] = (39, 42) __lowerCamelCase : Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , entity_spans=[span] , add_prefix_space=SCREAMING_SNAKE_CASE__ , return_tensors='pt' ) __lowerCamelCase : List[str] = model(**SCREAMING_SNAKE_CASE__ ) # Verify word hidden states if model_size == "large": __lowerCamelCase : Dict = torch.Size((1, 42, 1_024) ) __lowerCamelCase : int = torch.tensor( [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] ) else: # base __lowerCamelCase : Union[str, Any] = torch.Size((1, 42, 768) ) __lowerCamelCase : Tuple = torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] ) if not (outputs.last_hidden_state.shape == expected_shape): raise ValueError( f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' ) if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ): raise ValueError # Verify entity hidden states if model_size == "large": __lowerCamelCase : Union[str, Any] = torch.Size((1, 1, 1_024) ) __lowerCamelCase : Dict = torch.tensor([[0.0_466, -0.0_106, -0.0_179]] ) else: # base __lowerCamelCase : int = torch.Size((1, 1, 768) ) __lowerCamelCase : Dict = torch.tensor([[0.1_457, 0.1_044, 0.0_174]] ) if not (outputs.entity_last_hidden_state.shape != expected_shape): raise ValueError( f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is' f' {expected_shape}' ) if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ): raise ValueError # Finally, save our PyTorch model and tokenizer print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__ ) ) model.save_pretrained(SCREAMING_SNAKE_CASE__ ) def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ): __lowerCamelCase : Tuple = {} with open(SCREAMING_SNAKE_CASE__ , 'r' , encoding='utf-8' ) as f: for index, line in enumerate(SCREAMING_SNAKE_CASE__ ): __lowerCamelCase , __lowerCamelCase : List[Any] = line.rstrip().split('\t' ) __lowerCamelCase : Any = index return entity_vocab if __name__ == "__main__": lowercase_ = argparse.ArgumentParser() # Required parameters parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.') parser.add_argument( '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.' ) parser.add_argument( '--entity_vocab_path', default=None, type=str, help='Path to an entity_vocab.tsv file, containing the entity vocabulary.', ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.' ) parser.add_argument( '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.' ) lowercase_ = parser.parse_args() convert_luke_checkpoint( args.checkpoint_path, args.metadata_path, args.entity_vocab_path, args.pytorch_dump_folder_path, args.model_size, )

'''simple docstring''' import unittest from datasets import load_dataset from transformers.pipelines import pipeline from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow @is_pipeline_test @require_torch class lowerCamelCase_ ( unittest.TestCase ): @require_torch def lowercase_ ( self : Optional[int] ): '''simple docstring''' UpperCAmelCase__ : Optional[int] = pipeline( task='''zero-shot-audio-classification''' , model='''hf-internal-testing/tiny-clap-htsat-unfused''' ) UpperCAmelCase__ : Optional[int] = load_dataset('''ashraq/esc50''' ) UpperCAmelCase__ : Optional[Any] = dataset['''train''']['''audio'''][-1]['''array'''] UpperCAmelCase__ : str = audio_classifier(__A , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] ) self.assertEqual( nested_simplify(__A ) , [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}] , ) @unittest.skip('''No models are available in TF''' ) def lowercase_ ( self : Any ): '''simple docstring''' pass @slow @require_torch def lowercase_ ( self : Any ): '''simple docstring''' UpperCAmelCase__ : Optional[int] = pipeline( task='''zero-shot-audio-classification''' , model='''laion/clap-htsat-unfused''' , ) # This is an audio of a dog UpperCAmelCase__ : Tuple = load_dataset('''ashraq/esc50''' ) UpperCAmelCase__ : Optional[Any] = dataset['''train''']['''audio'''][-1]['''array'''] UpperCAmelCase__ : List[str] = audio_classifier(__A , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] ) self.assertEqual( nested_simplify(__A ) , [ {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''}, {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''}, ] , ) UpperCAmelCase__ : str = audio_classifier([audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] ) self.assertEqual( nested_simplify(__A ) , [ [ {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''}, {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''}, ], ] * 5 , ) UpperCAmelCase__ : Dict = audio_classifier( [audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] , batch_size=5 ) self.assertEqual( nested_simplify(__A ) , [ [ {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''}, {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''}, ], ] * 5 , ) @unittest.skip('''No models are available in TF''' ) def lowercase_ ( self : Optional[Any] ): '''simple docstring''' pass

"""simple docstring""" from transformers import DistilBertTokenizer, DistilBertTokenizerFast from transformers.testing_utils import require_tokenizers, slow from ..bert.test_tokenization_bert import BertTokenizationTest @require_tokenizers class __A ( _SCREAMING_SNAKE_CASE ): """simple docstring""" __lowerCAmelCase = DistilBertTokenizer __lowerCAmelCase = DistilBertTokenizerFast __lowerCAmelCase = True @slow def SCREAMING_SNAKE_CASE ( self ) -> int: a =DistilBertTokenizer.from_pretrained('''distilbert-base-uncased''' ) a =tokenizer.encode('''sequence builders''' , add_special_tokens=__A ) a =tokenizer.encode('''multi-sequence build''' , add_special_tokens=__A ) a =tokenizer.build_inputs_with_special_tokens(__A ) a =tokenizer.build_inputs_with_special_tokens(__A , __A ) assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [ tokenizer.sep_token_id ]

import collections.abc from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACTaFN from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention from ...modeling_utils import PreTrainedModel from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_poolformer import PoolFormerConfig __lowerCamelCase : Any = logging.get_logger(__name__) # General docstring __lowerCamelCase : Dict = """PoolFormerConfig""" # Base docstring __lowerCamelCase : Dict = """sail/poolformer_s12""" __lowerCamelCase : Any = [1, 512, 7, 7] # Image classification docstring __lowerCamelCase : Optional[int] = """sail/poolformer_s12""" __lowerCamelCase : Dict = """tabby, tabby cat""" __lowerCamelCase : Dict = [ """sail/poolformer_s12""", # See all PoolFormer models at https://huggingface.co./models?filter=poolformer ] def SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : float = 0.0 , snake_case_ : bool = False ): if drop_prob == 0.0 or not training: return input snake_case__ : str = 1 - drop_prob snake_case__ : Dict = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets snake_case__ : List[Any] = keep_prob + torch.rand(snake_case_ , dtype=input.dtype , device=input.device ) random_tensor.floor_() # binarize snake_case__ : Tuple = input.div(snake_case_ ) * random_tensor return output class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : str , __A : Optional[float] = None ): super().__init__() snake_case__ : Union[str, Any] = drop_prob def _lowercase ( self : List[Any] , __A : torch.Tensor ): return drop_path(__A , self.drop_prob , self.training ) def _lowercase ( self : Tuple ): return "p={}".format(self.drop_prob ) class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : Optional[Any] , __A : List[Any] , __A : str , __A : List[Any] , __A : List[str] , __A : List[Any] , __A : Optional[int]=None ): super().__init__() snake_case__ : str = patch_size if isinstance(__A , collections.abc.Iterable ) else (patch_size, patch_size) snake_case__ : int = stride if isinstance(__A , collections.abc.Iterable ) else (stride, stride) snake_case__ : str = padding if isinstance(__A , collections.abc.Iterable ) else (padding, padding) snake_case__ : Union[str, Any] = nn.Convad(__A , __A , kernel_size=__A , stride=__A , padding=__A ) snake_case__ : Tuple = norm_layer(__A ) if norm_layer else nn.Identity() def _lowercase ( self : Any , __A : List[str] ): snake_case__ : str = self.projection(__A ) snake_case__ : List[str] = self.norm(__A ) return embeddings class SCREAMING_SNAKE_CASE__ ( nn.GroupNorm ): def __init__( self : Dict , __A : Tuple , **__A : Optional[Any] ): super().__init__(1 , __A , **__A ) class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : List[Any] , __A : List[Any] ): super().__init__() snake_case__ : Union[str, Any] = nn.AvgPoolad(__A , stride=1 , padding=pool_size // 2 , count_include_pad=__A ) def _lowercase ( self : Dict , __A : str ): return self.pool(__A ) - hidden_states class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : Optional[int] , __A : Optional[int] , __A : str , __A : Dict , __A : Dict ): super().__init__() snake_case__ : int = nn.Convad(__A , __A , 1 ) snake_case__ : Optional[Any] = nn.Convad(__A , __A , 1 ) snake_case__ : Union[str, Any] = PoolFormerDropPath(__A ) if isinstance(config.hidden_act , __A ): snake_case__ : Any = ACTaFN[config.hidden_act] else: snake_case__ : Union[str, Any] = config.hidden_act def _lowercase ( self : Dict , __A : List[str] ): snake_case__ : str = self.conva(__A ) snake_case__ : Tuple = self.act_fn(__A ) snake_case__ : Tuple = self.drop(__A ) snake_case__ : List[str] = self.conva(__A ) snake_case__ : Tuple = self.drop(__A ) return hidden_states class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : List[Any] , __A : int , __A : Tuple , __A : Tuple , __A : str , __A : Dict , __A : Union[str, Any] ): super().__init__() snake_case__ : List[Any] = PoolFormerPooling(__A ) snake_case__ : Optional[Any] = PoolFormerOutput(__A , __A , __A , __A ) snake_case__ : Optional[Any] = PoolFormerGroupNorm(__A ) snake_case__ : int = PoolFormerGroupNorm(__A ) # Useful for training neural nets snake_case__ : Union[str, Any] = PoolFormerDropPath(__A ) if drop_path > 0.0 else nn.Identity() snake_case__ : Any = config.use_layer_scale if config.use_layer_scale: snake_case__ : Optional[Any] = nn.Parameter( config.layer_scale_init_value * torch.ones((__A) ) , requires_grad=__A ) snake_case__ : Any = nn.Parameter( config.layer_scale_init_value * torch.ones((__A) ) , requires_grad=__A ) def _lowercase ( self : Any , __A : Any ): if self.use_layer_scale: snake_case__ : Optional[Any] = self.pooling(self.before_norm(__A ) ) snake_case__ : Tuple = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output # First residual connection snake_case__ : Dict = hidden_states + self.drop_path(__A ) snake_case__ : Optional[Any] = () snake_case__ : Optional[Any] = self.output(self.after_norm(__A ) ) snake_case__ : Tuple = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output # Second residual connection snake_case__ : int = hidden_states + self.drop_path(__A ) snake_case__ : Any = (output,) + outputs return outputs else: snake_case__ : str = self.drop_path(self.pooling(self.before_norm(__A ) ) ) # First residual connection snake_case__ : Tuple = pooling_output + hidden_states snake_case__ : Union[str, Any] = () # Second residual connection inside the PoolFormerOutput block snake_case__ : Optional[int] = self.drop_path(self.output(self.after_norm(__A ) ) ) snake_case__ : Tuple = hidden_states + layer_output snake_case__ : str = (output,) + outputs return outputs class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : List[Any] , __A : str ): super().__init__() snake_case__ : Optional[int] = config # stochastic depth decay rule snake_case__ : List[Any] = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )] # patch embeddings snake_case__ : Any = [] for i in range(config.num_encoder_blocks ): embeddings.append( PoolFormerEmbeddings( patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) ) snake_case__ : List[str] = nn.ModuleList(__A ) # Transformer blocks snake_case__ : Dict = [] snake_case__ : List[str] = 0 for i in range(config.num_encoder_blocks ): # each block consists of layers snake_case__ : List[str] = [] if i != 0: cur += config.depths[i - 1] for j in range(config.depths[i] ): layers.append( PoolFormerLayer( __A , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) ) blocks.append(nn.ModuleList(__A ) ) snake_case__ : Optional[int] = nn.ModuleList(__A ) def _lowercase ( self : Optional[Any] , __A : Tuple , __A : List[str]=False , __A : int=True ): snake_case__ : Dict = () if output_hidden_states else None snake_case__ : str = pixel_values for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ): snake_case__ : Tuple = layers # Get patch embeddings from hidden_states snake_case__ : str = embedding_layer(__A ) # Send the embeddings through the blocks for _, blk in enumerate(__A ): snake_case__ : int = blk(__A ) snake_case__ : List[Any] = layer_outputs[0] if output_hidden_states: snake_case__ : Optional[int] = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None ) return BaseModelOutputWithNoAttention(last_hidden_state=__A , hidden_states=__A ) class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ): a_ = PoolFormerConfig a_ = "poolformer" a_ = "pixel_values" a_ = True def _lowercase ( self : Dict , __A : Any ): if isinstance(__A , (nn.Linear, nn.Convad) ): module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range ) if module.bias is not None: module.bias.data.zero_() elif isinstance(__A , nn.LayerNorm ): module.bias.data.zero_() module.weight.data.fill_(1.0 ) def _lowercase ( self : Tuple , __A : List[Any] , __A : Dict=False ): if isinstance(__A , __A ): snake_case__ : str = value __lowerCamelCase : Optional[int] = R""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ __lowerCamelCase : Dict = R""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`PoolFormerImageProcessor.__call__`] for details. """ @add_start_docstrings( "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top." , UpperCamelCase_ , ) class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ): def __init__( self : str , __A : Optional[int] ): super().__init__(__A ) snake_case__ : str = config snake_case__ : Union[str, Any] = PoolFormerEncoder(__A ) # Initialize weights and apply final processing self.post_init() def _lowercase ( self : Union[str, Any] ): return self.embeddings.patch_embeddings @add_start_docstrings_to_model_forward(__A ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC , output_type=__A , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , ) def _lowercase ( self : Any , __A : Optional[torch.FloatTensor] = None , __A : Optional[bool] = None , __A : Optional[bool] = None , ): snake_case__ : List[str] = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) snake_case__ : Tuple = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values" ) snake_case__ : Any = self.encoder( __A , output_hidden_states=__A , return_dict=__A , ) snake_case__ : List[str] = encoder_outputs[0] if not return_dict: return (sequence_output, None) + encoder_outputs[1:] return BaseModelOutputWithNoAttention( last_hidden_state=__A , hidden_states=encoder_outputs.hidden_states , ) class SCREAMING_SNAKE_CASE__ ( nn.Module ): def __init__( self : List[Any] , __A : Optional[Any] ): super().__init__() snake_case__ : Dict = nn.Linear(config.hidden_size , config.hidden_size ) def _lowercase ( self : Dict , __A : List[Any] ): snake_case__ : int = self.dense(__A ) return output @add_start_docstrings( "\n PoolFormer Model transformer with an image classification head on top\n " , UpperCamelCase_ , ) class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ): def __init__( self : Any , __A : Tuple ): super().__init__(__A ) snake_case__ : Optional[Any] = config.num_labels snake_case__ : int = PoolFormerModel(__A ) # Final norm snake_case__ : Any = PoolFormerGroupNorm(config.hidden_sizes[-1] ) # Classifier head snake_case__ : int = ( nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(__A ) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , ) def _lowercase ( self : int , __A : Optional[torch.FloatTensor] = None , __A : Optional[torch.LongTensor] = None , __A : Optional[bool] = None , __A : Optional[bool] = None , ): snake_case__ : str = return_dict if return_dict is not None else self.config.use_return_dict snake_case__ : List[str] = self.poolformer( __A , output_hidden_states=__A , return_dict=__A , ) snake_case__ : Optional[Any] = outputs[0] snake_case__ : str = self.classifier(self.norm(__A ).mean([-2, -1] ) ) snake_case__ : Any = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: snake_case__ : List[str] = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): snake_case__ : str = "single_label_classification" else: snake_case__ : Any = "multi_label_classification" if self.config.problem_type == "regression": snake_case__ : Tuple = MSELoss() if self.num_labels == 1: snake_case__ : Optional[Any] = loss_fct(logits.squeeze() , labels.squeeze() ) else: snake_case__ : List[Any] = loss_fct(__A , __A ) elif self.config.problem_type == "single_label_classification": snake_case__ : Optional[int] = CrossEntropyLoss() snake_case__ : Any = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) ) elif self.config.problem_type == "multi_label_classification": snake_case__ : int = BCEWithLogitsLoss() snake_case__ : Union[str, Any] = loss_fct(__A , __A ) if not return_dict: snake_case__ : str = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=__A , logits=__A , hidden_states=outputs.hidden_states )

import json import pathlib import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import DeformableDetrImageProcessor class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ): """simple docstring""" def __init__( self : List[str] , __A : int , __A : str=7 , __A : Union[str, Any]=3 , __A : Union[str, Any]=3_0 , __A : Optional[int]=4_0_0 , __A : Optional[Any]=True , __A : Optional[int]=None , __A : Union[str, Any]=True , __A : Optional[int]=[0.5, 0.5, 0.5] , __A : Any=[0.5, 0.5, 0.5] , __A : Optional[int]=True , __A : Optional[Any]=1 / 2_5_5 , __A : Union[str, Any]=True , ): # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p snake_case__ : Optional[Any] = size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3} snake_case__ : List[Any] = parent snake_case__ : Union[str, Any] = batch_size snake_case__ : Tuple = num_channels snake_case__ : List[Any] = min_resolution snake_case__ : Optional[Any] = max_resolution snake_case__ : str = do_resize snake_case__ : List[str] = size snake_case__ : List[Any] = do_normalize snake_case__ : Dict = image_mean snake_case__ : List[Any] = image_std snake_case__ : int = do_rescale snake_case__ : Tuple = rescale_factor snake_case__ : str = do_pad def _lowercase ( self : List[str] ): return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_pad": self.do_pad, } def _lowercase ( self : Optional[Any] , __A : Dict , __A : Union[str, Any]=False ): if not batched: snake_case__ : List[str] = image_inputs[0] if isinstance(__A , Image.Image ): snake_case__, snake_case__ : Any = image.size else: snake_case__, snake_case__ : List[str] = image.shape[1], image.shape[2] if w < h: snake_case__ : List[str] = int(self.size["shortest_edge"] * h / w ) snake_case__ : Tuple = self.size["shortest_edge"] elif w > h: snake_case__ : Optional[int] = self.size["shortest_edge"] snake_case__ : Union[str, Any] = int(self.size["shortest_edge"] * w / h ) else: snake_case__ : Optional[Any] = self.size["shortest_edge"] snake_case__ : List[Any] = self.size["shortest_edge"] else: snake_case__ : Union[str, Any] = [] for image in image_inputs: snake_case__, snake_case__ : int = self.get_expected_values([image] ) expected_values.append((expected_height, expected_width) ) snake_case__ : Any = max(__A , key=lambda __A : item[0] )[0] snake_case__ : Optional[int] = max(__A , key=lambda __A : item[1] )[1] return expected_height, expected_width @require_torch @require_vision class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ , unittest.TestCase ): """simple docstring""" a_ = DeformableDetrImageProcessor if is_vision_available() else None def _lowercase ( self : Optional[int] ): snake_case__ : str = DeformableDetrImageProcessingTester(self ) @property def _lowercase ( self : List[Any] ): return self.image_processor_tester.prepare_image_processor_dict() def _lowercase ( self : Union[str, Any] ): snake_case__ : int = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__A , "image_mean" ) ) self.assertTrue(hasattr(__A , "image_std" ) ) self.assertTrue(hasattr(__A , "do_normalize" ) ) self.assertTrue(hasattr(__A , "do_resize" ) ) self.assertTrue(hasattr(__A , "do_rescale" ) ) self.assertTrue(hasattr(__A , "do_pad" ) ) self.assertTrue(hasattr(__A , "size" ) ) def _lowercase ( self : Tuple ): snake_case__ : int = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {"shortest_edge": 1_8, "longest_edge": 1_3_3_3} ) self.assertEqual(image_processor.do_pad , __A ) snake_case__ : List[Any] = self.image_processing_class.from_dict( self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=__A ) self.assertEqual(image_processor.size , {"shortest_edge": 4_2, "longest_edge": 8_4} ) self.assertEqual(image_processor.do_pad , __A ) def _lowercase ( self : Any ): pass def _lowercase ( self : Optional[int] ): # Initialize image_processing snake_case__ : List[str] = self.image_processing_class(**self.image_processor_dict ) # create random PIL images snake_case__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A ) for image in image_inputs: self.assertIsInstance(__A , Image.Image ) # Test not batched input snake_case__ : Tuple = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values snake_case__, snake_case__ : Dict = self.image_processor_tester.get_expected_values(__A ) self.assertEqual( encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , ) # Test batched snake_case__, snake_case__ : Optional[Any] = self.image_processor_tester.get_expected_values(__A , batched=__A ) snake_case__ : Union[str, Any] = image_processing(__A , return_tensors="pt" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ) , ) def _lowercase ( self : Any ): # Initialize image_processing snake_case__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors snake_case__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , numpify=__A ) for image in image_inputs: self.assertIsInstance(__A , np.ndarray ) # Test not batched input snake_case__ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values snake_case__, snake_case__ : Union[str, Any] = self.image_processor_tester.get_expected_values(__A ) self.assertEqual( encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , ) # Test batched snake_case__ : Union[str, Any] = image_processing(__A , return_tensors="pt" ).pixel_values snake_case__, snake_case__ : Any = self.image_processor_tester.get_expected_values(__A , batched=__A ) self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ) , ) def _lowercase ( self : Union[str, Any] ): # Initialize image_processing snake_case__ : str = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors snake_case__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , torchify=__A ) for image in image_inputs: self.assertIsInstance(__A , torch.Tensor ) # Test not batched input snake_case__ : Tuple = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values snake_case__, snake_case__ : List[Any] = self.image_processor_tester.get_expected_values(__A ) self.assertEqual( encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , ) # Test batched snake_case__ : Optional[Any] = image_processing(__A , return_tensors="pt" ).pixel_values snake_case__, snake_case__ : int = self.image_processor_tester.get_expected_values(__A , batched=__A ) self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, expected_height, expected_width, ) , ) @slow def _lowercase ( self : Optional[int] ): # prepare image and target snake_case__ : List[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ) with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f: snake_case__ : Any = json.loads(f.read() ) snake_case__ : List[str] = {"image_id": 3_9_7_6_9, "annotations": target} # encode them snake_case__ : Optional[Any] = DeformableDetrImageProcessor() snake_case__ : Tuple = image_processing(images=__A , annotations=__A , return_tensors="pt" ) # verify pixel values snake_case__ : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] ) self.assertEqual(encoding["pixel_values"].shape , __A ) snake_case__ : int = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] ) self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __A , atol=1e-4 ) ) # verify area snake_case__ : int = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] ) self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __A ) ) # verify boxes snake_case__ : str = torch.Size([6, 4] ) self.assertEqual(encoding["labels"][0]["boxes"].shape , __A ) snake_case__ : List[Any] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] ) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __A , atol=1e-3 ) ) # verify image_id snake_case__ : int = torch.tensor([3_9_7_6_9] ) self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __A ) ) # verify is_crowd snake_case__ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] ) self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __A ) ) # verify class_labels snake_case__ : Optional[int] = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] ) self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __A ) ) # verify orig_size snake_case__ : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] ) self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __A ) ) # verify size snake_case__ : List[str] = torch.tensor([8_0_0, 1_0_6_6] ) self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __A ) ) @slow def _lowercase ( self : Union[str, Any] ): # prepare image, target and masks_path snake_case__ : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ) with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f: snake_case__ : Optional[int] = json.loads(f.read() ) snake_case__ : Any = {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target} snake_case__ : List[Any] = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" ) # encode them snake_case__ : Dict = DeformableDetrImageProcessor(format="coco_panoptic" ) snake_case__ : List[Any] = image_processing(images=__A , annotations=__A , masks_path=__A , return_tensors="pt" ) # verify pixel values snake_case__ : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] ) self.assertEqual(encoding["pixel_values"].shape , __A ) snake_case__ : Tuple = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] ) self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __A , atol=1e-4 ) ) # verify area snake_case__ : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] ) self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __A ) ) # verify boxes snake_case__ : Union[str, Any] = torch.Size([6, 4] ) self.assertEqual(encoding["labels"][0]["boxes"].shape , __A ) snake_case__ : Tuple = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] ) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __A , atol=1e-3 ) ) # verify image_id snake_case__ : Union[str, Any] = torch.tensor([3_9_7_6_9] ) self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __A ) ) # verify is_crowd snake_case__ : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] ) self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __A ) ) # verify class_labels snake_case__ : List[str] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] ) self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __A ) ) # verify masks snake_case__ : Optional[Any] = 8_2_2_8_7_3 self.assertEqual(encoding["labels"][0]["masks"].sum().item() , __A ) # verify orig_size snake_case__ : Dict = torch.tensor([4_8_0, 6_4_0] ) self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __A ) ) # verify size snake_case__ : Dict = torch.tensor([8_0_0, 1_0_6_6] ) self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __A ) )

"""simple docstring""" import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import SchedulerMixin, SchedulerOutput class A_ (lowercase__ ,lowercase__ ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : Tuple = 1 @register_to_config def __init__( self , lowercase_ = 1000 , lowercase_ = None ): """simple docstring""" self.set_timesteps(__lowerCAmelCase ) # standard deviation of the initial noise distribution UpperCAmelCase_ : List[str] = 1.0 # For now we only support F-PNDM, i.e. the runge-kutta method # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf # mainly at formula (9), (12), (13) and the Algorithm 2. UpperCAmelCase_ : Union[str, Any] = 4 # running values UpperCAmelCase_ : Optional[Any] = [] def UpperCamelCase__ ( self , lowercase_ , lowercase_ = None ): """simple docstring""" UpperCAmelCase_ : Optional[int] = num_inference_steps UpperCAmelCase_ : Any = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1] UpperCAmelCase_ : int = torch.cat([steps, torch.tensor([0.0] )] ) if self.config.trained_betas is not None: UpperCAmelCase_ : Any = torch.tensor(self.config.trained_betas , dtype=torch.floataa ) else: UpperCAmelCase_ : Optional[Any] = torch.sin(steps * math.pi / 2 ) ** 2 UpperCAmelCase_ : Optional[int] = (1.0 - self.betas**2) ** 0.5 UpperCAmelCase_ : str = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1] UpperCAmelCase_ : str = timesteps.to(__lowerCAmelCase ) UpperCAmelCase_ : Dict = [] def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ = True , ): """simple docstring""" if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) UpperCAmelCase_ : Tuple = (self.timesteps == timestep).nonzero().item() UpperCAmelCase_ : List[str] = timestep_index + 1 UpperCAmelCase_ : Optional[Any] = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index] self.ets.append(__lowerCAmelCase ) if len(self.ets ) == 1: UpperCAmelCase_ : Optional[int] = self.ets[-1] elif len(self.ets ) == 2: UpperCAmelCase_ : int = (3 * self.ets[-1] - self.ets[-2]) / 2 elif len(self.ets ) == 3: UpperCAmelCase_ : List[str] = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12 else: UpperCAmelCase_ : Tuple = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4]) UpperCAmelCase_ : Optional[Any] = self._get_prev_sample(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=__lowerCAmelCase ) def UpperCamelCase__ ( self , lowercase_ , *lowercase_ , **lowercase_ ): """simple docstring""" return sample def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : Optional[int] = self.alphas[timestep_index] UpperCAmelCase_ : Tuple = self.betas[timestep_index] UpperCAmelCase_ : List[Any] = self.alphas[prev_timestep_index] UpperCAmelCase_ : str = self.betas[prev_timestep_index] UpperCAmelCase_ : str = (sample - sigma * ets) / max(__lowerCAmelCase , 1E-8 ) UpperCAmelCase_ : int = next_alpha * pred + ets * next_sigma return prev_sample def __len__( self ): """simple docstring""" return self.config.num_train_timesteps

"""simple docstring""" from queue import Queue from typing import TYPE_CHECKING, Optional if TYPE_CHECKING: from ..models.auto import AutoTokenizer class _A : def A__ ( self , __lowerCAmelCase ): """simple docstring""" raise NotImplementedError() def A__ ( self ): """simple docstring""" raise NotImplementedError() class _A ( lowerCAmelCase ): def __init__( self , __lowerCAmelCase , __lowerCAmelCase = False , **__lowerCAmelCase ): """simple docstring""" lowercase = tokenizer lowercase = skip_prompt lowercase = decode_kwargs # variables used in the streaming process lowercase = [] lowercase = 0 lowercase = True def A__ ( self , __lowerCAmelCase ): """simple docstring""" if len(value.shape ) > 1 and value.shape[0] > 1: raise ValueError("""TextStreamer only supports batch size 1""" ) elif len(value.shape ) > 1: lowercase = value[0] if self.skip_prompt and self.next_tokens_are_prompt: lowercase = False return # Add the new token to the cache and decodes the entire thing. self.token_cache.extend(value.tolist() ) lowercase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs ) # After the symbol for a new line, we flush the cache. if text.endswith("""\n""" ): lowercase = text[self.print_len :] lowercase = [] lowercase = 0 # If the last token is a CJK character, we print the characters. elif len(__lowerCAmelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ): lowercase = text[self.print_len :] self.print_len += len(__lowerCAmelCase ) # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words, # which may change with the subsequent token -- there are probably smarter ways to do this!) else: lowercase = text[self.print_len : text.rfind(""" """ ) + 1] self.print_len += len(__lowerCAmelCase ) self.on_finalized_text(__lowerCAmelCase ) def A__ ( self ): """simple docstring""" if len(self.token_cache ) > 0: lowercase = self.tokenizer.decode(self.token_cache , **self.decode_kwargs ) lowercase = text[self.print_len :] lowercase = [] lowercase = 0 else: lowercase = """""" lowercase = True self.on_finalized_text(__lowerCAmelCase , stream_end=__lowerCAmelCase ) def A__ ( self , __lowerCAmelCase , __lowerCAmelCase = False ): """simple docstring""" print(__lowerCAmelCase , flush=__lowerCAmelCase , end="""""" if not stream_end else None ) def A__ ( self , __lowerCAmelCase ): """simple docstring""" if ( (cp >= 0X4_e00 and cp <= 0X9_fff) or (cp >= 0X3_400 and cp <= 0X4_dbf) # or (cp >= 0X20_000 and cp <= 0X2a_6df) # or (cp >= 0X2a_700 and cp <= 0X2b_73f) # or (cp >= 0X2b_740 and cp <= 0X2b_81f) # or (cp >= 0X2b_820 and cp <= 0X2c_eaf) # or (cp >= 0Xf_900 and cp <= 0Xf_aff) or (cp >= 0X2f_800 and cp <= 0X2f_a1f) # ): # return True return False class _A ( lowerCAmelCase ): def __init__( self , __lowerCAmelCase , __lowerCAmelCase = False , __lowerCAmelCase = None , **__lowerCAmelCase ): """simple docstring""" super().__init__(__lowerCAmelCase , __lowerCAmelCase , **__lowerCAmelCase ) lowercase = Queue() lowercase = None lowercase = timeout def A__ ( self , __lowerCAmelCase , __lowerCAmelCase = False ): """simple docstring""" self.text_queue.put(__lowerCAmelCase , timeout=self.timeout ) if stream_end: self.text_queue.put(self.stop_signal , timeout=self.timeout ) def __iter__( self ): """simple docstring""" return self def A__ ( self ): """simple docstring""" lowercase = self.text_queue.get(timeout=self.timeout ) if value == self.stop_signal: raise StopIteration() else: return value

"""simple docstring""" import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __lowercase ( _UpperCamelCase , unittest.TestCase ): '''simple docstring''' __lowerCAmelCase = MgpstrTokenizer __lowerCAmelCase = False __lowerCAmelCase = {} __lowerCAmelCase = False def _lowerCamelCase ( self ): super().setUp() # fmt: off __a : Dict = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z'''] # fmt: on __a : Dict = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) ) __a : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write(json.dumps(_UpperCAmelCase ) + '''\n''' ) def _lowerCamelCase ( self , **_UpperCAmelCase ): return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase ) def _lowerCamelCase ( self , _UpperCAmelCase ): __a : List[Any] = '''tester''' __a : Union[str, Any] = '''tester''' return input_text, output_text @unittest.skip('''MGP-STR always lower cases letters.''' ) def _lowerCamelCase ( self ): pass def _lowerCamelCase ( self ): __a : List[str] = self.get_tokenizers(do_lower_case=_UpperCAmelCase ) for tokenizer in tokenizers: with self.subTest(f"""{tokenizer.__class__.__name__}""" ): __a : List[Any] = '''[SPECIAL_TOKEN]''' tokenizer.add_special_tokens({'''cls_token''': special_token} ) __a : List[str] = tokenizer.encode([special_token] , add_special_tokens=_UpperCAmelCase ) self.assertEqual(len(_UpperCAmelCase ) , 1 ) __a : List[Any] = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase ) self.assertTrue(special_token not in decoded ) def _lowerCamelCase ( self ): __a : Optional[Any] = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"""{tokenizer.__class__.__name__}""" ): __a , __a : Optional[Any] = self.get_input_output_texts(_UpperCAmelCase ) __a : Tuple = tokenizer.tokenize(_UpperCAmelCase ) __a : Any = tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) __a : List[Any] = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) __a : Optional[int] = tokenizer.convert_ids_to_tokens(_UpperCAmelCase ) self.assertNotEqual(len(_UpperCAmelCase ) , 0 ) __a : Union[str, Any] = tokenizer.decode(_UpperCAmelCase ) self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase ) self.assertEqual(text_a.replace(''' ''' , '''''' ) , _UpperCAmelCase ) @unittest.skip('''MGP-STR tokenizer only handles one sequence.''' ) def _lowerCamelCase ( self ): pass @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' ) def _lowerCamelCase ( self ): pass

"""simple docstring""" import comet # From: unbabel-comet import torch import datasets A = datasets.logging.get_logger(__name__) A = '''\ @inproceedings{rei-EtAl:2020:WMT, author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon}, title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task}, booktitle = {Proceedings of the Fifth Conference on Machine Translation}, month = {November}, year = {2020}, address = {Online}, publisher = {Association for Computational Linguistics}, pages = {909--918}, } @inproceedings{rei-etal-2020-comet, title = "{COMET}: A Neural Framework for {MT} Evaluation", author = "Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.213", pages = "2685--2702", } ''' A = '''\ Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM). With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition. See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information. ''' A = ''' COMET score. Args: `sources` (list of str): Source sentences `predictions` (list of str): candidate translations `references` (list of str): reference translations `cuda` (bool): If set to True, runs COMET using GPU `show_progress` (bool): Shows progress `model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None. Returns: `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`. `scores`: List of scores. Examples: >>> comet_metric = datasets.load_metric(\'comet\') >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."] >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"] >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"] >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source) >>> print([round(v, 2) for v in results["scores"]]) [0.19, 0.92] ''' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class __lowercase ( datasets.Metric ): '''simple docstring''' def _lowerCamelCase ( self ): return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { '''sources''': datasets.Value('''string''' , id='''sequence''' ), '''predictions''': datasets.Value('''string''' , id='''sequence''' ), '''references''': datasets.Value('''string''' , id='''sequence''' ), } ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[ '''https://github.com/Unbabel/COMET''', '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''', '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''', ] , ) def _lowerCamelCase ( self , _UpperCAmelCase ): if self.config_name == "default": __a : List[str] = comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) ) else: __a : List[str] = comet.load_from_checkpoint(comet.download_model(self.config_name ) ) def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=False ): if gpus is None: __a : str = 1 if torch.cuda.is_available() else 0 __a : Optional[Any] = {'''src''': sources, '''mt''': predictions, '''ref''': references} __a : Dict = [dict(zip(_UpperCAmelCase , _UpperCAmelCase ) ) for t in zip(*data.values() )] __a , __a : int = self.scorer.predict(_UpperCAmelCase , gpus=_UpperCAmelCase , progress_bar=_UpperCAmelCase ) return {"mean_score": mean_score, "scores": scores}

from ..utils import DummyObject, requires_backends class UpperCamelCase__ (metaclass=lowerCAmelCase__ ): '''simple docstring''' lowerCamelCase_ : Optional[int] = ["torch", "scipy"] def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]: requires_backends(self , ["torch", "scipy"] ) @classmethod def _lowercase ( cls , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]: requires_backends(cls , ["torch", "scipy"] ) @classmethod def _lowercase ( cls , *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]: requires_backends(cls , ["torch", "scipy"] )

"""simple docstring""" def UpperCAmelCase__ (lowerCAmelCase_ ): '''simple docstring''' if upper_limit < 0: raise ValueError("Limit for the Catalan sequence must be ≥ 0" ) __SCREAMING_SNAKE_CASE = [0] * (upper_limit + 1) # Base case: C(0) = C(1) = 1 __SCREAMING_SNAKE_CASE = 1 if upper_limit > 0: __SCREAMING_SNAKE_CASE = 1 # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i for i in range(2 , upper_limit + 1 ): for j in range(lowerCAmelCase_ ): catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1] return catalan_list if __name__ == "__main__": print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''') print('''\n*** Enter -1 at any time to quit ***''') print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''') try: while True: a__ : List[str] = int(input().strip()) if N < 0: print('''\n********* Goodbye!! ************''') break else: print(F"The Catalan numbers from 0 through {N} are:") print(catalan_numbers(N)) print('''Try another upper limit for the sequence: ''', end='''''') except (NameError, ValueError): print('''\n********* Invalid input, goodbye! ************\n''') import doctest doctest.testmod()

"""simple docstring""" from ...utils import logging from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel from .configuration_mta import MTaConfig lowerCamelCase_ = logging.get_logger(__name__) lowerCamelCase_ = "T5Config" class _SCREAMING_SNAKE_CASE( A ): SCREAMING_SNAKE_CASE_ : int = '''mt5''' SCREAMING_SNAKE_CASE_ : str = MTaConfig class _SCREAMING_SNAKE_CASE( A ): SCREAMING_SNAKE_CASE_ : List[Any] = '''mt5''' SCREAMING_SNAKE_CASE_ : List[Any] = MTaConfig class _SCREAMING_SNAKE_CASE( A ): SCREAMING_SNAKE_CASE_ : Tuple = '''mt5''' SCREAMING_SNAKE_CASE_ : Optional[Any] = MTaConfig

"""simple docstring""" def __lowerCamelCase ( a_ : str ) -> list: return [ txt[:a] + txt[a].upper() + txt[a + 1 :] for a in range(len(a_ ) ) if txt[a].isalpha() ] if __name__ == "__main__": __import__("doctest").testmod()

"""simple docstring""" import importlib import os from dataclasses import dataclass from enum import Enum from typing import Any, Dict, Optional, Union import torch from ..utils import BaseOutput SCREAMING_SNAKE_CASE = "scheduler_config.json" class UpperCAmelCase_ ( A_ ): lowercase__ = 1 lowercase__ = 2 lowercase__ = 3 lowercase__ = 4 lowercase__ = 5 lowercase__ = 6 lowercase__ = 7 lowercase__ = 8 lowercase__ = 9 lowercase__ = 10 lowercase__ = 11 lowercase__ = 12 lowercase__ = 13 lowercase__ = 14 @dataclass class UpperCAmelCase_ ( A_ ): lowercase__ = 42 class UpperCAmelCase_ : lowercase__ = SCHEDULER_CONFIG_NAME lowercase__ = [] lowercase__ = True @classmethod def __magic_name__ ( cls : int , snake_case_ : List[str] = None , snake_case_ : Dict = None , snake_case_ : str=False , **snake_case_ : Optional[int] , ) -> Optional[Any]: '''simple docstring''' A__ = cls.load_config( pretrained_model_name_or_path=SCREAMING_SNAKE_CASE__ , subfolder=SCREAMING_SNAKE_CASE__ , return_unused_kwargs=SCREAMING_SNAKE_CASE__ , return_commit_hash=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , ) return cls.from_config(SCREAMING_SNAKE_CASE__ , return_unused_kwargs=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) def __magic_name__ ( self : Optional[Any] , snake_case_ : List[str] , snake_case_ : Tuple = False , **snake_case_ : Optional[int] ) -> Optional[int]: '''simple docstring''' self.save_config(save_directory=SCREAMING_SNAKE_CASE__ , push_to_hub=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) @property def __magic_name__ ( self : int ) -> Tuple: '''simple docstring''' return self._get_compatibles() @classmethod def __magic_name__ ( cls : Optional[int] ) -> int: '''simple docstring''' A__ = list(set([cls.__name__] + cls._compatibles ) ) A__ = importlib.import_module(__name__.split("." )[0] ) A__ = [ getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for c in compatible_classes_str if hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) ] return compatible_classes

"""simple docstring""" import qiskit def __lowerCamelCase ( a_ : int , a_ : int ) -> qiskit.result.counts.Counts: __SCREAMING_SNAKE_CASE :Tuple = qiskit.Aer.get_backend('''aer_simulator''' ) # Create a Quantum Circuit acting on the q register __SCREAMING_SNAKE_CASE :Union[str, Any] = qiskit.QuantumCircuit(a_ , a_ ) # Apply X (NOT) Gate to Qubits 0 & 1 circuit.x(0 ) circuit.x(1 ) # Map the quantum measurement to the classical bits circuit.measure([0, 1] , [0, 1] ) # Execute the circuit on the qasm simulator __SCREAMING_SNAKE_CASE :Tuple = qiskit.execute(a_ , a_ , shots=10_00 ) # Return the histogram data of the results of the experiment. return job.result().get_counts(a_ ) if __name__ == "__main__": lowerCamelCase_ = single_qubit_measure(2, 2) print(f'Total count for various states are: {counts}')

import argparse import os import re import packaging.version _lowercase : List[str] ="examples/" _lowercase : List[str] ={ "examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"), "init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"), "setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","), "doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"), } _lowercase : Optional[int] ={ "init": "src/diffusers/__init__.py", "setup": "setup.py", } _lowercase : Dict ="README.md" def lowerCAmelCase_ ( _lowercase : Tuple , _lowercase : Optional[int] , _lowercase : List[str]) -> str: """simple docstring""" with open(_lowercase , """r""" , encoding="""utf-8""" , newline="""\n""") as f: a__ : int = f.read() a__ , a__ : Any = REPLACE_PATTERNS[pattern] a__ : str = replace.replace("""VERSION""" , _lowercase) a__ : Optional[int] = re_pattern.sub(_lowercase , _lowercase) with open(_lowercase , """w""" , encoding="""utf-8""" , newline="""\n""") as f: f.write(_lowercase) def lowerCAmelCase_ ( _lowercase : Any) -> Tuple: """simple docstring""" for folder, directories, fnames in os.walk(_lowercase): # Removing some of the folders with non-actively maintained examples from the walk if "research_projects" in directories: directories.remove("""research_projects""") if "legacy" in directories: directories.remove("""legacy""") for fname in fnames: if fname.endswith(""".py"""): update_version_in_file(os.path.join(_lowercase , _lowercase) , _lowercase , pattern="""examples""") def lowerCAmelCase_ ( _lowercase : List[Any] , _lowercase : Dict=False) -> str: """simple docstring""" for pattern, fname in REPLACE_FILES.items(): update_version_in_file(_lowercase , _lowercase , _lowercase) if not patch: update_version_in_examples(_lowercase) def lowerCAmelCase_ ( ) -> Any: """simple docstring""" a__ : Optional[int] = """🤗 Transformers currently provides the following architectures""" a__ : Union[str, Any] = """1. Want to contribute a new model?""" with open(_lowercase , """r""" , encoding="""utf-8""" , newline="""\n""") as f: a__ : List[Any] = f.readlines() # Find the start of the list. a__ : int = 0 while not lines[start_index].startswith(_start_prompt): start_index += 1 start_index += 1 a__ : Union[str, Any] = start_index # Update the lines in the model list. while not lines[index].startswith(_end_prompt): if lines[index].startswith("""1."""): a__ : int = lines[index].replace( """https://huggingface.co./docs/diffusers/main/model_doc""" , """https://huggingface.co./docs/diffusers/model_doc""" , ) index += 1 with open(_lowercase , """w""" , encoding="""utf-8""" , newline="""\n""") as f: f.writelines(_lowercase) def lowerCAmelCase_ ( ) -> Tuple: """simple docstring""" with open(REPLACE_FILES["""init"""] , """r""") as f: a__ : str = f.read() a__ : Tuple = REPLACE_PATTERNS["""init"""][0].search(_lowercase).groups()[0] return packaging.version.parse(_lowercase) def lowerCAmelCase_ ( _lowercase : Optional[int]=False) -> Any: """simple docstring""" a__ : Tuple = get_version() if patch and default_version.is_devrelease: raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""") if default_version.is_devrelease: a__ : List[Any] = default_version.base_version elif patch: a__ : Dict = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}''' else: a__ : Union[str, Any] = F'''{default_version.major}.{default_version.minor + 1}.0''' # Now let's ask nicely if that's the right one. a__ : List[Any] = input(F'''Which version are you releasing? [{default_version}]''') if len(_lowercase) == 0: a__ : Tuple = default_version print(F'''Updating version to {version}.''') global_version_update(_lowercase , patch=_lowercase) def lowerCAmelCase_ ( ) -> str: """simple docstring""" a__ : Any = get_version() a__ : Any = F'''{current_version.major}.{current_version.minor + 1}.0.dev0''' a__ : Tuple = current_version.base_version # Check with the user we got that right. a__ : Dict = input(F'''Which version are we developing now? [{dev_version}]''') if len(_lowercase) == 0: a__ : List[str] = dev_version print(F'''Updating version to {version}.''') global_version_update(_lowercase) # print("Cleaning main README, don't forget to run `make fix-copies`.") # clean_main_ref_in_model_list() if __name__ == "__main__": _lowercase : Any =argparse.ArgumentParser() parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.") parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.") _lowercase : Dict =parser.parse_args() if not args.post_release: pre_release_work(patch=args.patch) elif args.patch: print("Nothing to do after a patch :-)") else: post_release_work()

from __future__ import annotations import math def lowerCAmelCase_ ( _lowercase : float , _lowercase : int) -> float: """simple docstring""" a__ : Union[str, Any] = u for i in range(1 , _lowercase): a__ : Optional[int] = temp * (u - i) return temp def lowerCAmelCase_ ( ) -> None: """simple docstring""" a__ : Tuple = int(input("""enter the numbers of values: """)) a__ : list[list[float]] = [] for _ in range(_lowercase): y.append([]) for i in range(_lowercase): for j in range(_lowercase): y[i].append(_lowercase) a__ : Optional[Any] = 0 print("""enter the values of parameters in a list: """) a__ : List[Any] = list(map(_lowercase , input().split())) print("""enter the values of corresponding parameters: """) for i in range(_lowercase): a__ : Optional[Any] = float(input()) a__ : Tuple = int(input("""enter the value to interpolate: """)) a__ : int = (value - x[0]) / (x[1] - x[0]) # for calculating forward difference table for i in range(1 , _lowercase): for j in range(n - i): a__ : int = y[j + 1][i - 1] - y[j][i - 1] a__ : Optional[int] = y[0][0] for i in range(1 , _lowercase): summ += (ucal(_lowercase , _lowercase) * y[0][i]) / math.factorial(_lowercase) print(F'''the value at {value} is {summ}''') if __name__ == "__main__": main()

"""simple docstring""" import numpy as np from scipy.spatial.distance import cdist from sklearn.metrics import fa_score import datasets __a = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n" __a = "\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n" __a = "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"precision\": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'precision@10': 1.0}\n\n" def A_ ( _lowercase, _lowercase ): '''simple docstring''' return float((preds == labels).mean() ) def A_ ( _lowercase, _lowercase ): '''simple docstring''' snake_case_ :Any = simple_accuracy(_lowercase, _lowercase ) snake_case_ :List[str] = float(fa_score(y_true=_lowercase, y_pred=_lowercase ) ) return { "accuracy": acc, "f1": fa, } def A_ ( _lowercase, _lowercase ): '''simple docstring''' snake_case_ :Union[str, Any] = np.array(_lowercase ) snake_case_ :Tuple = np.array(_lowercase ) snake_case_ :Dict = en_sentvecs.shape[0] # mean centering snake_case_ :int = en_sentvecs - np.mean(_lowercase, axis=0 ) snake_case_ :Dict = in_sentvecs - np.mean(_lowercase, axis=0 ) snake_case_ :int = cdist(_lowercase, _lowercase, """cosine""" ) snake_case_ :Tuple = np.array(range(_lowercase ) ) snake_case_ :Optional[Any] = sim.argsort(axis=1 )[:, :10] snake_case_ :List[str] = np.any(preds == actual[:, None], axis=1 ) return float(matches.mean() ) @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowerCamelCase ( datasets.Metric ): '''simple docstring''' def lowerCAmelCase_ ( self: List[str] ) -> Union[str, Any]: if self.config_name not in [ "wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", "cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", "wiki-ner", ]: raise KeyError( """You should supply a configuration name selected in """ """[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", """ """\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", """ """\"wiki-ner\"]""" ) return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""int64""" ) if self.config_name != """cvit-mkb-clsr""" else datasets.Sequence(datasets.Value("""float32""" ) ), """references""": datasets.Value("""int64""" ) if self.config_name != """cvit-mkb-clsr""" else datasets.Sequence(datasets.Value("""float32""" ) ), } ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" if self.config_name != """cvit-mkb-clsr""" else None , ) def lowerCAmelCase_ ( self: int , snake_case: Tuple , snake_case: Optional[int] ) -> Tuple: if self.config_name == "cvit-mkb-clsr": return {"precision@10": precision_at_aa(snake_case , snake_case )} elif self.config_name in ["wiki-ner"]: return acc_and_fa(snake_case , snake_case ) elif self.config_name in [ "wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md", ]: return {"accuracy": simple_accuracy(snake_case , snake_case )} else: raise KeyError( """You should supply a configuration name selected in """ """[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", """ """\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", """ """\"wiki-ner\"]""" )

"""simple docstring""" import re def A_ ( _lowercase ): '''simple docstring''' snake_case_ :Optional[int] = re.compile( r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" ) return bool(re.search(_lowercase, _lowercase ) ) if __name__ == "__main__": __a = "0094702343221" print(is_sri_lankan_phone_number(phone))

'''simple docstring''' from typing import Any class __magic_name__ : def __init__( self , snake_case) -> Optional[Any]: '''simple docstring''' _UpperCAmelCase : Dict =data _UpperCAmelCase : str =None def __repr__( self) -> str: '''simple docstring''' return f"Node({self.data})" class __magic_name__ : def __init__( self) -> Any: '''simple docstring''' _UpperCAmelCase : str =None def __iter__( self) -> Any: '''simple docstring''' _UpperCAmelCase : Tuple =self.head while node: yield node.data _UpperCAmelCase : List[Any] =node.next def __len__( self) -> int: '''simple docstring''' return sum(1 for _ in self) def __repr__( self) -> str: '''simple docstring''' return "->".join([str(SCREAMING_SNAKE_CASE_) for item in self]) def __getitem__( self , snake_case) -> Any: '''simple docstring''' if not 0 <= index < len(self): raise ValueError('list index out of range.') for i, node in enumerate(self): if i == index: return node return None def __setitem__( self , snake_case , snake_case) -> None: '''simple docstring''' if not 0 <= index < len(self): raise ValueError('list index out of range.') _UpperCAmelCase : List[str] =self.head for _ in range(SCREAMING_SNAKE_CASE_): _UpperCAmelCase : Union[str, Any] =current.next _UpperCAmelCase : Dict =data def lowerCAmelCase ( self , snake_case) -> None: '''simple docstring''' self.insert_nth(len(self) , SCREAMING_SNAKE_CASE_) def lowerCAmelCase ( self , snake_case) -> None: '''simple docstring''' self.insert_nth(0 , SCREAMING_SNAKE_CASE_) def lowerCAmelCase ( self , snake_case , snake_case) -> None: '''simple docstring''' if not 0 <= index <= len(self): raise IndexError('list index out of range') _UpperCAmelCase : Any =Node(SCREAMING_SNAKE_CASE_) if self.head is None: _UpperCAmelCase : Optional[int] =new_node elif index == 0: _UpperCAmelCase : Any =self.head # link new_node to head _UpperCAmelCase : Tuple =new_node else: _UpperCAmelCase : str =self.head for _ in range(index - 1): _UpperCAmelCase : Dict =temp.next _UpperCAmelCase : int =temp.next _UpperCAmelCase : Optional[int] =new_node def lowerCAmelCase ( self) -> None: # print every node data '''simple docstring''' print(self) def lowerCAmelCase ( self) -> Any: '''simple docstring''' return self.delete_nth(0) def lowerCAmelCase ( self) -> Any: # delete from tail '''simple docstring''' return self.delete_nth(len(self) - 1) def lowerCAmelCase ( self , snake_case = 0) -> Any: '''simple docstring''' if not 0 <= index <= len(self) - 1: # test if index is valid raise IndexError('List index out of range.') _UpperCAmelCase : List[str] =self.head # default first node if index == 0: _UpperCAmelCase : int =self.head.next else: _UpperCAmelCase : List[str] =self.head for _ in range(index - 1): _UpperCAmelCase : str =temp.next _UpperCAmelCase : Tuple =temp.next _UpperCAmelCase : Any =temp.next.next return delete_node.data def lowerCAmelCase ( self) -> bool: '''simple docstring''' return self.head is None def lowerCAmelCase ( self) -> None: '''simple docstring''' _UpperCAmelCase : int =None _UpperCAmelCase : Optional[Any] =self.head while current: # Store the current node's next node. _UpperCAmelCase : str =current.next # Make the current node's next point backwards _UpperCAmelCase : List[str] =prev # Make the previous node be the current node _UpperCAmelCase : Tuple =current # Make the current node the next node (to progress iteration) _UpperCAmelCase : Optional[Any] =next_node # Return prev in order to put the head at the end _UpperCAmelCase : List[Any] =prev def lowerCamelCase__ ( ): '''simple docstring''' _UpperCAmelCase : Union[str, Any] =LinkedList() assert linked_list.is_empty() is True assert str(snake_case__ ) == "" try: linked_list.delete_head() raise AssertionError # This should not happen. except IndexError: assert True # This should happen. try: linked_list.delete_tail() raise AssertionError # This should not happen. except IndexError: assert True # This should happen. for i in range(1_0 ): assert len(snake_case__ ) == i linked_list.insert_nth(snake_case__ , i + 1 ) assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(1 , 1_1 ) ) linked_list.insert_head(0 ) linked_list.insert_tail(1_1 ) assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(0 , 1_2 ) ) assert linked_list.delete_head() == 0 assert linked_list.delete_nth(9 ) == 1_0 assert linked_list.delete_tail() == 1_1 assert len(snake_case__ ) == 9 assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(1 , 1_0 ) ) assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True for i in range(0 , 9 ): _UpperCAmelCase : Union[str, Any] =-i assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True linked_list.reverse() assert str(snake_case__ ) == "->".join(str(snake_case__ ) for i in range(-8 , 1 ) ) def lowerCamelCase__ ( ): '''simple docstring''' _UpperCAmelCase : Optional[Any] =[ -9, 1_0_0, Node(7_7_3_4_5_1_1_2 ), 'dlrow olleH', 7, 5_5_5_5, 0, -1_92.5_55_55, 'Hello, world!', 77.9, Node(1_0 ), None, None, 12.20, ] _UpperCAmelCase : Any =LinkedList() for i in test_input: linked_list.insert_tail(snake_case__ ) # Check if it's empty or not assert linked_list.is_empty() is False assert ( str(snake_case__ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->" "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2" ) # Delete the head _UpperCAmelCase : Dict =linked_list.delete_head() assert result == -9 assert ( str(snake_case__ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->" "Hello, world!->77.9->Node(10)->None->None->12.2" ) # Delete the tail _UpperCAmelCase : Any =linked_list.delete_tail() assert result == 12.2 assert ( str(snake_case__ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->" "Hello, world!->77.9->Node(10)->None->None" ) # Delete a node in specific location in linked list _UpperCAmelCase : Tuple =linked_list.delete_nth(1_0 ) assert result is None assert ( str(snake_case__ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->" "Hello, world!->77.9->Node(10)->None" ) # Add a Node instance to its head linked_list.insert_head(Node('Hello again, world!' ) ) assert ( str(snake_case__ ) == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->" "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None" ) # Add None to its tail linked_list.insert_tail(snake_case__ ) assert ( str(snake_case__ ) == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->" "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None" ) # Reverse the linked list linked_list.reverse() assert ( str(snake_case__ ) == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->" "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)" ) def lowerCamelCase__ ( ): '''simple docstring''' from doctest import testmod testmod() _UpperCAmelCase : Union[str, Any] =LinkedList() linked_list.insert_head(input('Inserting 1st at head ' ).strip() ) linked_list.insert_head(input('Inserting 2nd at head ' ).strip() ) print('\nPrint list:' ) linked_list.print_list() linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() ) linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() ) print('\nPrint list:' ) linked_list.print_list() print('\nDelete head' ) linked_list.delete_head() print('Delete tail' ) linked_list.delete_tail() print('\nPrint list:' ) linked_list.print_list() print('\nReverse linked list' ) linked_list.reverse() print('\nPrint list:' ) linked_list.print_list() print('\nString representation of linked list:' ) print(snake_case__ ) print('\nReading/changing Node data using indexing:' ) print(f"Element at Position 1: {linked_list[1]}" ) _UpperCAmelCase : int =input('Enter New Value: ' ).strip() print('New list:' ) print(snake_case__ ) print(f"length of linked_list is : {len(snake_case__ )}" ) if __name__ == "__main__": main()

'''simple docstring''' from __future__ import annotations def lowerCamelCase__ ( __lowerCamelCase : list[int] ): '''simple docstring''' if not nums: return 0 _UpperCAmelCase : Tuple =nums[0] _UpperCAmelCase : int =0 for num in nums[1:]: _UpperCAmelCase , _UpperCAmelCase : Optional[int] =( max_excluding + num, max(__lowerCamelCase , __lowerCamelCase ), ) return max(__lowerCamelCase , __lowerCamelCase ) if __name__ == "__main__": import doctest doctest.testmod()

'''simple docstring''' from __future__ import annotations def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> dict[str, float]: if (voltage, current, resistance).count(0 ) != 1: raise ValueError('''One and only one argument must be 0''' ) if resistance < 0: raise ValueError('''Resistance cannot be negative''' ) if voltage == 0: return {"voltage": float(current * resistance )} elif current == 0: return {"current": voltage / resistance} elif resistance == 0: return {"resistance": voltage / current} else: raise ValueError('''Exactly one argument must be 0''' ) if __name__ == "__main__": import doctest doctest.testmod()

'''simple docstring''' import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST class SCREAMING_SNAKE_CASE: """simple docstring""" def __init__( self : Optional[int] , __snake_case : str , __snake_case : Union[str, Any]=2 , __snake_case : Optional[int]=8 , __snake_case : Any=True , __snake_case : Union[str, Any]=True , __snake_case : Dict=True , __snake_case : int=True , __snake_case : List[Any]=99 , __snake_case : str=16 , __snake_case : Tuple=5 , __snake_case : Tuple=2 , __snake_case : str=36 , __snake_case : Dict="gelu" , __snake_case : str=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Optional[int]=512 , __snake_case : Optional[Any]=16 , __snake_case : int=2 , __snake_case : int=0.02 , __snake_case : str=3 , __snake_case : Dict=4 , __snake_case : str=None , ) -> Optional[int]: UpperCAmelCase : Optional[int] = parent UpperCAmelCase : Tuple = batch_size UpperCAmelCase : List[str] = seq_length UpperCAmelCase : List[Any] = is_training UpperCAmelCase : int = use_input_mask UpperCAmelCase : Any = use_token_type_ids UpperCAmelCase : str = use_labels UpperCAmelCase : Union[str, Any] = vocab_size UpperCAmelCase : List[str] = hidden_size UpperCAmelCase : Optional[Any] = num_hidden_layers UpperCAmelCase : Union[str, Any] = num_attention_heads UpperCAmelCase : Optional[Any] = intermediate_size UpperCAmelCase : Union[str, Any] = hidden_act UpperCAmelCase : int = hidden_dropout_prob UpperCAmelCase : Optional[int] = attention_probs_dropout_prob UpperCAmelCase : Union[str, Any] = max_position_embeddings UpperCAmelCase : str = type_vocab_size UpperCAmelCase : List[str] = type_sequence_label_size UpperCAmelCase : Tuple = initializer_range UpperCAmelCase : Optional[Any] = num_labels UpperCAmelCase : Optional[int] = num_choices UpperCAmelCase : Any = scope def A ( self : Tuple ) -> Union[str, Any]: UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase : Optional[int] = None if self.use_input_mask: UpperCAmelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase : Dict = None if self.use_token_type_ids: UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase : str = None UpperCAmelCase : Tuple = None UpperCAmelCase : int = None if self.use_labels: UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase : List[str] = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def A ( self : int ) -> Tuple: return MraConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , ) def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[Any] = self.get_config() UpperCAmelCase : int = 300 return config def A ( self : Optional[Any] ) -> Any: ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : Tuple = self.prepare_config_and_inputs() UpperCAmelCase : Dict = True UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def A ( self : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] , __snake_case : int , __snake_case : Dict , __snake_case : Tuple , __snake_case : Optional[Any] ) -> List[str]: UpperCAmelCase : int = MraModel(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Tuple = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Optional[int] = model(__snake_case , token_type_ids=__snake_case ) UpperCAmelCase : Dict = model(__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[str] , __snake_case : int , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : Optional[Any] , ) -> Tuple: UpperCAmelCase : str = True UpperCAmelCase : Tuple = MraModel(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , ) UpperCAmelCase : Optional[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , ) UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A ( self : Tuple , __snake_case : str , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : int ) -> Any: UpperCAmelCase : Dict = MraForMaskedLM(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : Optional[int] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def A ( self : Tuple , __snake_case : Tuple , __snake_case : Dict , __snake_case : Dict , __snake_case : Any , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Tuple ) -> Optional[int]: UpperCAmelCase : List[str] = MraForQuestionAnswering(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[Any] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def A ( self : str , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : int , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] ) -> int: UpperCAmelCase : int = self.num_labels UpperCAmelCase : Union[str, Any] = MraForSequenceClassification(__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def A ( self : str , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> int: UpperCAmelCase : Tuple = self.num_labels UpperCAmelCase : List[str] = MraForTokenClassification(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def A ( self : str , __snake_case : int , __snake_case : Any , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : str , __snake_case : Union[str, Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.num_choices UpperCAmelCase : int = MraForMultipleChoice(config=__snake_case ) model.to(__snake_case ) model.eval() UpperCAmelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() UpperCAmelCase : List[str] = model( __snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def A ( self : str ) -> Dict: UpperCAmelCase : Any = self.prepare_config_and_inputs() ( ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ( UpperCAmelCase ) , ) : List[str] = config_and_inputs UpperCAmelCase : Any = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask} return config, inputs_dict @require_torch class SCREAMING_SNAKE_CASE( A__ , unittest.TestCase ): """simple docstring""" lowerCamelCase__ = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = False lowerCamelCase__ = () def A ( self : int ) -> Union[str, Any]: UpperCAmelCase : List[str] = MraModelTester(self ) UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=__snake_case , hidden_size=37 ) def A ( self : Optional[Any] ) -> str: self.config_tester.run_common_tests() def A ( self : Tuple ) -> Optional[Any]: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__snake_case ) def A ( self : List[Any] ) -> Optional[Any]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: UpperCAmelCase : List[Any] = type self.model_tester.create_and_check_model(*__snake_case ) def A ( self : Tuple ) -> Dict: UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*__snake_case ) def A ( self : Tuple ) -> List[str]: UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*__snake_case ) def A ( self : int ) -> Dict: UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*__snake_case ) def A ( self : Dict ) -> Optional[int]: UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*__snake_case ) def A ( self : Any ) -> Optional[int]: UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*__snake_case ) @slow def A ( self : Dict ) -> Any: for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCAmelCase : str = MraModel.from_pretrained(__snake_case ) self.assertIsNotNone(__snake_case ) @unittest.skip(reason='''MRA does not output attentions''' ) def A ( self : str ) -> Optional[Any]: return @require_torch class SCREAMING_SNAKE_CASE( unittest.TestCase ): """simple docstring""" @slow def A ( self : Tuple ) -> List[Any]: UpperCAmelCase : int = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Optional[Any] = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : Optional[Any] = torch.Size((1, 256, 768) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Any = torch.tensor( [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : Optional[Any] ) -> Any: UpperCAmelCase : Optional[int] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' ) UpperCAmelCase : Dict = torch.arange(256 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : List[Any] = model(__snake_case )[0] UpperCAmelCase : int = 50265 UpperCAmelCase : int = torch.Size((1, 256, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Union[str, Any] = torch.tensor( [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) ) @slow def A ( self : str ) -> List[Any]: UpperCAmelCase : List[Any] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' ) UpperCAmelCase : List[Any] = torch.arange(4096 ).unsqueeze(0 ) with torch.no_grad(): UpperCAmelCase : Tuple = model(__snake_case )[0] UpperCAmelCase : Optional[int] = 50265 UpperCAmelCase : Tuple = torch.Size((1, 4096, vocab_size) ) self.assertEqual(output.shape , __snake_case ) UpperCAmelCase : Optional[int] = torch.tensor( [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1E-4 ) )

"""simple docstring""" from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _A = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _A = [ """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""", """FocalNetForImageClassification""", """FocalNetForMaskedImageModeling""", """FocalNetBackbone""", """FocalNetModel""", """FocalNetPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_focalnet import ( FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST, FocalNetBackbone, FocalNetForImageClassification, FocalNetForMaskedImageModeling, FocalNetModel, FocalNetPreTrainedModel, ) else: import sys _A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring""" import unittest from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_tf, require_torch, require_vision, slow, ) from .test_pipelines_common import ANY if is_vision_available(): from PIL import Image else: class _lowerCAmelCase : @staticmethod def lowerCamelCase ( *UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]: '''simple docstring''' pass @is_pipeline_test @require_vision @require_torch class _lowerCAmelCase ( unittest.TestCase ): __UpperCAmelCase : List[Any] = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]: '''simple docstring''' snake_case : List[Any] = pipeline( "zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" ) snake_case : int = [ { "image": "./tests/fixtures/tests_samples/COCO/000000039769.png", "candidate_labels": ["cat", "remote", "couch"], } ] return object_detector, examples def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple: '''simple docstring''' snake_case : str = object_detector(examples[0] , threshold=0.0 ) snake_case : str = len(UpperCamelCase__ ) self.assertGreater(UpperCamelCase__ , 0 ) self.assertEqual( UpperCamelCase__ , [ { "score": ANY(UpperCamelCase__ ), "label": ANY(UpperCamelCase__ ), "box": {"xmin": ANY(UpperCamelCase__ ), "ymin": ANY(UpperCamelCase__ ), "xmax": ANY(UpperCamelCase__ ), "ymax": ANY(UpperCamelCase__ )}, } for i in range(UpperCamelCase__ ) ] , ) @require_tf @unittest.skip("Zero Shot Object Detection not implemented in TF" ) def lowerCamelCase ( self ) -> List[Any]: '''simple docstring''' pass @require_torch def lowerCamelCase ( self ) -> Tuple: '''simple docstring''' snake_case : Dict = pipeline( "zero-shot-object-detection" , model="hf-internal-testing/tiny-random-owlvit-object-detection" ) snake_case : Optional[Any] = object_detector( "./tests/fixtures/tests_samples/COCO/000000039769.png" , candidate_labels=["cat", "remote", "couch"] , threshold=0.64 , ) self.assertEqual( nested_simplify(UpperCamelCase__ , decimals=4 ) , [ {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}}, {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}}, {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}}, {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}}, {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}}, {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}}, {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}}, {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}}, {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}}, ] , ) snake_case : Dict = object_detector( [ { "image": "./tests/fixtures/tests_samples/COCO/000000039769.png", "candidate_labels": ["cat", "remote", "couch"], } ] , threshold=0.64 , ) self.assertEqual( nested_simplify(UpperCamelCase__ , decimals=4 ) , [ [ {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}}, {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}}, {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}}, {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}}, {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}}, {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}}, {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}}, {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}}, {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}}, ] ] , ) @require_torch @slow def lowerCamelCase ( self ) -> str: '''simple docstring''' snake_case : Optional[int] = pipeline("zero-shot-object-detection" ) snake_case : Tuple = object_detector( "http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , ) self.assertEqual( nested_simplify(UpperCamelCase__ , decimals=4 ) , [ {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}}, {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}}, {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}}, {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}}, {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}}, ] , ) snake_case : List[Any] = object_detector( [ { "image": "http://images.cocodataset.org/val2017/000000039769.jpg", "candidate_labels": ["cat", "remote", "couch"], }, { "image": "http://images.cocodataset.org/val2017/000000039769.jpg", "candidate_labels": ["cat", "remote", "couch"], }, ] , ) self.assertEqual( nested_simplify(UpperCamelCase__ , decimals=4 ) , [ [ {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}}, {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}}, {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}}, {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}}, {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}}, ], [ {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}}, {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}}, {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}}, {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}}, {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}}, ], ] , ) @require_tf @unittest.skip("Zero Shot Object Detection not implemented in TF" ) def lowerCamelCase ( self ) -> str: '''simple docstring''' pass @require_torch @slow def lowerCamelCase ( self ) -> List[Any]: '''simple docstring''' snake_case : Optional[Any] = 0.2 snake_case : List[str] = pipeline("zero-shot-object-detection" ) snake_case : List[Any] = object_detector( "http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , threshold=UpperCamelCase__ , ) self.assertEqual( nested_simplify(UpperCamelCase__ , decimals=4 ) , [ {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}}, {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}}, {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}}, ] , ) @require_torch @slow def lowerCamelCase ( self ) -> Optional[int]: '''simple docstring''' snake_case : List[Any] = 2 snake_case : Optional[Any] = pipeline("zero-shot-object-detection" ) snake_case : Optional[int] = object_detector( "http://images.cocodataset.org/val2017/000000039769.jpg" , candidate_labels=["cat", "remote", "couch"] , top_k=UpperCamelCase__ , ) self.assertEqual( nested_simplify(UpperCamelCase__ , decimals=4 ) , [ {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}}, {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}}, ] , )

"""simple docstring""" import warnings from typing import List, Optional, Union from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class SCREAMING_SNAKE_CASE ( a_ ): """simple docstring""" lowercase__ = ["image_processor", "tokenizer"] lowercase__ = "ViltImageProcessor" lowercase__ = ("BertTokenizer", "BertTokenizerFast") def __init__( self : List[Any] ,lowercase_ : int=None ,lowercase_ : int=None ,**lowercase_ : Union[str, Any] ): lowerCAmelCase__ : List[Any] = None if "feature_extractor" in kwargs: warnings.warn( '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`''' ''' instead.''' ,lowercase_ ,) lowerCAmelCase__ : Optional[Any] = kwargs.pop('''feature_extractor''' ) lowerCAmelCase__ : List[str] = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('''You need to specify an `image_processor`.''' ) if tokenizer is None: raise ValueError('''You need to specify a `tokenizer`.''' ) super().__init__(lowercase_ ,lowercase_ ) lowerCAmelCase__ : Optional[Any] = self.image_processor def __call__( self : Tuple ,lowercase_ : Tuple ,lowercase_ : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,lowercase_ : bool = True ,lowercase_ : Union[bool, str, PaddingStrategy] = False ,lowercase_ : Union[bool, str, TruncationStrategy] = None ,lowercase_ : Optional[int] = None ,lowercase_ : int = 0 ,lowercase_ : Optional[int] = None ,lowercase_ : Optional[bool] = None ,lowercase_ : Optional[bool] = None ,lowercase_ : bool = False ,lowercase_ : bool = False ,lowercase_ : bool = False ,lowercase_ : bool = False ,lowercase_ : bool = True ,lowercase_ : Optional[Union[str, TensorType]] = None ,**lowercase_ : List[str] ,): lowerCAmelCase__ : List[Any] = self.tokenizer( text=lowercase_ ,add_special_tokens=lowercase_ ,padding=lowercase_ ,truncation=lowercase_ ,max_length=lowercase_ ,stride=lowercase_ ,pad_to_multiple_of=lowercase_ ,return_token_type_ids=lowercase_ ,return_attention_mask=lowercase_ ,return_overflowing_tokens=lowercase_ ,return_special_tokens_mask=lowercase_ ,return_offsets_mapping=lowercase_ ,return_length=lowercase_ ,verbose=lowercase_ ,return_tensors=lowercase_ ,**lowercase_ ,) # add pixel_values + pixel_mask lowerCAmelCase__ : Optional[Any] = self.image_processor(lowercase_ ,return_tensors=lowercase_ ) encoding.update(lowercase_ ) return encoding def __lowerCAmelCase ( self : Tuple ,*lowercase_ : Any ,**lowercase_ : List[str] ): return self.tokenizer.batch_decode(*lowercase_ ,**lowercase_ ) def __lowerCAmelCase ( self : Tuple ,*lowercase_ : str ,**lowercase_ : Tuple ): return self.tokenizer.decode(*lowercase_ ,**lowercase_ ) @property def __lowerCAmelCase ( self : int ): lowerCAmelCase__ : Tuple = self.tokenizer.model_input_names lowerCAmelCase__ : Optional[Any] = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) @property def __lowerCAmelCase ( self : Union[str, Any] ): warnings.warn( '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,lowercase_ ,) return self.image_processor_class @property def __lowerCAmelCase ( self : int ): warnings.warn( '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,lowercase_ ,) return self.image_processor

'''simple docstring''' a__ : Union[str, Any] = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)] def _UpperCamelCase ( __A ) -> int: '''simple docstring''' UpperCamelCase__ = 0 while number: # Increased Speed Slightly by checking every 5 digits together. sum_of_digits_squared += DIGITS_SQUARED[number % 100000] number //= 100000 return sum_of_digits_squared # There are 2 Chains made, # One ends with 89 with the chain member 58 being the one which when declared first, # there will be the least number of iterations for all the members to be checked. # The other one ends with 1 and has only one element 1. # So 58 and 1 are chosen to be declared at the starting. # Changed dictionary to an array to quicken the solution a__ : list[bool | None] = [None] * 1_0_0_0_0_0_0_0 a__ : Optional[Any] = True a__ : Optional[Any] = False def _UpperCamelCase ( __A ) -> bool: '''simple docstring''' if CHAINS[number - 1] is not None: return CHAINS[number - 1] # type: ignore UpperCamelCase__ = chain(next_number(__A ) ) UpperCamelCase__ = number_chain while number < 10000000: UpperCamelCase__ = number_chain number *= 10 return number_chain def _UpperCamelCase ( __A = 10000000 ) -> int: '''simple docstring''' for i in range(1 , __A ): if CHAINS[i] is None: chain(i + 1 ) return CHAINS[:number].count(__A ) if __name__ == "__main__": import doctest doctest.testmod() print(F"""{solution() = }""")

"""simple docstring""" def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_=False ) -> Any: if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ): A__ = len(set_a.intersection(lowercase_ ) ) if alternative_union: A__ = len(lowercase_ ) + len(lowercase_ ) else: A__ = len(set_a.union(lowercase_ ) ) return intersection / union if isinstance(lowercase_ , (list, tuple) ) and isinstance(lowercase_ , (list, tuple) ): A__ = [element for element in set_a if element in set_b] if alternative_union: A__ = len(lowercase_ ) + len(lowercase_ ) return len(lowercase_ ) / union else: A__ = set_a + [element for element in set_b if element not in set_a] return len(lowercase_ ) / len(lowercase_ ) return len(lowercase_ ) / len(lowercase_ ) return None if __name__ == "__main__": SCREAMING_SNAKE_CASE = {"a", "b", "c", "d", "e"} SCREAMING_SNAKE_CASE = {"c", "d", "e", "f", "h", "i"} print(jaccard_similarity(set_a, set_b))

"""simple docstring""" import random from .binary_exp_mod import bin_exp_mod def _SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=10_00 ) -> Optional[Any]: if n < 2: return False if n % 2 == 0: return n == 2 # this means n is odd A__ = n - 1 A__ = 0 while d % 2 == 0: d /= 2 exp += 1 # n - 1=d*(2**exp) A__ = 0 while count < prec: A__ = random.randint(2 , n - 1 ) A__ = bin_exp_mod(lowercase_ , lowercase_ , lowercase_ ) if b != 1: A__ = True for _ in range(lowercase_ ): if b == n - 1: A__ = False break A__ = b * b b %= n if flag: return False count += 1 return True if __name__ == "__main__": SCREAMING_SNAKE_CASE = abs(int(input("Enter bound : ").strip())) print("Here's the list of primes:") print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))

# Algorithm for the pigeonhole sorting def _UpperCAmelCase ( a__): '''simple docstring''' a_ : List[Any] = min(a__) # min() finds the minimum value a_ : List[str] = max(a__) # max() finds the maximum value a_ : str = max_val - min_val + 1 # size is difference of max and min values plus one # list of pigeonholes of size equal to the variable size a_ : Any = [0] * size # Populate the pigeonholes. for x in a: assert isinstance(a__ , a__), "integers only please" holes[x - min_val] += 1 # Putting the elements back into the array in an order. a_ : Tuple = 0 for count in range(a__): while holes[count] > 0: holes[count] -= 1 a_ : Optional[Any] = count + min_val i += 1 def _UpperCAmelCase ( ): '''simple docstring''' a_ : List[Any] = [8, 3, 2, 7, 4, 6, 8] pigeonhole_sort(a__) print("""Sorted order is:""" , """ """.join(a__)) if __name__ == "__main__": main()

from __future__ import annotations def _UpperCAmelCase ( a__): '''simple docstring''' a_ : List[str] = str(a__) return len(a__) == 9 and set(a__) == set("""123456789""") def _UpperCAmelCase ( ): '''simple docstring''' for base_num in range(9_9_9_9 , 4_9_9_9 , -1): a_ : Dict = 1_0_0_0_0_2 * base_num if is_9_pandigital(a__): return candidate for base_num in range(3_3_3 , 9_9 , -1): a_ : Tuple = 1_0_0_2_0_0_3 * base_num if is_9_pandigital(a__): return candidate return None if __name__ == "__main__": print(F"""{solution() = }""")

import os from dataclasses import dataclass, field from io import BytesIO from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union import numpy as np import pyarrow as pa from .. import config from ..download.streaming_download_manager import xopen, xsplitext from ..table import array_cast from ..utils.py_utils import no_op_if_value_is_null, string_to_dict if TYPE_CHECKING: from .features import FeatureType lowercase_ ,lowercase_ ,lowercase_ = False, False, False @dataclass class A_ : '''simple docstring''' __snake_case = None __snake_case = True __snake_case = True __snake_case = None # Automatically constructed __snake_case = "dict" __snake_case = pa.struct({"""bytes""": pa.binary(), """path""": pa.string()} ) __snake_case = field(default="""Audio""" , init=__UpperCamelCase , repr=__UpperCamelCase ) def __call__( self: int ): return self.pa_type def _snake_case ( self: Tuple , a: Union[str, bytes, dict] ): try: import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files. except ImportError as err: raise ImportError('To support encoding audio data, please install \'soundfile\'.' ) from err if isinstance(a , a ): return {"bytes": None, "path": value} elif isinstance(a , a ): return {"bytes": value, "path": None} elif "array" in value: # convert the audio array to wav bytes __lowerCamelCase : List[str] = BytesIO() sf.write(a , value['array'] , value['sampling_rate'] , format='wav' ) return {"bytes": buffer.getvalue(), "path": None} elif value.get('path' ) is not None and os.path.isfile(value['path'] ): # we set "bytes": None to not duplicate the data if they're already available locally if value["path"].endswith('pcm' ): # "PCM" only has raw audio bytes if value.get('sampling_rate' ) is None: # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object' ) if value.get('bytes' ): # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!) __lowerCamelCase : int = np.frombuffer(value['bytes'] , dtype=np.intaa ).astype(np.floataa ) / 3_2767 else: __lowerCamelCase : Dict = np.memmap(value['path'] , dtype='h' , mode='r' ).astype(np.floataa ) / 3_2767 __lowerCamelCase : Dict = BytesIO(bytes() ) sf.write(a , a , value['sampling_rate'] , format='wav' ) return {"bytes": buffer.getvalue(), "path": None} else: return {"bytes": None, "path": value.get('path' )} elif value.get('bytes' ) is not None or value.get('path' ) is not None: # store the audio bytes, and path is used to infer the audio format using the file extension return {"bytes": value.get('bytes' ), "path": value.get('path' )} else: raise ValueError( F'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.' ) def _snake_case ( self: Tuple , a: dict , a: Optional[Dict[str, Union[str, bool, None]]] = None ): if not self.decode: raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.' ) __lowerCamelCase , __lowerCamelCase : Optional[int] = (value['path'], BytesIO(value['bytes'] )) if value['bytes'] is not None else (value['path'], None) if path is None and file is None: raise ValueError(F'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.' ) try: import librosa import soundfile as sf except ImportError as err: raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.' ) from err __lowerCamelCase : Dict = xsplitext(a )[1][1:].lower() if path is not None else None if not config.IS_OPUS_SUPPORTED and audio_format == "opus": raise RuntimeError( 'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, ' 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ' ) elif not config.IS_MP3_SUPPORTED and audio_format == "mp3": raise RuntimeError( 'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, ' 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ' ) if file is None: __lowerCamelCase : Union[str, Any] = token_per_repo_id or {} __lowerCamelCase : str = path.split('::' )[-1] try: __lowerCamelCase : Optional[Any] = string_to_dict(a , config.HUB_DATASETS_URL )['repo_id'] __lowerCamelCase : List[str] = token_per_repo_id[repo_id] except (ValueError, KeyError): __lowerCamelCase : Optional[int] = None with xopen(a , 'rb' , use_auth_token=a ) as f: __lowerCamelCase , __lowerCamelCase : Optional[int] = sf.read(a ) else: __lowerCamelCase , __lowerCamelCase : str = sf.read(a ) __lowerCamelCase : Any = array.T if self.mono: __lowerCamelCase : List[str] = librosa.to_mono(a ) if self.sampling_rate and self.sampling_rate != sampling_rate: __lowerCamelCase : List[Any] = librosa.resample(a , orig_sr=a , target_sr=self.sampling_rate ) __lowerCamelCase : Optional[Any] = self.sampling_rate return {"path": path, "array": array, "sampling_rate": sampling_rate} def _snake_case ( self: Tuple ): from .features import Value if self.decode: raise ValueError('Cannot flatten a decoded Audio feature.' ) return { "bytes": Value('binary' ), "path": Value('string' ), } def _snake_case ( self: Optional[Any] , a: Union[pa.StringArray, pa.StructArray] ): if pa.types.is_string(storage.type ): __lowerCamelCase : Dict = pa.array([None] * len(a ) , type=pa.binary() ) __lowerCamelCase : List[str] = pa.StructArray.from_arrays([bytes_array, storage] , ['bytes', 'path'] , mask=storage.is_null() ) elif pa.types.is_binary(storage.type ): __lowerCamelCase : str = pa.array([None] * len(a ) , type=pa.string() ) __lowerCamelCase : Any = pa.StructArray.from_arrays([storage, path_array] , ['bytes', 'path'] , mask=storage.is_null() ) elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('array' ): __lowerCamelCase : Union[str, Any] = pa.array([Audio().encode_example(a ) if x is not None else None for x in storage.to_pylist()] ) elif pa.types.is_struct(storage.type ): if storage.type.get_field_index('bytes' ) >= 0: __lowerCamelCase : Optional[Any] = storage.field('bytes' ) else: __lowerCamelCase : List[str] = pa.array([None] * len(a ) , type=pa.binary() ) if storage.type.get_field_index('path' ) >= 0: __lowerCamelCase : List[str] = storage.field('path' ) else: __lowerCamelCase : Optional[Any] = pa.array([None] * len(a ) , type=pa.string() ) __lowerCamelCase : Optional[Any] = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=storage.is_null() ) return array_cast(a , self.pa_type ) def _snake_case ( self: Optional[Any] , a: pa.StructArray ): @no_op_if_value_is_null def path_to_bytes(a: Any ): with xopen(a , 'rb' ) as f: __lowerCamelCase : List[str] = f.read() return bytes_ __lowerCamelCase : Dict = pa.array( [ (path_to_bytes(x['path'] ) if x['bytes'] is None else x['bytes']) if x is not None else None for x in storage.to_pylist() ] , type=pa.binary() , ) __lowerCamelCase : List[Any] = pa.array( [os.path.basename(a ) if path is not None else None for path in storage.field('path' ).to_pylist()] , type=pa.string() , ) __lowerCamelCase : Optional[Any] = pa.StructArray.from_arrays([bytes_array, path_array] , ['bytes', 'path'] , mask=bytes_array.is_null() ) return array_cast(a , self.pa_type )

import argparse import json import os import torch from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer from transformers.tokenization_utils_base import AddedToken @torch.no_grad() def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): # Load configuration defined in the metadata file with open(SCREAMING_SNAKE_CASE__ ) as metadata_file: __lowerCamelCase : List[str] = json.load(SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : int = LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE__ , **metadata['model_config'] ) # Load in the weights from the checkpoint_path __lowerCamelCase : Union[str, Any] = torch.load(SCREAMING_SNAKE_CASE__ , map_location='cpu' ) # Load the entity vocab file __lowerCamelCase : List[Any] = load_entity_vocab(SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : Optional[int] = RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] ) # Add special tokens to the token vocabulary for downstream tasks __lowerCamelCase : str = AddedToken('<ent>' , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : Dict = AddedToken('<ent2>' , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} ) config.vocab_size += 2 print(f'Saving tokenizer to {pytorch_dump_folder_path}' ) tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ ) with open(os.path.join(SCREAMING_SNAKE_CASE__ , LukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f: json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) __lowerCamelCase : str = LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ ) # Initialize the embeddings of the special tokens __lowerCamelCase : Union[str, Any] = state_dict['embeddings.word_embeddings.weight'] __lowerCamelCase : Tuple = word_emb[tokenizer.convert_tokens_to_ids(['@'] )[0]].unsqueeze(0 ) __lowerCamelCase : Any = word_emb[tokenizer.convert_tokens_to_ids(['#'] )[0]].unsqueeze(0 ) __lowerCamelCase : List[str] = torch.cat([word_emb, ent_emb, enta_emb] ) # Initialize the query layers of the entity-aware self-attention mechanism for layer_index in range(config.num_hidden_layers ): for matrix_name in ["query.weight", "query.bias"]: __lowerCamelCase : Optional[int] = f'encoder.layer.{layer_index}.attention.self.' __lowerCamelCase : Dict = state_dict[prefix + matrix_name] __lowerCamelCase : List[Any] = state_dict[prefix + matrix_name] __lowerCamelCase : Union[str, Any] = state_dict[prefix + matrix_name] # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks __lowerCamelCase : Optional[int] = state_dict['entity_embeddings.entity_embeddings.weight'] __lowerCamelCase : Union[str, Any] = entity_emb[entity_vocab['[MASK]']] __lowerCamelCase : Optional[Any] = LukeModel(config=SCREAMING_SNAKE_CASE__ ).eval() __lowerCamelCase , __lowerCamelCase : List[Any] = model.load_state_dict(SCREAMING_SNAKE_CASE__ , strict=SCREAMING_SNAKE_CASE__ ) if not (len(SCREAMING_SNAKE_CASE__ ) == 1 and missing_keys[0] == "embeddings.position_ids"): raise ValueError(f'Missing keys {", ".join(SCREAMING_SNAKE_CASE__ )}. Expected only missing embeddings.position_ids' ) if not (all(key.startswith('entity_predictions' ) or key.startswith('lm_head' ) for key in unexpected_keys )): raise ValueError( 'Unexpected keys' f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}' ) # Check outputs __lowerCamelCase : Optional[Any] = LukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ , task='entity_classification' ) __lowerCamelCase : Dict = ( 'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the' ' new world number one avoid a humiliating second- round exit at Wimbledon .' ) __lowerCamelCase : Union[str, Any] = (39, 42) __lowerCamelCase : Optional[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , entity_spans=[span] , add_prefix_space=SCREAMING_SNAKE_CASE__ , return_tensors='pt' ) __lowerCamelCase : List[str] = model(**SCREAMING_SNAKE_CASE__ ) # Verify word hidden states if model_size == "large": __lowerCamelCase : Dict = torch.Size((1, 42, 1_024) ) __lowerCamelCase : int = torch.tensor( [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]] ) else: # base __lowerCamelCase : Union[str, Any] = torch.Size((1, 42, 768) ) __lowerCamelCase : Tuple = torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]] ) if not (outputs.last_hidden_state.shape == expected_shape): raise ValueError( f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' ) if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ): raise ValueError # Verify entity hidden states if model_size == "large": __lowerCamelCase : Union[str, Any] = torch.Size((1, 1, 1_024) ) __lowerCamelCase : Dict = torch.tensor([[0.0_466, -0.0_106, -0.0_179]] ) else: # base __lowerCamelCase : int = torch.Size((1, 1, 768) ) __lowerCamelCase : Dict = torch.tensor([[0.1_457, 0.1_044, 0.0_174]] ) if not (outputs.entity_last_hidden_state.shape != expected_shape): raise ValueError( f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is' f' {expected_shape}' ) if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ): raise ValueError # Finally, save our PyTorch model and tokenizer print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__ ) ) model.save_pretrained(SCREAMING_SNAKE_CASE__ ) def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ): __lowerCamelCase : Tuple = {} with open(SCREAMING_SNAKE_CASE__ , 'r' , encoding='utf-8' ) as f: for index, line in enumerate(SCREAMING_SNAKE_CASE__ ): __lowerCamelCase , __lowerCamelCase : List[Any] = line.rstrip().split('\t' ) __lowerCamelCase : Any = index return entity_vocab if __name__ == "__main__": lowercase_ = argparse.ArgumentParser() # Required parameters parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.') parser.add_argument( '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.' ) parser.add_argument( '--entity_vocab_path', default=None, type=str, help='Path to an entity_vocab.tsv file, containing the entity vocabulary.', ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.' ) parser.add_argument( '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.' ) lowercase_ = parser.parse_args() convert_luke_checkpoint( args.checkpoint_path, args.metadata_path, args.entity_vocab_path, args.pytorch_dump_folder_path, args.model_size, )

"""simple docstring""" import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": __lowercase = argparse.ArgumentParser( description=( """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned""" """ Distillation""" ) ) parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""]) parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str) parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str) parser.add_argument("""--vocab_transform""", action="""store_true""") __lowercase = parser.parse_args() if args.model_type == "bert": __lowercase = BertForMaskedLM.from_pretrained(args.model_name) __lowercase = """bert""" else: raise ValueError("""args.model_type should be \"bert\".""") __lowercase = model.state_dict() __lowercase = {} for w in ["word_embeddings", "position_embeddings"]: __lowercase = state_dict[f'''{prefix}.embeddings.{w}.weight'''] for w in ["weight", "bias"]: __lowercase = state_dict[f'''{prefix}.embeddings.LayerNorm.{w}'''] __lowercase = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}''' ] __lowercase = state_dict[ f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}''' ] std_idx += 1 __lowercase = state_dict["""cls.predictions.decoder.weight"""] __lowercase = state_dict["""cls.predictions.bias"""] if args.vocab_transform: for w in ["weight", "bias"]: __lowercase = state_dict[f'''cls.predictions.transform.dense.{w}'''] __lowercase = state_dict[f'''cls.predictions.transform.LayerNorm.{w}'''] print(f'''N layers selected for distillation: {std_idx}''') print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''') print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''') torch.save(compressed_sd, args.dump_checkpoint)

'''simple docstring''' __SCREAMING_SNAKE_CASE :List[str] = '''0.18.2''' from .configuration_utils import ConfigMixin from .utils import ( OptionalDependencyNotAvailable, is_flax_available, is_inflect_available, is_invisible_watermark_available, is_k_diffusion_available, is_k_diffusion_version, is_librosa_available, is_note_seq_available, is_onnx_available, is_scipy_available, is_torch_available, is_torchsde_available, is_transformers_available, is_transformers_version, is_unidecode_available, logging, ) try: if not is_onnx_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_onnx_objects import * # noqa F403 else: from .pipelines import OnnxRuntimeModel try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * # noqa F403 else: from .models import ( AutoencoderKL, ControlNetModel, ModelMixin, PriorTransformer, TaFilmDecoder, TransformeraDModel, UNetaDModel, UNetaDConditionModel, UNetaDModel, UNetaDConditionModel, VQModel, ) from .optimization import ( get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pipelines import ( AudioPipelineOutput, ConsistencyModelPipeline, DanceDiffusionPipeline, DDIMPipeline, DDPMPipeline, DiffusionPipeline, DiTPipeline, ImagePipelineOutput, KarrasVePipeline, LDMPipeline, LDMSuperResolutionPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline, ) from .schedulers import ( CMStochasticIterativeScheduler, DDIMInverseScheduler, DDIMParallelScheduler, DDIMScheduler, DDPMParallelScheduler, DDPMScheduler, DEISMultistepScheduler, DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, IPNDMScheduler, KarrasVeScheduler, KDPMaAncestralDiscreteScheduler, KDPMaDiscreteScheduler, PNDMScheduler, RePaintScheduler, SchedulerMixin, ScoreSdeVeScheduler, UnCLIPScheduler, UniPCMultistepScheduler, VQDiffusionScheduler, ) from .training_utils import EMAModel try: if not (is_torch_available() and is_scipy_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_scipy_objects import * # noqa F403 else: from .schedulers import LMSDiscreteScheduler try: if not (is_torch_available() and is_torchsde_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_torchsde_objects import * # noqa F403 else: from .schedulers import DPMSolverSDEScheduler try: if not (is_torch_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipelines import ( AltDiffusionImgaImgPipeline, AltDiffusionPipeline, AudioLDMPipeline, CycleDiffusionPipeline, IFImgaImgPipeline, IFImgaImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline, ImageTextPipelineOutput, KandinskyImgaImgPipeline, KandinskyInpaintPipeline, KandinskyPipeline, KandinskyPriorPipeline, KandinskyVaaControlnetImgaImgPipeline, KandinskyVaaControlnetPipeline, KandinskyVaaImgaImgPipeline, KandinskyVaaInpaintPipeline, KandinskyVaaPipeline, KandinskyVaaPriorEmbaEmbPipeline, KandinskyVaaPriorPipeline, LDMTextToImagePipeline, PaintByExamplePipeline, SemanticStableDiffusionPipeline, ShapEImgaImgPipeline, ShapEPipeline, StableDiffusionAttendAndExcitePipeline, StableDiffusionControlNetImgaImgPipeline, StableDiffusionControlNetInpaintPipeline, StableDiffusionControlNetPipeline, StableDiffusionDepthaImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionImageVariationPipeline, StableDiffusionImgaImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionInstructPixaPixPipeline, StableDiffusionLatentUpscalePipeline, StableDiffusionLDMaDPipeline, StableDiffusionModelEditingPipeline, StableDiffusionPanoramaPipeline, StableDiffusionParadigmsPipeline, StableDiffusionPipeline, StableDiffusionPipelineSafe, StableDiffusionPixaPixZeroPipeline, StableDiffusionSAGPipeline, StableDiffusionUpscalePipeline, StableUnCLIPImgaImgPipeline, StableUnCLIPPipeline, TextToVideoSDPipeline, TextToVideoZeroPipeline, UnCLIPImageVariationPipeline, UnCLIPPipeline, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder, VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, VideoToVideoSDPipeline, VQDiffusionPipeline, ) try: if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403 else: from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline try: if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .pipelines import StableDiffusionKDiffusionPipeline try: if not (is_torch_available() and is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403 else: from .pipelines import ( OnnxStableDiffusionImgaImgPipeline, OnnxStableDiffusionInpaintPipeline, OnnxStableDiffusionInpaintPipelineLegacy, OnnxStableDiffusionPipeline, OnnxStableDiffusionUpscalePipeline, StableDiffusionOnnxPipeline, ) try: if not (is_torch_available() and is_librosa_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_librosa_objects import * # noqa F403 else: from .pipelines import AudioDiffusionPipeline, Mel try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .pipelines import SpectrogramDiffusionPipeline try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_objects import * # noqa F403 else: from .models.controlnet_flax import FlaxControlNetModel from .models.modeling_flax_utils import FlaxModelMixin from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel from .models.vae_flax import FlaxAutoencoderKL from .pipelines import FlaxDiffusionPipeline from .schedulers import ( FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxDPMSolverMultistepScheduler, FlaxKarrasVeScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, FlaxSchedulerMixin, FlaxScoreSdeVeScheduler, ) try: if not (is_flax_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_and_transformers_objects import * # noqa F403 else: from .pipelines import ( FlaxStableDiffusionControlNetPipeline, FlaxStableDiffusionImgaImgPipeline, FlaxStableDiffusionInpaintPipeline, FlaxStableDiffusionPipeline, ) try: if not (is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_note_seq_objects import * # noqa F403 else: from .pipelines import MidiProcessor

"""simple docstring""" import timeit import numpy as np import datasets from datasets.arrow_writer import ArrowWriter from datasets.features.features import _ArrayXD def _snake_case ( lowercase__ ): def wrapper(*lowercase__ , **lowercase__ ): _lowerCamelCase : Tuple = timeit.default_timer() _lowerCamelCase : Tuple = func(*lowercase__ , **lowercase__ ) _lowerCamelCase : str = timeit.default_timer() - starttime return delta _lowerCamelCase : List[Any] = func.__name__ return wrapper def _snake_case ( lowercase__ , lowercase__=100 , lowercase__=None ): _lowerCamelCase : Dict = [] _lowerCamelCase : List[str] = seq_shapes or {} for i in range(lowercase__ ): _lowerCamelCase : Optional[int] = {} for col_id, (k, v) in enumerate(features.items() ): if isinstance(lowercase__ , _ArrayXD ): _lowerCamelCase : List[str] = np.random.rand(*v.shape ).astype(v.dtype ) elif isinstance(lowercase__ , datasets.Value ): if v.dtype == "string": _lowerCamelCase : Any = 'The small grey turtle was surprisingly fast when challenged.' else: _lowerCamelCase : List[str] = np.random.randint(10 , size=1 ).astype(v.dtype ).item() elif isinstance(lowercase__ , datasets.Sequence ): while isinstance(lowercase__ , datasets.Sequence ): _lowerCamelCase : Tuple = v.feature _lowerCamelCase : List[Any] = seq_shapes[k] _lowerCamelCase : int = np.random.rand(*lowercase__ ).astype(v.dtype ) _lowerCamelCase : List[str] = data dummy_data.append((i, example) ) return dummy_data def _snake_case ( lowercase__ , lowercase__ , lowercase__=100 , lowercase__=None ): _lowerCamelCase : List[str] = generate_examples(lowercase__ , num_examples=lowercase__ , seq_shapes=lowercase__ ) with ArrowWriter(features=lowercase__ , path=lowercase__ ) as writer: for key, record in dummy_data: _lowerCamelCase : str = features.encode_example(lowercase__ ) writer.write(lowercase__ ) _lowerCamelCase, _lowerCamelCase : Optional[Any] = writer.finalize() if not num_final_examples == num_examples: raise ValueError( f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' ) _lowerCamelCase : Optional[int] = datasets.Dataset.from_file(filename=lowercase__ , info=datasets.DatasetInfo(features=lowercase__ ) ) return dataset

"""simple docstring""" import torch from diffusers import UnCLIPScheduler from .test_schedulers import SchedulerCommonTest class lowerCAmelCase__ ( lowercase ): '''simple docstring''' lowerCamelCase__ = (UnCLIPScheduler,) def A_ ( self , **lowercase ): _lowerCamelCase : Any = { 'num_train_timesteps': 1000, 'variance_type': 'fixed_small_log', 'clip_sample': True, 'clip_sample_range': 1.0, 'prediction_type': 'epsilon', } config.update(**lowercase ) return config def A_ ( self ): for timesteps in [1, 5, 100, 1000]: self.check_over_configs(num_train_timesteps=lowercase ) def A_ ( self ): for variance in ["fixed_small_log", "learned_range"]: self.check_over_configs(variance_type=lowercase ) def A_ ( self ): for clip_sample in [True, False]: self.check_over_configs(clip_sample=lowercase ) def A_ ( self ): for clip_sample_range in [1, 5, 10, 20]: self.check_over_configs(clip_sample_range=lowercase ) def A_ ( self ): for prediction_type in ["epsilon", "sample"]: self.check_over_configs(prediction_type=lowercase ) def A_ ( self ): for time_step in [0, 500, 999]: for prev_timestep in [None, 5, 100, 250, 500, 750]: if prev_timestep is not None and prev_timestep >= time_step: continue self.check_over_forward(time_step=lowercase , prev_timestep=lowercase ) def A_ ( self ): _lowerCamelCase : Optional[Any] = self.scheduler_classes[0] _lowerCamelCase : Optional[int] = self.get_scheduler_config(variance_type='fixed_small_log' ) _lowerCamelCase : str = scheduler_class(**lowercase ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0000E-10 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_54_96_25 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_99_49_87 ) ) < 1E-5 def A_ ( self ): _lowerCamelCase : List[str] = self.scheduler_classes[0] _lowerCamelCase : Optional[Any] = self.get_scheduler_config(variance_type='learned_range' ) _lowerCamelCase : int = scheduler_class(**lowercase ) _lowerCamelCase : List[str] = 0.5 assert scheduler._get_variance(1 , predicted_variance=lowercase ) - -10.1_71_27_90 < 1E-5 assert scheduler._get_variance(487 , predicted_variance=lowercase ) - -5.7_99_80_52 < 1E-5 assert scheduler._get_variance(999 , predicted_variance=lowercase ) - -0.0_01_00_11 < 1E-5 def A_ ( self ): _lowerCamelCase : List[Any] = self.scheduler_classes[0] _lowerCamelCase : Optional[Any] = self.get_scheduler_config() _lowerCamelCase : Tuple = scheduler_class(**lowercase ) _lowerCamelCase : Union[str, Any] = scheduler.timesteps _lowerCamelCase : Any = self.dummy_model() _lowerCamelCase : Optional[Any] = self.dummy_sample_deter _lowerCamelCase : Optional[int] = torch.manual_seed(0 ) for i, t in enumerate(lowercase ): # 1. predict noise residual _lowerCamelCase : Tuple = model(lowercase , lowercase ) # 2. predict previous mean of sample x_t-1 _lowerCamelCase : List[Any] = scheduler.step(lowercase , lowercase , lowercase , generator=lowercase ).prev_sample _lowerCamelCase : Optional[int] = pred_prev_sample _lowerCamelCase : Optional[Any] = torch.sum(torch.abs(lowercase ) ) _lowerCamelCase : List[Any] = torch.mean(torch.abs(lowercase ) ) assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1E-2 assert abs(result_mean.item() - 0.3_28_47_43 ) < 1E-3 def A_ ( self ): _lowerCamelCase : Tuple = self.scheduler_classes[0] _lowerCamelCase : str = self.get_scheduler_config() _lowerCamelCase : Optional[Any] = scheduler_class(**lowercase ) scheduler.set_timesteps(25 ) _lowerCamelCase : Optional[Any] = scheduler.timesteps _lowerCamelCase : Optional[int] = self.dummy_model() _lowerCamelCase : Any = self.dummy_sample_deter _lowerCamelCase : str = torch.manual_seed(0 ) for i, t in enumerate(lowercase ): # 1. predict noise residual _lowerCamelCase : List[Any] = model(lowercase , lowercase ) if i + 1 == timesteps.shape[0]: _lowerCamelCase : Optional[int] = None else: _lowerCamelCase : List[str] = timesteps[i + 1] # 2. predict previous mean of sample x_t-1 _lowerCamelCase : Union[str, Any] = scheduler.step( lowercase , lowercase , lowercase , prev_timestep=lowercase , generator=lowercase ).prev_sample _lowerCamelCase : List[Any] = pred_prev_sample _lowerCamelCase : Optional[Any] = torch.sum(torch.abs(lowercase ) ) _lowerCamelCase : List[str] = torch.mean(torch.abs(lowercase ) ) assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1E-2 assert abs(result_mean.item() - 0.3_36_20_38 ) < 1E-3 def A_ ( self ): pass def A_ ( self ): pass

"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_torch, require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import MgpstrProcessor, ViTImageProcessor @require_torch @require_vision class _A ( unittest.TestCase ): snake_case__ : List[str] = ViTImageProcessor if is_vision_available() else None @property def A__ ( self ): """simple docstring""" return self.image_processor_tester.prepare_image_processor_dict() def A__ ( self ): """simple docstring""" lowercase = (3, 32, 128) lowercase = tempfile.mkdtemp() # fmt: off lowercase = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""] # fmt: on lowercase = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) ) lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(__lowerCAmelCase ) + """\n""" ) lowercase = { """do_normalize""": False, """do_resize""": True, """image_processor_type""": """ViTImageProcessor""", """resample""": 3, """size""": {"""height""": 32, """width""": 128}, } lowercase = os.path.join(self.tmpdirname , __lowerCAmelCase ) with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp: json.dump(__lowerCAmelCase , __lowerCAmelCase ) def A__ ( self , **__lowerCAmelCase ): """simple docstring""" return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase ) def A__ ( self , **__lowerCAmelCase ): """simple docstring""" return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowerCAmelCase ) def A__ ( self ): """simple docstring""" shutil.rmtree(self.tmpdirname ) def A__ ( self ): """simple docstring""" lowercase = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta ) lowercase = Image.fromarray(np.moveaxis(__lowerCAmelCase , 0 , -1 ) ) return image_input def A__ ( self ): """simple docstring""" lowercase = self.get_tokenizer() lowercase = self.get_image_processor() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) processor.save_pretrained(self.tmpdirname ) lowercase = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=__lowerCAmelCase ) self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() ) self.assertIsInstance(processor.char_tokenizer , __lowerCAmelCase ) self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor.image_processor , __lowerCAmelCase ) def A__ ( self ): """simple docstring""" lowercase = self.get_tokenizer() lowercase = self.get_image_processor() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) processor.save_pretrained(self.tmpdirname ) lowercase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) lowercase = self.get_image_processor(do_normalize=__lowerCAmelCase , padding_value=1.0 ) lowercase = MgpstrProcessor.from_pretrained( self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__lowerCAmelCase , padding_value=1.0 ) self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.char_tokenizer , __lowerCAmelCase ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , __lowerCAmelCase ) def A__ ( self ): """simple docstring""" lowercase = self.get_image_processor() lowercase = self.get_tokenizer() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) lowercase = self.prepare_image_inputs() lowercase = image_processor(__lowerCAmelCase , return_tensors="""np""" ) lowercase = processor(images=__lowerCAmelCase , return_tensors="""np""" ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 ) def A__ ( self ): """simple docstring""" lowercase = self.get_image_processor() lowercase = self.get_tokenizer() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) lowercase = """test""" lowercase = processor(text=__lowerCAmelCase ) lowercase = tokenizer(__lowerCAmelCase ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def A__ ( self ): """simple docstring""" lowercase = self.get_image_processor() lowercase = self.get_tokenizer() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) lowercase = """test""" lowercase = self.prepare_image_inputs() lowercase = processor(text=__lowerCAmelCase , images=__lowerCAmelCase ) self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] ) # test if it raises when no input is passed with pytest.raises(__lowerCAmelCase ): processor() def A__ ( self ): """simple docstring""" lowercase = self.get_image_processor() lowercase = self.get_tokenizer() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) lowercase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]] lowercase = processor.char_decode(__lowerCAmelCase ) lowercase = tokenizer.batch_decode(__lowerCAmelCase ) lowercase = [seq.replace(""" """ , """""" ) for seq in decoded_tok] self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase ) def A__ ( self ): """simple docstring""" lowercase = self.get_image_processor() lowercase = self.get_tokenizer() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) lowercase = None lowercase = self.prepare_image_inputs() lowercase = processor(text=__lowerCAmelCase , images=__lowerCAmelCase ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) def A__ ( self ): """simple docstring""" lowercase = self.get_image_processor() lowercase = self.get_tokenizer() lowercase = MgpstrProcessor(tokenizer=__lowerCAmelCase , image_processor=__lowerCAmelCase ) lowercase = torch.randn(1 , 27 , 38 ) lowercase = torch.randn(1 , 27 , 5_0257 ) lowercase = torch.randn(1 , 27 , 3_0522 ) lowercase = processor.batch_decode([char_input, bpe_input, wp_input] ) self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )

"""simple docstring""" def UpperCAmelCase__ ( lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[Any] ) -> int: '''simple docstring''' lowercase = [0 for i in range(r + 1 )] # nc0 = 1 lowercase = 1 for i in range(1 , n + 1 ): # to compute current row from previous row. lowercase = min(lowerCAmelCase__ , lowerCAmelCase__ ) while j > 0: c[j] += c[j - 1] j -= 1 return c[r] print(binomial_coefficient(n=1_0, r=5))

from dataclasses import dataclass from typing import Optional, Tuple import torch from torch import nn from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel from transformers.utils import ModelOutput @dataclass class lowercase ( a ): lowercase__ : Optional[torch.FloatTensor] = None lowercase__ : torch.FloatTensor = None lowercase__ : Optional[Tuple[torch.FloatTensor]] = None lowercase__ : Optional[Tuple[torch.FloatTensor]] = None class lowercase ( a ): def __init__( self : Dict , _UpperCamelCase : Optional[int]=1 , _UpperCamelCase : Optional[int]=0 , _UpperCamelCase : List[str]=2 , _UpperCamelCase : List[Any]=512 , _UpperCamelCase : Optional[int]="cls" , _UpperCamelCase : List[str]=False , _UpperCamelCase : Any=True , **_UpperCamelCase : List[str] , ) -> Tuple: '''simple docstring''' super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase ) SCREAMING_SNAKE_CASE = project_dim SCREAMING_SNAKE_CASE = pooler_fn SCREAMING_SNAKE_CASE = learn_encoder SCREAMING_SNAKE_CASE = use_attention_mask class lowercase ( a ): lowercase__ : Dict = [R"""pooler""", R"""logit_scale"""] lowercase__ : Any = [R"""position_ids""", R"""predictions.decoder.bias"""] lowercase__ : Optional[int] = """roberta""" lowercase__ : Union[str, Any] = RobertaSeriesConfig def __init__( self : List[str] , _UpperCamelCase : List[Any] ) -> Optional[int]: '''simple docstring''' super().__init__(_UpperCamelCase ) SCREAMING_SNAKE_CASE = XLMRobertaModel(_UpperCamelCase ) SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , config.project_dim ) SCREAMING_SNAKE_CASE = getattr(_UpperCamelCase , "has_pre_transformation" , _UpperCamelCase ) if self.has_pre_transformation: SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , config.project_dim ) SCREAMING_SNAKE_CASE = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps ) self.post_init() def __snake_case( self : Dict , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[torch.Tensor] = None , _UpperCamelCase : Optional[bool] = None , _UpperCamelCase : Optional[bool] = None , _UpperCamelCase : Optional[bool] = None , ) -> Tuple: '''simple docstring''' SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict SCREAMING_SNAKE_CASE = self.base_model( input_ids=_UpperCamelCase , attention_mask=_UpperCamelCase , token_type_ids=_UpperCamelCase , position_ids=_UpperCamelCase , head_mask=_UpperCamelCase , inputs_embeds=_UpperCamelCase , encoder_hidden_states=_UpperCamelCase , encoder_attention_mask=_UpperCamelCase , output_attentions=_UpperCamelCase , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=_UpperCamelCase , ) if self.has_pre_transformation: SCREAMING_SNAKE_CASE = outputs["hidden_states"][-2] SCREAMING_SNAKE_CASE = self.pre_LN(_UpperCamelCase ) SCREAMING_SNAKE_CASE = self.transformation_pre(_UpperCamelCase ) return TransformationModelOutput( projection_state=_UpperCamelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , ) else: SCREAMING_SNAKE_CASE = self.transformation(outputs.last_hidden_state ) return TransformationModelOutput( projection_state=_UpperCamelCase , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )

def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : int ): while b: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = b, a % b return a def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : int ): return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase__ , a % b ) def __lowerCamelCase (): print(F"euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}" ) print(F"euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}" ) print(F"euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}" ) print(F"euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}" ) print(F"euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}" ) print(F"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}" ) print(F"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}" ) print(F"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}" ) print(F"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}" ) print(F"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}" ) if __name__ == "__main__": main()

'''simple docstring''' # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser from accelerate.commands.config import get_config_parser from accelerate.commands.env import env_command_parser from accelerate.commands.launch import launch_command_parser from accelerate.commands.test import test_command_parser from accelerate.commands.tpu import tpu_command_parser def __SCREAMING_SNAKE_CASE ( ): _snake_case = ArgumentParser("""Accelerate CLI tool""" , usage="""accelerate <command> [<args>]""" , allow_abbrev=_SCREAMING_SNAKE_CASE ) _snake_case = parser.add_subparsers(help="""accelerate command helpers""" ) # Register commands get_config_parser(subparsers=_SCREAMING_SNAKE_CASE ) env_command_parser(subparsers=_SCREAMING_SNAKE_CASE ) launch_command_parser(subparsers=_SCREAMING_SNAKE_CASE ) tpu_command_parser(subparsers=_SCREAMING_SNAKE_CASE ) test_command_parser(subparsers=_SCREAMING_SNAKE_CASE ) # Let's go _snake_case = parser.parse_args() if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ): parser.print_help() exit(1 ) # Run args.func(_SCREAMING_SNAKE_CASE ) if __name__ == "__main__": main()

'''simple docstring''' import unittest import numpy as np import timeout_decorator # noqa from transformers import BlenderbotConfig, is_flax_available from transformers.testing_utils import jax_device, require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html __lowerCAmelCase = 'platform' import jax import jax.numpy as jnp from transformers import BlenderbotTokenizer from transformers.models.blenderbot.modeling_flax_blenderbot import ( FlaxBlenderbotForConditionalGeneration, FlaxBlenderbotModel, shift_tokens_right, ) def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , ): if attention_mask is None: _snake_case = np.where(input_ids != config.pad_token_id , 1 , 0 ) if decoder_attention_mask is None: _snake_case = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 ) if head_mask is None: _snake_case = np.ones((config.encoder_layers, config.encoder_attention_heads) ) if decoder_head_mask is None: _snake_case = np.ones((config.decoder_layers, config.decoder_attention_heads) ) if cross_attn_head_mask is None: _snake_case = np.ones((config.decoder_layers, config.decoder_attention_heads) ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } class _lowerCAmelCase : '''simple docstring''' def __init__(self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=7 , UpperCAmelCase=True , UpperCAmelCase=False , UpperCAmelCase=99 , UpperCAmelCase=16 , UpperCAmelCase=2 , UpperCAmelCase=4 , UpperCAmelCase=4 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=32 , UpperCAmelCase=2 , UpperCAmelCase=1 , UpperCAmelCase=0 , UpperCAmelCase=0.02 , ) -> Union[str, Any]: _snake_case = parent _snake_case = batch_size _snake_case = seq_length _snake_case = is_training _snake_case = use_labels _snake_case = vocab_size _snake_case = hidden_size _snake_case = num_hidden_layers _snake_case = num_attention_heads _snake_case = intermediate_size _snake_case = hidden_act _snake_case = hidden_dropout_prob _snake_case = attention_probs_dropout_prob _snake_case = max_position_embeddings _snake_case = eos_token_id _snake_case = pad_token_id _snake_case = bos_token_id _snake_case = initializer_range def lowercase (self ) -> str: _snake_case = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size ) _snake_case = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 ) _snake_case = shift_tokens_right(UpperCAmelCase , 1 , 2 ) _snake_case = BlenderbotConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=UpperCAmelCase , ) _snake_case = prepare_blenderbot_inputs_dict(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) return config, inputs_dict def lowercase (self ) -> Dict: _snake_case, _snake_case = self.prepare_config_and_inputs() return config, inputs_dict def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict: _snake_case = 20 _snake_case = model_class_name(UpperCAmelCase ) _snake_case = model.encode(inputs_dict["""input_ids"""] ) _snake_case, _snake_case = ( inputs_dict["""decoder_input_ids"""], inputs_dict["""decoder_attention_mask"""], ) _snake_case = model.init_cache(decoder_input_ids.shape[0] , UpperCAmelCase , UpperCAmelCase ) _snake_case = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" ) _snake_case = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _snake_case = model.decode( decoder_input_ids[:, :-1] , UpperCAmelCase , decoder_attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , decoder_position_ids=UpperCAmelCase , ) _snake_case = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" ) _snake_case = model.decode( decoder_input_ids[:, -1:] , UpperCAmelCase , decoder_attention_mask=UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=UpperCAmelCase , ) _snake_case = model.decode(UpperCAmelCase , UpperCAmelCase ) _snake_case = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) ) self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" ) def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[Any]: _snake_case = 20 _snake_case = model_class_name(UpperCAmelCase ) _snake_case = model.encode(inputs_dict["""input_ids"""] ) _snake_case, _snake_case = ( inputs_dict["""decoder_input_ids"""], inputs_dict["""decoder_attention_mask"""], ) _snake_case = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ), ] , axis=-1 , ) _snake_case = model.init_cache(decoder_input_ids.shape[0] , UpperCAmelCase , UpperCAmelCase ) _snake_case = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) _snake_case = model.decode( decoder_input_ids[:, :-1] , UpperCAmelCase , decoder_attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , decoder_position_ids=UpperCAmelCase , ) _snake_case = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" ) _snake_case = model.decode( decoder_input_ids[:, -1:] , UpperCAmelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=UpperCAmelCase , decoder_position_ids=UpperCAmelCase , ) _snake_case = model.decode(UpperCAmelCase , UpperCAmelCase , decoder_attention_mask=UpperCAmelCase ) _snake_case = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) ) self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" ) @require_flax class _lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = 99 def lowercase (self ) -> Any: _snake_case = np.array( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ] , dtype=np.intaa , ) _snake_case = input_ids.shape[0] _snake_case = BlenderbotConfig( vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , ) return config, input_ids, batch_size def lowercase (self ) -> Optional[Any]: _snake_case, _snake_case, _snake_case = self._get_config_and_data() _snake_case = FlaxBlenderbotForConditionalGeneration(UpperCAmelCase ) _snake_case = lm_model(input_ids=UpperCAmelCase ) _snake_case = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs["""logits"""].shape , UpperCAmelCase ) def lowercase (self ) -> int: _snake_case = BlenderbotConfig( vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , ) _snake_case = FlaxBlenderbotForConditionalGeneration(UpperCAmelCase ) _snake_case = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa ) _snake_case = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa ) _snake_case = lm_model(input_ids=UpperCAmelCase , decoder_input_ids=UpperCAmelCase ) _snake_case = (*summary.shape, config.vocab_size) self.assertEqual(outputs["""logits"""].shape , UpperCAmelCase ) def lowercase (self ) -> Tuple: _snake_case = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa ) _snake_case = shift_tokens_right(UpperCAmelCase , 1 , 2 ) _snake_case = np.equal(UpperCAmelCase , 1 ).astype(np.floataa ).sum() _snake_case = np.equal(UpperCAmelCase , 1 ).astype(np.floataa ).sum() self.assertEqual(shifted.shape , input_ids.shape ) self.assertEqual(UpperCAmelCase , n_pad_before - 1 ) self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() ) @require_flax class _lowerCAmelCase ( __snake_case , unittest.TestCase , __snake_case ): '''simple docstring''' lowerCAmelCase_ = True lowerCAmelCase_ = ( ( FlaxBlenderbotModel, FlaxBlenderbotForConditionalGeneration, ) if is_flax_available() else () ) lowerCAmelCase_ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else () def lowercase (self ) -> Any: _snake_case = FlaxBlenderbotModelTester(self ) def lowercase (self ) -> str: _snake_case, _snake_case = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) def lowercase (self ) -> List[str]: _snake_case, _snake_case = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) def lowercase (self ) -> Dict: _snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): _snake_case = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) _snake_case = model_class(UpperCAmelCase ) @jax.jit def encode_jitted(UpperCAmelCase , UpperCAmelCase=None , **UpperCAmelCase ): return model.encode(input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase ) with self.subTest("""JIT Enabled""" ): _snake_case = encode_jitted(**UpperCAmelCase ).to_tuple() with self.subTest("""JIT Disabled""" ): with jax.disable_jit(): _snake_case = encode_jitted(**UpperCAmelCase ).to_tuple() self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) ) for jitted_output, output in zip(UpperCAmelCase , UpperCAmelCase ): self.assertEqual(jitted_output.shape , output.shape ) def lowercase (self ) -> str: _snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): _snake_case = model_class(UpperCAmelCase ) _snake_case = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] ) _snake_case = { """decoder_input_ids""": inputs_dict["""decoder_input_ids"""], """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""], """encoder_outputs""": encoder_outputs, } @jax.jit def decode_jitted(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ): return model.decode( decoder_input_ids=UpperCAmelCase , decoder_attention_mask=UpperCAmelCase , encoder_outputs=UpperCAmelCase , ) with self.subTest("""JIT Enabled""" ): _snake_case = decode_jitted(**UpperCAmelCase ).to_tuple() with self.subTest("""JIT Disabled""" ): with jax.disable_jit(): _snake_case = decode_jitted(**UpperCAmelCase ).to_tuple() self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) ) for jitted_output, output in zip(UpperCAmelCase , UpperCAmelCase ): self.assertEqual(jitted_output.shape , output.shape ) @slow def lowercase (self ) -> Any: for model_class_name in self.all_model_classes: _snake_case = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""" ) # FlaxBlenderbotForSequenceClassification expects eos token in input_ids _snake_case = np.ones((1, 1) ) * model.config.eos_token_id _snake_case = model(UpperCAmelCase ) self.assertIsNotNone(UpperCAmelCase ) @unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""" ) @slow def lowercase (self ) -> Dict: _snake_case = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25} _snake_case = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True} _snake_case = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=UpperCAmelCase ) _snake_case = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""" ) _snake_case = ["""Sam"""] _snake_case = tokenizer(UpperCAmelCase , return_tensors="""jax""" ) _snake_case = model.generate(**UpperCAmelCase , **UpperCAmelCase ) _snake_case = """Sam is a great name. It means \"sun\" in Gaelic.""" _snake_case = tokenizer.batch_decode(UpperCAmelCase , **UpperCAmelCase ) assert generated_txt[0].strip() == tgt_text

'''simple docstring''' import webbrowser from sys import argv from urllib.parse import parse_qs, quote import requests from bsa import BeautifulSoup from fake_useragent import UserAgent if __name__ == "__main__": SCREAMING_SNAKE_CASE_: Any ='%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: '))) print('Googling.....') SCREAMING_SNAKE_CASE_: Optional[int] =f"https://www.google.com/search?q={query}&num=100" SCREAMING_SNAKE_CASE_: int =requests.get( url, headers={'User-Agent': str(UserAgent().random)}, ) try: SCREAMING_SNAKE_CASE_: Union[str, Any] =( BeautifulSoup(res.text, 'html.parser') .find('div', attrs={'class': 'yuRUbf'}) .find('a') .get('href') ) except AttributeError: SCREAMING_SNAKE_CASE_: Tuple =parse_qs( BeautifulSoup(res.text, 'html.parser') .find('div', attrs={'class': 'kCrYT'}) .find('a') .get('href') )['url'][0] webbrowser.open(link)

'''simple docstring''' import unittest from transformers import load_tool from transformers.utils import is_torch_available if is_torch_available(): import torch from transformers.testing_utils import require_torch from .test_tools_common import ToolTesterMixin @require_torch class __A ( unittest.TestCase , UpperCamelCase__ ): def _lowercase (self : Tuple ): UpperCAmelCase_ = load_tool("text-to-speech" ) self.tool.setup() def _lowercase (self : Union[str, Any] ): # SpeechT5 isn't deterministic torch.manual_seed(0 ) UpperCAmelCase_ = self.tool("hey" ) UpperCAmelCase_ = result.to_raw() self.assertTrue( torch.allclose( resulting_tensor[:3] , torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85] ) , ) ) def _lowercase (self : List[str] ): # SpeechT5 isn't deterministic torch.manual_seed(0 ) UpperCAmelCase_ = self.tool("hey" ) UpperCAmelCase_ = result.to_raw() self.assertTrue( torch.allclose( resulting_tensor[:3] , torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85] ) , ) )

# Lint as: python3 import sys from collections.abc import Mapping from typing import TYPE_CHECKING import numpy as np import pyarrow as pa from .. import config from ..utils.py_utils import map_nested from .formatting import TensorFormatter if TYPE_CHECKING: import torch class __snake_case ( TensorFormatter[Mapping, '''torch.Tensor''', Mapping] ): def __init__( self : Any , _snake_case : int=None , **_snake_case : Dict): """simple docstring""" super().__init__(features=_snake_case) UpperCAmelCase_ = torch_tensor_kwargs import torch # noqa import torch at initialization def lowerCamelCase ( self : Optional[Any] , _snake_case : Optional[int]): """simple docstring""" import torch if isinstance(_snake_case , _snake_case) and column: if all( isinstance(_snake_case , torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column): return torch.stack(_snake_case) return column def lowerCamelCase ( self : Optional[int] , _snake_case : List[str]): """simple docstring""" import torch if isinstance(_snake_case , (str, bytes, type(_snake_case))): return value elif isinstance(_snake_case , (np.character, np.ndarray)) and np.issubdtype(value.dtype , np.character): return value.tolist() UpperCAmelCase_ = {} if isinstance(_snake_case , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.integer): UpperCAmelCase_ = {'''dtype''': torch.intaa} elif isinstance(_snake_case , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.floating): UpperCAmelCase_ = {'''dtype''': torch.floataa} elif config.PIL_AVAILABLE and "PIL" in sys.modules: import PIL.Image if isinstance(_snake_case , PIL.Image.Image): UpperCAmelCase_ = np.asarray(_snake_case) return torch.tensor(_snake_case , **{**default_dtype, **self.torch_tensor_kwargs}) def lowerCamelCase ( self : List[Any] , _snake_case : List[Any]): """simple docstring""" import torch # support for torch, tf, jax etc. if hasattr(_snake_case , '''__array__''') and not isinstance(_snake_case , torch.Tensor): UpperCAmelCase_ = data_struct.__array__() # support for nested types like struct of list of struct if isinstance(_snake_case , np.ndarray): if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects return self._consolidate([self.recursive_tensorize(_snake_case) for substruct in data_struct]) elif isinstance(_snake_case , (list, tuple)): return self._consolidate([self.recursive_tensorize(_snake_case) for substruct in data_struct]) return self._tensorize(_snake_case) def lowerCamelCase ( self : List[Any] , _snake_case : dict): """simple docstring""" return map_nested(self._recursive_tensorize , _snake_case , map_list=_snake_case) def lowerCamelCase ( self : List[Any] , _snake_case : pa.Table): """simple docstring""" UpperCAmelCase_ = self.numpy_arrow_extractor().extract_row(_snake_case) UpperCAmelCase_ = self.python_features_decoder.decode_row(_snake_case) return self.recursive_tensorize(_snake_case) def lowerCamelCase ( self : List[Any] , _snake_case : pa.Table): """simple docstring""" UpperCAmelCase_ = self.numpy_arrow_extractor().extract_column(_snake_case) UpperCAmelCase_ = self.python_features_decoder.decode_column(_snake_case , pa_table.column_names[0]) UpperCAmelCase_ = self.recursive_tensorize(_snake_case) UpperCAmelCase_ = self._consolidate(_snake_case) return column def lowerCamelCase ( self : List[str] , _snake_case : pa.Table): """simple docstring""" UpperCAmelCase_ = self.numpy_arrow_extractor().extract_batch(_snake_case) UpperCAmelCase_ = self.python_features_decoder.decode_batch(_snake_case) UpperCAmelCase_ = self.recursive_tensorize(_snake_case) for column_name in batch: UpperCAmelCase_ = self._consolidate(batch[column_name]) return batch

import json import os import re import shutil import tempfile import unittest from typing import Tuple from transformers import AddedToken, BatchEncoding, ByTaTokenizer from transformers.utils import cached_property, is_tf_available, is_torch_available from ...test_tokenization_common import TokenizerTesterMixin if is_torch_available(): snake_case_ : Optional[Any] = "pt" elif is_tf_available(): snake_case_ : Union[str, Any] = "tf" else: snake_case_ : str = "jax" class __snake_case ( a , unittest.TestCase ): UpperCAmelCase__ : List[Any] = ByTaTokenizer UpperCAmelCase__ : int = False def lowerCamelCase ( self : Optional[int]): """simple docstring""" super().setUp() UpperCAmelCase_ = ByTaTokenizer() tokenizer.save_pretrained(self.tmpdirname) @cached_property def lowerCamelCase ( self : Tuple): """simple docstring""" return ByTaTokenizer.from_pretrained('''google/byt5-small''') def lowerCamelCase ( self : List[str] , **_snake_case : Union[str, Any]): """simple docstring""" return self.tokenizer_class.from_pretrained(self.tmpdirname , **_snake_case) def lowerCamelCase ( self : Dict , _snake_case : int , _snake_case : Tuple=False , _snake_case : Dict=20 , _snake_case : Optional[Any]=5): """simple docstring""" UpperCAmelCase_ = [] for i in range(len(_snake_case)): try: UpperCAmelCase_ = tokenizer.decode([i] , clean_up_tokenization_spaces=_snake_case) except UnicodeDecodeError: pass toks.append((i, tok)) UpperCAmelCase_ = list(filter(lambda _snake_case: re.match(r'''^[ a-zA-Z]+$''' , t[1]) , _snake_case)) UpperCAmelCase_ = list(filter(lambda _snake_case: [t[0]] == tokenizer.encode(t[1] , add_special_tokens=_snake_case) , _snake_case)) if max_length is not None and len(_snake_case) > max_length: UpperCAmelCase_ = toks[:max_length] if min_length is not None and len(_snake_case) < min_length and len(_snake_case) > 0: while len(_snake_case) < min_length: UpperCAmelCase_ = toks + toks # toks_str = [t[1] for t in toks] UpperCAmelCase_ = [t[0] for t in toks] # Ensure consistency UpperCAmelCase_ = tokenizer.decode(_snake_case , clean_up_tokenization_spaces=_snake_case) if " " not in output_txt and len(_snake_case) > 1: UpperCAmelCase_ = ( tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=_snake_case) + ''' ''' + tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=_snake_case) ) if with_prefix_space: UpperCAmelCase_ = ''' ''' + output_txt UpperCAmelCase_ = tokenizer.encode(_snake_case , add_special_tokens=_snake_case) return output_txt, output_ids def lowerCamelCase ( self : Union[str, Any]): """simple docstring""" UpperCAmelCase_ = self.ta_base_tokenizer UpperCAmelCase_ = tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']) UpperCAmelCase_ = tokenizer(['''hi''', '''I went to the gym''', '''''']) self.assertListEqual(batch_with_eos_added['''input_ids'''] , batch_without_eos_added['''input_ids''']) def lowerCamelCase ( self : str): """simple docstring""" UpperCAmelCase_ = self.ta_base_tokenizer UpperCAmelCase_ = '''Unicode €.''' UpperCAmelCase_ = tokenizer(_snake_case) UpperCAmelCase_ = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1] self.assertEqual(encoded['''input_ids'''] , _snake_case) # decoding UpperCAmelCase_ = tokenizer.decode(_snake_case) self.assertEqual(_snake_case , '''Unicode €.</s>''') UpperCAmelCase_ = tokenizer('''e è é ê ë''') UpperCAmelCase_ = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1] self.assertEqual(encoded['''input_ids'''] , _snake_case) # decoding UpperCAmelCase_ = tokenizer.decode(_snake_case) self.assertEqual(_snake_case , '''e è é ê ë</s>''') # encode/decode, but with `encode` instead of `__call__` self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''')) , '''e è é ê ë</s>''') def lowerCamelCase ( self : Any): """simple docstring""" UpperCAmelCase_ = self.ta_base_tokenizer UpperCAmelCase_ = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.'''] # fmt: off UpperCAmelCase_ = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0] # fmt: on UpperCAmelCase_ = tokenizer(_snake_case , padding=_snake_case , return_tensors=_snake_case) self.assertIsInstance(_snake_case , _snake_case) if FRAMEWORK != "jax": UpperCAmelCase_ = list(batch.input_ids.numpy()[0]) else: UpperCAmelCase_ = list(batch.input_ids.tolist()[0]) self.assertListEqual(_snake_case , _snake_case) self.assertEqual((2, 37) , batch.input_ids.shape) self.assertEqual((2, 37) , batch.attention_mask.shape) def lowerCamelCase ( self : Optional[Any]): """simple docstring""" UpperCAmelCase_ = self.ta_base_tokenizer UpperCAmelCase_ = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.'''] UpperCAmelCase_ = tokenizer(_snake_case , padding=_snake_case , return_tensors=_snake_case) # check if input_ids are returned and no decoder_input_ids self.assertIn('''input_ids''' , _snake_case) self.assertIn('''attention_mask''' , _snake_case) self.assertNotIn('''decoder_input_ids''' , _snake_case) self.assertNotIn('''decoder_attention_mask''' , _snake_case) def lowerCamelCase ( self : Tuple): """simple docstring""" UpperCAmelCase_ = self.ta_base_tokenizer UpperCAmelCase_ = [ '''Summary of the text.''', '''Another summary.''', ] UpperCAmelCase_ = tokenizer( text_target=_snake_case , max_length=32 , padding='''max_length''' , truncation=_snake_case , return_tensors=_snake_case) self.assertEqual(32 , targets['''input_ids'''].shape[1]) def lowerCamelCase ( self : int): """simple docstring""" UpperCAmelCase_ = self.ta_base_tokenizer UpperCAmelCase_ = ['''A long paragraph for summarization. </s>'''] UpperCAmelCase_ = ['''Summary of the text. </s>'''] # fmt: off UpperCAmelCase_ = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1] UpperCAmelCase_ = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1] # fmt: on UpperCAmelCase_ = tokenizer(_snake_case , text_target=_snake_case) self.assertEqual(_snake_case , batch['''input_ids'''][0]) self.assertEqual(_snake_case , batch['''labels'''][0]) def lowerCamelCase ( self : Tuple): """simple docstring""" UpperCAmelCase_ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}"""): self.assertNotEqual(tokenizer.model_max_length , 42) # Now let's start the test UpperCAmelCase_ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}"""): # Isolate this from the other tests because we save additional tokens/etc UpperCAmelCase_ = tempfile.mkdtemp() UpperCAmelCase_ = ''' He is very happy, UNwant\u00E9d,running''' UpperCAmelCase_ = tokenizer.encode(_snake_case , add_special_tokens=_snake_case) tokenizer.save_pretrained(_snake_case) UpperCAmelCase_ = tokenizer.__class__.from_pretrained(_snake_case) UpperCAmelCase_ = after_tokenizer.encode(_snake_case , add_special_tokens=_snake_case) self.assertListEqual(_snake_case , _snake_case) shutil.rmtree(_snake_case) UpperCAmelCase_ = self.get_tokenizers(model_max_length=42) for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}"""): # Isolate this from the other tests because we save additional tokens/etc UpperCAmelCase_ = tempfile.mkdtemp() UpperCAmelCase_ = ''' He is very happy, UNwant\u00E9d,running''' tokenizer.add_tokens(['''bim''', '''bambam''']) UpperCAmelCase_ = tokenizer.additional_special_tokens additional_special_tokens.append('''new_additional_special_token''') tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}) UpperCAmelCase_ = tokenizer.encode(_snake_case , add_special_tokens=_snake_case) tokenizer.save_pretrained(_snake_case) UpperCAmelCase_ = tokenizer.__class__.from_pretrained(_snake_case) UpperCAmelCase_ = after_tokenizer.encode(_snake_case , add_special_tokens=_snake_case) self.assertListEqual(_snake_case , _snake_case) self.assertIn('''new_additional_special_token''' , after_tokenizer.additional_special_tokens) self.assertEqual(after_tokenizer.model_max_length , 42) UpperCAmelCase_ = tokenizer.__class__.from_pretrained(_snake_case , model_max_length=43) self.assertEqual(tokenizer.model_max_length , 43) shutil.rmtree(_snake_case) def lowerCamelCase ( self : List[Any]): """simple docstring""" UpperCAmelCase_ = [] if self.test_slow_tokenizer: tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())) if self.test_rust_tokenizer: tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())) for tokenizer_class, tokenizer_utils in tokenizer_list: with tempfile.TemporaryDirectory() as tmp_dir: tokenizer_utils.save_pretrained(_snake_case) with open(os.path.join(_snake_case , '''special_tokens_map.json''') , encoding='''utf-8''') as json_file: UpperCAmelCase_ = json.load(_snake_case) with open(os.path.join(_snake_case , '''tokenizer_config.json''') , encoding='''utf-8''') as json_file: UpperCAmelCase_ = json.load(_snake_case) UpperCAmelCase_ = [F"""<extra_id_{i}>""" for i in range(125)] UpperCAmelCase_ = added_tokens_extra_ids + [ '''an_additional_special_token''' ] UpperCAmelCase_ = added_tokens_extra_ids + [ '''an_additional_special_token''' ] with open(os.path.join(_snake_case , '''special_tokens_map.json''') , '''w''' , encoding='''utf-8''') as outfile: json.dump(_snake_case , _snake_case) with open(os.path.join(_snake_case , '''tokenizer_config.json''') , '''w''' , encoding='''utf-8''') as outfile: json.dump(_snake_case , _snake_case) # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and # "special_tokens_map.json" files UpperCAmelCase_ = tokenizer_class.from_pretrained( _snake_case , ) self.assertIn( '''an_additional_special_token''' , tokenizer_without_change_in_init.additional_special_tokens) # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab self.assertEqual( ['''an_additional_special_token'''] , tokenizer_without_change_in_init.convert_ids_to_tokens( tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''])) , ) # Now we test that we can change the value of additional_special_tokens in the from_pretrained UpperCAmelCase_ = added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' , lstrip=_snake_case)] UpperCAmelCase_ = tokenizer_class.from_pretrained( _snake_case , additional_special_tokens=_snake_case , ) self.assertIn('''a_new_additional_special_token''' , tokenizer.additional_special_tokens) self.assertEqual( ['''a_new_additional_special_token'''] , tokenizer.convert_ids_to_tokens( tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''])) , ) def lowerCamelCase ( self : Any): """simple docstring""" UpperCAmelCase_ = [] if self.test_slow_tokenizer: tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())) if self.test_rust_tokenizer: tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())) for tokenizer_class, tokenizer_utils in tokenizer_list: with tempfile.TemporaryDirectory() as tmp_dir: tokenizer_utils.save_pretrained(_snake_case) UpperCAmelCase_ = tokenizer_class.from_pretrained(_snake_case) self.assertTrue(tokenizer.decode([255]) == '''''') def lowerCamelCase ( self : int): """simple docstring""" pass def lowerCamelCase ( self : Optional[int]): """simple docstring""" pass def lowerCamelCase ( self : Dict): """simple docstring""" pass def lowerCamelCase ( self : List[Any]): """simple docstring""" pass def lowerCamelCase ( self : Tuple): """simple docstring""" UpperCAmelCase_ = self.get_tokenizers(fast=_snake_case , do_lower_case=_snake_case) for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}"""): UpperCAmelCase_ = ['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>'''] UpperCAmelCase_ = tokenizer.convert_tokens_to_string(_snake_case) self.assertIsInstance(_snake_case , _snake_case) def lowerCamelCase ( self : Union[str, Any]): """simple docstring""" UpperCAmelCase_ = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}"""): UpperCAmelCase_ = [ '''bos_token''', '''eos_token''', '''unk_token''', '''sep_token''', '''pad_token''', '''cls_token''', '''mask_token''', ] UpperCAmelCase_ = 0 UpperCAmelCase_ = tokenizer.convert_ids_to_tokens( _snake_case , skip_special_tokens=_snake_case) for attr in attributes_list: setattr(_snake_case , attr + '''_id''' , _snake_case) self.assertEqual(getattr(_snake_case , _snake_case) , _snake_case) self.assertEqual(getattr(_snake_case , attr + '''_id''') , _snake_case) setattr(_snake_case , attr + '''_id''' , _snake_case) self.assertEqual(getattr(_snake_case , _snake_case) , _snake_case) self.assertEqual(getattr(_snake_case , attr + '''_id''') , _snake_case) setattr(_snake_case , '''additional_special_tokens_ids''' , []) self.assertListEqual(getattr(_snake_case , '''additional_special_tokens''') , []) self.assertListEqual(getattr(_snake_case , '''additional_special_tokens_ids''') , []) setattr(_snake_case , '''additional_special_tokens_ids''' , [token_id_to_test_setters]) self.assertListEqual(getattr(_snake_case , '''additional_special_tokens''') , [token_to_test_setters]) self.assertListEqual(getattr(_snake_case , '''additional_special_tokens_ids''') , [token_id_to_test_setters])

'''simple docstring''' from __future__ import annotations from collections.abc import Generator def lowercase (): """simple docstring""" _lowerCAmelCase : dict[int, int] = {} _lowerCAmelCase : str = 2 while True: _lowerCAmelCase : Optional[Any] = factor_map.pop(_A , _A ) if factor: _lowerCAmelCase : Optional[int] = factor + prime while x in factor_map: x += factor _lowerCAmelCase : Dict = factor else: _lowerCAmelCase : Optional[Any] = prime yield prime prime += 1 def lowercase (_A = 1E10 ): """simple docstring""" _lowerCAmelCase : Union[str, Any] = sieve() _lowerCAmelCase : Union[str, Any] = 1 while True: _lowerCAmelCase : List[Any] = next(_A ) if (2 * prime * n) > limit: return n # Ignore the next prime as the reminder will be 2. next(_A ) n += 2 if __name__ == "__main__": print(solution())

'''simple docstring''' from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging lowerCAmelCase : int = logging.get_logger(__name__) lowerCAmelCase : Union[str, Any] = { """google/mobilenet_v2_1.4_224""": """https://huggingface.co./google/mobilenet_v2_1.4_224/resolve/main/config.json""", """google/mobilenet_v2_1.0_224""": """https://huggingface.co./google/mobilenet_v2_1.0_224/resolve/main/config.json""", """google/mobilenet_v2_0.75_160""": """https://huggingface.co./google/mobilenet_v2_0.75_160/resolve/main/config.json""", """google/mobilenet_v2_0.35_96""": """https://huggingface.co./google/mobilenet_v2_0.35_96/resolve/main/config.json""", # See all MobileNetV2 models at https://huggingface.co./models?filter=mobilenet_v2 } class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __magic_name__ = "mobilenet_v2" def __init__( self , snake_case__=3 , snake_case__=224 , snake_case__=1.0 , snake_case__=8 , snake_case__=8 , snake_case__=6 , snake_case__=32 , snake_case__=True , snake_case__=True , snake_case__="relu6" , snake_case__=True , snake_case__=0.8 , snake_case__=0.02 , snake_case__=0.001 , snake_case__=255 , **snake_case__ , ): '''simple docstring''' super().__init__(**snake_case__ ) if depth_multiplier <= 0: raise ValueError('depth_multiplier must be greater than zero.' ) _lowerCAmelCase : List[str] = num_channels _lowerCAmelCase : Union[str, Any] = image_size _lowerCAmelCase : List[Any] = depth_multiplier _lowerCAmelCase : List[Any] = depth_divisible_by _lowerCAmelCase : Optional[Any] = min_depth _lowerCAmelCase : str = expand_ratio _lowerCAmelCase : str = output_stride _lowerCAmelCase : Any = first_layer_is_expansion _lowerCAmelCase : int = finegrained_output _lowerCAmelCase : str = hidden_act _lowerCAmelCase : List[str] = tf_padding _lowerCAmelCase : Optional[int] = classifier_dropout_prob _lowerCAmelCase : int = initializer_range _lowerCAmelCase : Optional[int] = layer_norm_eps _lowerCAmelCase : str = semantic_loss_ignore_index class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __magic_name__ = version.parse("1.11" ) @property def a ( self ): '''simple docstring''' return OrderedDict([('pixel_values', {0: 'batch'})] ) @property def a ( self ): '''simple docstring''' if self.task == "image-classification": return OrderedDict([('logits', {0: 'batch'})] ) else: return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})] ) @property def a ( self ): '''simple docstring''' return 1E-4

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_torch_available, ) __a = { 'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __a = [ 'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST', 'MegaForCausalLM', 'MegaForMaskedLM', 'MegaForMultipleChoice', 'MegaForQuestionAnswering', 'MegaForSequenceClassification', 'MegaForTokenClassification', 'MegaModel', 'MegaPreTrainedModel', ] if TYPE_CHECKING: from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mega import ( MEGA_PRETRAINED_MODEL_ARCHIVE_LIST, MegaForCausalLM, MegaForMaskedLM, MegaForMultipleChoice, MegaForQuestionAnswering, MegaForSequenceClassification, MegaForTokenClassification, MegaModel, MegaPreTrainedModel, ) else: import sys __a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase__ = logging.get_logger(__name__) UpperCAmelCase__ = { '''microsoft/wavlm-base''': '''https://huggingface.co./microsoft/wavlm-base/resolve/main/config.json''', # See all WavLM models at https://huggingface.co./models?filter=wavlm } class lowerCamelCase__ ( lowerCAmelCase): SCREAMING_SNAKE_CASE__ = '''wavlm''' def __init__(self , UpperCAmelCase=3_2 , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase="group" , UpperCAmelCase="gelu" , UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2) , UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2) , UpperCAmelCase=False , UpperCAmelCase=1_2_8 , UpperCAmelCase=1_6 , UpperCAmelCase=3_2_0 , UpperCAmelCase=8_0_0 , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=0.05 , UpperCAmelCase=1_0 , UpperCAmelCase=2 , UpperCAmelCase=0.0 , UpperCAmelCase=1_0 , UpperCAmelCase=3_2_0 , UpperCAmelCase=2 , UpperCAmelCase=0.1 , UpperCAmelCase=1_0_0 , UpperCAmelCase=2_5_6 , UpperCAmelCase=2_5_6 , UpperCAmelCase=0.1 , UpperCAmelCase="mean" , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=2_5_6 , UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , UpperCAmelCase=(5, 3, 3, 1, 1) , UpperCAmelCase=(1, 2, 3, 1, 1) , UpperCAmelCase=5_1_2 , UpperCAmelCase=8_0 , UpperCAmelCase=0 , UpperCAmelCase=1 , UpperCAmelCase=2 , UpperCAmelCase=False , UpperCAmelCase=3 , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=None , **UpperCAmelCase , ) -> Optional[Any]: super().__init__(**UpperCAmelCase , pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase ) _lowercase =hidden_size _lowercase =feat_extract_norm _lowercase =feat_extract_activation _lowercase =list(UpperCAmelCase ) _lowercase =list(UpperCAmelCase ) _lowercase =list(UpperCAmelCase ) _lowercase =conv_bias _lowercase =num_buckets _lowercase =max_bucket_distance _lowercase =num_conv_pos_embeddings _lowercase =num_conv_pos_embedding_groups _lowercase =len(self.conv_dim ) _lowercase =num_hidden_layers _lowercase =intermediate_size _lowercase =hidden_act _lowercase =num_attention_heads _lowercase =hidden_dropout _lowercase =attention_dropout _lowercase =activation_dropout _lowercase =feat_proj_dropout _lowercase =final_dropout _lowercase =layerdrop _lowercase =layer_norm_eps _lowercase =initializer_range _lowercase =num_ctc_classes _lowercase =vocab_size _lowercase =do_stable_layer_norm _lowercase =use_weighted_layer_sum _lowercase =classifier_proj_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==''' ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =''' f" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`," f" `len(config.conv_kernel) = {len(self.conv_kernel )}`." ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _lowercase =apply_spec_augment _lowercase =mask_time_prob _lowercase =mask_time_length _lowercase =mask_time_min_masks _lowercase =mask_feature_prob _lowercase =mask_feature_length # parameters for pretraining with codevector quantized representations _lowercase =num_codevectors_per_group _lowercase =num_codevector_groups _lowercase =contrastive_logits_temperature _lowercase =num_negatives _lowercase =codevector_dim _lowercase =proj_codevector_dim _lowercase =diversity_loss_weight # ctc loss _lowercase =ctc_loss_reduction _lowercase =ctc_zero_infinity # adapter _lowercase =add_adapter _lowercase =adapter_kernel_size _lowercase =adapter_stride _lowercase =num_adapter_layers _lowercase =output_hidden_size or hidden_size # SequenceClassification-specific parameter. Feel free to ignore for other classes. _lowercase =classifier_proj_size # XVector-specific parameters. Feel free to ignore for other classes. _lowercase =list(UpperCAmelCase ) _lowercase =list(UpperCAmelCase ) _lowercase =list(UpperCAmelCase ) _lowercase =xvector_output_dim @property def __A (self ) -> int: return functools.reduce(operator.mul , self.conv_stride , 1 )

'''simple docstring''' import os import shutil from pathlib import Path from typing import Optional, Union import numpy as np from huggingface_hub import hf_hub_download from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging if is_onnx_available(): import onnxruntime as ort UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''tensor(bool)''': np.bool_, '''tensor(int8)''': np.inta, '''tensor(uint8)''': np.uinta, '''tensor(int16)''': np.intaa, '''tensor(uint16)''': np.uintaa, '''tensor(int32)''': np.intaa, '''tensor(uint32)''': np.uintaa, '''tensor(int64)''': np.intaa, '''tensor(uint64)''': np.uintaa, '''tensor(float16)''': np.floataa, '''tensor(float)''': np.floataa, '''tensor(double)''': np.floataa, } class lowerCAmelCase_ : '''simple docstring''' def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Any=None , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> Optional[Any]: '''simple docstring''' logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.''' ) A: int = model A: Union[str, Any] = kwargs.get('''model_save_dir''' , SCREAMING_SNAKE_CASE_ ) A: List[Any] = kwargs.get('''latest_model_name''' , SCREAMING_SNAKE_CASE_ ) def __call__( self : Any , **SCREAMING_SNAKE_CASE_ : str ) -> Any: '''simple docstring''' A: Optional[int] = {k: np.array(SCREAMING_SNAKE_CASE_ ) for k, v in kwargs.items()} return self.model.run(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) @staticmethod def _snake_case ( SCREAMING_SNAKE_CASE_ : Union[str, Path] , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , SCREAMING_SNAKE_CASE_ : Optional[Any]=None ) -> Tuple: '''simple docstring''' if provider is None: logger.info('''No onnxruntime provider specified, using CPUExecutionProvider''' ) A: Optional[int] = '''CPUExecutionProvider''' return ort.InferenceSession(SCREAMING_SNAKE_CASE_ , providers=[provider] , sess_options=SCREAMING_SNAKE_CASE_ ) def _snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : Union[str, Path] , SCREAMING_SNAKE_CASE_ : Optional[str] = None , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> str: '''simple docstring''' A: Dict = file_name if file_name is not None else ONNX_WEIGHTS_NAME A: Optional[int] = self.model_save_dir.joinpath(self.latest_model_name ) A: List[str] = Path(SCREAMING_SNAKE_CASE_ ).joinpath(SCREAMING_SNAKE_CASE_ ) try: shutil.copyfile(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) except shutil.SameFileError: pass # copy external weights (for models >2GB) A: Any = self.model_save_dir.joinpath(SCREAMING_SNAKE_CASE_ ) if src_path.exists(): A: Dict = Path(SCREAMING_SNAKE_CASE_ ).joinpath(SCREAMING_SNAKE_CASE_ ) try: shutil.copyfile(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) except shutil.SameFileError: pass def _snake_case ( self : Dict , SCREAMING_SNAKE_CASE_ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE_ : Dict , ) -> Tuple: '''simple docstring''' if os.path.isfile(SCREAMING_SNAKE_CASE_ ): logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" ) return os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ ) # saving model weights/files self._save_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) @classmethod def _snake_case ( cls : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Path] , SCREAMING_SNAKE_CASE_ : Optional[Union[bool, str, None]] = None , SCREAMING_SNAKE_CASE_ : Optional[Union[str, None]] = None , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional["ort.SessionOptions"] = None , **SCREAMING_SNAKE_CASE_ : Optional[Any] , ) -> Optional[Any]: '''simple docstring''' A: int = file_name if file_name is not None else ONNX_WEIGHTS_NAME # load model from local directory if os.path.isdir(SCREAMING_SNAKE_CASE_ ): A: Optional[Any] = OnnxRuntimeModel.load_model( os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , provider=SCREAMING_SNAKE_CASE_ , sess_options=SCREAMING_SNAKE_CASE_ ) A: Optional[int] = Path(SCREAMING_SNAKE_CASE_ ) # load model from hub else: # download model A: Optional[Any] = hf_hub_download( repo_id=SCREAMING_SNAKE_CASE_ , filename=SCREAMING_SNAKE_CASE_ , use_auth_token=SCREAMING_SNAKE_CASE_ , revision=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , force_download=SCREAMING_SNAKE_CASE_ , ) A: Optional[Any] = Path(SCREAMING_SNAKE_CASE_ ).parent A: Optional[Any] = Path(SCREAMING_SNAKE_CASE_ ).name A: Tuple = OnnxRuntimeModel.load_model(SCREAMING_SNAKE_CASE_ , provider=SCREAMING_SNAKE_CASE_ , sess_options=SCREAMING_SNAKE_CASE_ ) return cls(model=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) @classmethod def _snake_case ( cls : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Path] , SCREAMING_SNAKE_CASE_ : bool = True , SCREAMING_SNAKE_CASE_ : Optional[str] = None , SCREAMING_SNAKE_CASE_ : Optional[str] = None , **SCREAMING_SNAKE_CASE_ : List[Any] , ) -> Dict: '''simple docstring''' A: int = None if len(str(SCREAMING_SNAKE_CASE_ ).split('''@''' ) ) == 2: A , A: int = model_id.split('''@''' ) return cls._from_pretrained( model_id=SCREAMING_SNAKE_CASE_ , revision=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , force_download=SCREAMING_SNAKE_CASE_ , use_auth_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available UpperCamelCase = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = ['''GPTSw3Tokenizer'''] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt_swa import GPTSwaTokenizer else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

'''simple docstring''' from decimal import Decimal, getcontext from math import ceil, factorial def _lowercase ( __A ): '''simple docstring''' if not isinstance(__A ,__A ): raise TypeError("""Undefined for non-integers""" ) elif precision < 1: raise ValueError("""Undefined for non-natural numbers""" ) __UpperCamelCase = precision __UpperCamelCase = ceil(precision / 14 ) __UpperCamelCase = 426_880 * Decimal(10_005 ).sqrt() __UpperCamelCase = 1 __UpperCamelCase = 13_591_409 __UpperCamelCase = Decimal(__A ) for k in range(1 ,__A ): __UpperCamelCase = factorial(6 * k ) // (factorial(3 * k ) * factorial(__A ) ** 3) linear_term += 545_140_134 exponential_term *= -262_537_412_640_768_000 partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term return str(constant_term / partial_sum )[:-1] if __name__ == "__main__": a__ : Union[str, Any] = 5_0 print(f'''The first {n} digits of pi is: {pi(n)}''')

'''simple docstring''' import string def _lowercase ( __A ): '''simple docstring''' for key in range(len(string.ascii_uppercase ) ): __UpperCamelCase = """""" for symbol in message: if symbol in string.ascii_uppercase: __UpperCamelCase = string.ascii_uppercase.find(__A ) __UpperCamelCase = num - key if num < 0: __UpperCamelCase = num + len(string.ascii_uppercase ) __UpperCamelCase = translated + string.ascii_uppercase[num] else: __UpperCamelCase = translated + symbol print(f"Decryption using Key #{key}: {translated}" ) def _lowercase ( ): '''simple docstring''' __UpperCamelCase = input("""Encrypted message: """ ) __UpperCamelCase = message.upper() decrypt(__A ) if __name__ == "__main__": import doctest doctest.testmod() main()

'''simple docstring''' from torch import nn def snake_case_ ( lowerCAmelCase_ )-> List[Any]: '''simple docstring''' if act_fn in ["swish", "silu"]: return nn.SiLU() elif act_fn == "mish": return nn.Mish() elif act_fn == "gelu": return nn.GELU() else: raise ValueError(F'''Unsupported activation function: {act_fn}''' )

'''simple docstring''' import argparse import glob import logging import os import time from argparse import Namespace import numpy as np import torch from lightning_base import BaseTransformer, add_generic_args, generic_train from torch.utils.data import DataLoader, TensorDataset from transformers import glue_compute_metrics as compute_metrics from transformers import glue_convert_examples_to_features as convert_examples_to_features from transformers import glue_output_modes, glue_tasks_num_labels from transformers import glue_processors as processors A_ : Dict = logging.getLogger(__name__) class lowercase ( _lowerCamelCase ): """simple docstring""" UpperCAmelCase = """sequence-classification""" def __init__( self ,a_ ) -> Dict: if type(a_ ) == dict: _UpperCAmelCase : Tuple = Namespace(**a_ ) _UpperCAmelCase : Optional[int] = glue_output_modes[hparams.task] _UpperCAmelCase : Union[str, Any] = glue_tasks_num_labels[hparams.task] super().__init__(a_ ,a_ ,self.mode ) def _snake_case ( self ,**a_ ) -> Optional[Any]: return self.model(**a_ ) def _snake_case ( self ,a_ ,a_ ) -> Optional[Any]: _UpperCAmelCase : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]} if self.config.model_type not in ["distilbert", "bart"]: _UpperCAmelCase : Any = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None _UpperCAmelCase : Any = self(**a_ ) _UpperCAmelCase : int = outputs[0] _UpperCAmelCase : Any = self.trainer.lr_schedulers[0]["""scheduler"""] _UpperCAmelCase : Any = {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]} return {"loss": loss, "log": tensorboard_logs} def _snake_case ( self ) -> int: _UpperCAmelCase : Optional[int] = self.hparams _UpperCAmelCase : int = processors[args.task]() _UpperCAmelCase : str = processor.get_labels() for mode in ["train", "dev"]: _UpperCAmelCase : Tuple = self._feature_file(a_ ) if os.path.exists(a_ ) and not args.overwrite_cache: logger.info("""Loading features from cached file %s""" ,a_ ) else: logger.info("""Creating features from dataset file at %s""" ,args.data_dir ) _UpperCAmelCase : List[Any] = ( processor.get_dev_examples(args.data_dir ) if mode == """dev""" else processor.get_train_examples(args.data_dir ) ) _UpperCAmelCase : Union[str, Any] = convert_examples_to_features( a_ ,self.tokenizer ,max_length=args.max_seq_length ,label_list=self.labels ,output_mode=args.glue_output_mode ,) logger.info("""Saving features into cached file %s""" ,a_ ) torch.save(a_ ,a_ ) def _snake_case ( self ,a_ ,a_ ,a_ = False ) -> DataLoader: _UpperCAmelCase : Union[str, Any] = """dev""" if mode == """test""" else mode _UpperCAmelCase : Tuple = self._feature_file(a_ ) logger.info("""Loading features from cached file %s""" ,a_ ) _UpperCAmelCase : Union[str, Any] = torch.load(a_ ) _UpperCAmelCase : List[str] = torch.tensor([f.input_ids for f in features] ,dtype=torch.long ) _UpperCAmelCase : Tuple = torch.tensor([f.attention_mask for f in features] ,dtype=torch.long ) _UpperCAmelCase : str = torch.tensor([f.token_type_ids for f in features] ,dtype=torch.long ) if self.hparams.glue_output_mode == "classification": _UpperCAmelCase : Optional[int] = torch.tensor([f.label for f in features] ,dtype=torch.long ) elif self.hparams.glue_output_mode == "regression": _UpperCAmelCase : str = torch.tensor([f.label for f in features] ,dtype=torch.float ) return DataLoader( TensorDataset(a_ ,a_ ,a_ ,a_ ) ,batch_size=a_ ,shuffle=a_ ,) def _snake_case ( self ,a_ ,a_ ) -> Any: _UpperCAmelCase : Any = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]} if self.config.model_type not in ["distilbert", "bart"]: _UpperCAmelCase : int = batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None _UpperCAmelCase : List[str] = self(**a_ ) _UpperCAmelCase ,_UpperCAmelCase : Optional[int] = outputs[:2] _UpperCAmelCase : List[str] = logits.detach().cpu().numpy() _UpperCAmelCase : Union[str, Any] = inputs["""labels"""].detach().cpu().numpy() return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids} def _snake_case ( self ,a_ ) -> tuple: _UpperCAmelCase : Optional[int] = torch.stack([x["""val_loss"""] for x in outputs] ).mean().detach().cpu().item() _UpperCAmelCase : Any = np.concatenate([x["""pred"""] for x in outputs] ,axis=0 ) if self.hparams.glue_output_mode == "classification": _UpperCAmelCase : int = np.argmax(a_ ,axis=1 ) elif self.hparams.glue_output_mode == "regression": _UpperCAmelCase : Union[str, Any] = np.squeeze(a_ ) _UpperCAmelCase : str = np.concatenate([x["""target"""] for x in outputs] ,axis=0 ) _UpperCAmelCase : Tuple = [[] for _ in range(out_label_ids.shape[0] )] _UpperCAmelCase : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )] _UpperCAmelCase : Optional[int] = {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task ,a_ ,a_ )} _UpperCAmelCase : Dict = dict(results.items() ) _UpperCAmelCase : Any = results return ret, preds_list, out_label_list def _snake_case ( self ,a_ ) -> dict: _UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : Dict = self._eval_end(a_ ) _UpperCAmelCase : List[Any] = ret["""log"""] return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs} def _snake_case ( self ,a_ ) -> dict: _UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase : str = self._eval_end(a_ ) _UpperCAmelCase : List[Any] = ret["""log"""] # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss` return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs} @staticmethod def _snake_case ( a_ ,a_ ) -> Any: BaseTransformer.add_model_specific_args(a_ ,a_ ) parser.add_argument( """--max_seq_length""" ,default=128 ,type=a_ ,help=( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) ,) parser.add_argument( """--task""" ,default="""""" ,type=a_ ,required=a_ ,help="""The GLUE task to run""" ,) parser.add_argument( """--gpus""" ,default=0 ,type=a_ ,help="""The number of GPUs allocated for this, it is by default 0 meaning none""" ,) parser.add_argument( """--overwrite_cache""" ,action="""store_true""" ,help="""Overwrite the cached training and evaluation sets""" ) return parser def snake_case_ ( )-> Tuple: '''simple docstring''' _UpperCAmelCase : Optional[Any] = argparse.ArgumentParser() add_generic_args(lowerCAmelCase_ , os.getcwd() ) _UpperCAmelCase : Optional[int] = GLUETransformer.add_model_specific_args(lowerCAmelCase_ , os.getcwd() ) _UpperCAmelCase : Optional[int] = parser.parse_args() # If output_dir not provided, a folder will be generated in pwd if args.output_dir is None: _UpperCAmelCase : Optional[int] = os.path.join( """./results""" , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , ) os.makedirs(args.output_dir ) _UpperCAmelCase : int = GLUETransformer(lowerCAmelCase_ ) _UpperCAmelCase : Any = generic_train(lowerCAmelCase_ , lowerCAmelCase_ ) # Optionally, predict on dev set and write to output_dir if args.do_predict: _UpperCAmelCase : int = sorted(glob.glob(os.path.join(args.output_dir , """checkpoint-epoch=*.ckpt""" ) , recursive=lowerCAmelCase_ ) ) _UpperCAmelCase : int = model.load_from_checkpoint(checkpoints[-1] ) return trainer.test(lowerCAmelCase_ ) if __name__ == "__main__": main()

import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import PoolFormerImageProcessor class __lowerCAmelCase ( unittest.TestCase ): def __init__( self :int , __magic_name__ :Union[str, Any] , __magic_name__ :Optional[Any]=7 , __magic_name__ :List[str]=3 , __magic_name__ :str=30 , __magic_name__ :Dict=400 , __magic_name__ :Union[str, Any]=True , __magic_name__ :Tuple=None , __magic_name__ :int=0.9 , __magic_name__ :Optional[Any]=None , __magic_name__ :List[Any]=True , __magic_name__ :Tuple=[0.5, 0.5, 0.5] , __magic_name__ :Dict=[0.5, 0.5, 0.5] , ): '''simple docstring''' a = size if size is not None else {"""shortest_edge""": 30} a = crop_size if crop_size is not None else {"""height""": 30, """width""": 30} a = parent a = batch_size a = num_channels a = min_resolution a = max_resolution a = do_resize_and_center_crop a = size a = crop_pct a = crop_size a = do_normalize a = image_mean a = image_std def lowerCamelCase__ ( self :Optional[int] ): '''simple docstring''' return { "size": self.size, "do_resize_and_center_crop": self.do_resize_and_center_crop, "crop_pct": self.crop_pct, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } @require_torch @require_vision class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ): UpperCamelCase__ = PoolFormerImageProcessor if is_vision_available() else None def lowerCamelCase__ ( self :List[str] ): '''simple docstring''' a = PoolFormerImageProcessingTester(self ) @property def lowerCamelCase__ ( self :List[Any] ): '''simple docstring''' return self.image_processor_tester.prepare_image_processor_dict() def lowerCamelCase__ ( self :Any ): '''simple docstring''' a = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__magic_name__ , """do_resize_and_center_crop""" ) ) self.assertTrue(hasattr(__magic_name__ , """size""" ) ) self.assertTrue(hasattr(__magic_name__ , """crop_pct""" ) ) self.assertTrue(hasattr(__magic_name__ , """do_normalize""" ) ) self.assertTrue(hasattr(__magic_name__ , """image_mean""" ) ) self.assertTrue(hasattr(__magic_name__ , """image_std""" ) ) def lowerCamelCase__ ( self :Union[str, Any] ): '''simple docstring''' a = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {"""shortest_edge""": 30} ) self.assertEqual(image_processor.crop_size , {"""height""": 30, """width""": 30} ) a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 ) self.assertEqual(image_processor.size , {"""shortest_edge""": 42} ) self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} ) def lowerCamelCase__ ( self :List[Any] ): '''simple docstring''' pass def lowerCamelCase__ ( self :Tuple ): '''simple docstring''' a = self.image_processing_class(**self.image_processor_dict ) # create random PIL images a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ ) for image in image_inputs: self.assertIsInstance(__magic_name__ , Image.Image ) # Test not batched input a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) # Test batched a = image_processing(__magic_name__ , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) def lowerCamelCase__ ( self :int ): '''simple docstring''' a = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , numpify=__magic_name__ ) for image in image_inputs: self.assertIsInstance(__magic_name__ , np.ndarray ) # Test not batched input a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) # Test batched a = image_processing(__magic_name__ , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) def lowerCamelCase__ ( self :Optional[Any] ): '''simple docstring''' a = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__magic_name__ , torchify=__magic_name__ ) for image in image_inputs: self.assertIsInstance(__magic_name__ , torch.Tensor ) # Test not batched input a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) # Test batched a = image_processing(__magic_name__ , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , )

import json import os import unittest from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ): UpperCamelCase__ = GPTaTokenizer UpperCamelCase__ = GPTaTokenizerFast UpperCamelCase__ = True UpperCamelCase__ = {'''add_prefix_space''': True} UpperCamelCase__ = False def lowerCamelCase__ ( self :Optional[Any] ): '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt a = [ """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """\u0120""", """\u0120l""", """\u0120n""", """\u0120lo""", """\u0120low""", """er""", """\u0120lowest""", """\u0120newer""", """\u0120wider""", """<unk>""", """<|endoftext|>""", ] a = dict(zip(__magic_name__ , range(len(__magic_name__ ) ) ) ) a = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""] a = {"""unk_token""": """<unk>"""} a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(__magic_name__ ) + """\n""" ) with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp: fp.write("""\n""".join(__magic_name__ ) ) def lowerCamelCase__ ( self :Dict , **__magic_name__ :List[str] ): '''simple docstring''' kwargs.update(self.special_tokens_map ) return GPTaTokenizer.from_pretrained(self.tmpdirname , **__magic_name__ ) def lowerCamelCase__ ( self :List[str] , **__magic_name__ :Optional[Any] ): '''simple docstring''' kwargs.update(self.special_tokens_map ) return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **__magic_name__ ) def lowerCamelCase__ ( self :Dict , __magic_name__ :List[str] ): '''simple docstring''' a = """lower newer""" a = """lower newer""" return input_text, output_text def lowerCamelCase__ ( self :int ): '''simple docstring''' a = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map ) a = """lower newer""" a = ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""] a = tokenizer.tokenize(__magic_name__ , add_prefix_space=__magic_name__ ) self.assertListEqual(__magic_name__ , __magic_name__ ) a = tokens + [tokenizer.unk_token] a = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ ) def lowerCamelCase__ ( self :int ): '''simple docstring''' if not self.test_rust_tokenizer: return a = self.get_tokenizer() a = self.get_rust_tokenizer(add_prefix_space=__magic_name__ ) a = """lower newer""" # Testing tokenization a = tokenizer.tokenize(__magic_name__ , add_prefix_space=__magic_name__ ) a = rust_tokenizer.tokenize(__magic_name__ ) self.assertListEqual(__magic_name__ , __magic_name__ ) # Testing conversion to ids without special tokens a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ , add_prefix_space=__magic_name__ ) a = rust_tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ ) self.assertListEqual(__magic_name__ , __magic_name__ ) # Testing conversion to ids with special tokens a = self.get_rust_tokenizer(add_prefix_space=__magic_name__ ) a = tokenizer.encode(__magic_name__ , add_prefix_space=__magic_name__ ) a = rust_tokenizer.encode(__magic_name__ ) self.assertListEqual(__magic_name__ , __magic_name__ ) # Testing the unknown token a = tokens + [rust_tokenizer.unk_token] a = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__magic_name__ ) , __magic_name__ ) def lowerCamelCase__ ( self :Optional[int] , *__magic_name__ :Tuple , **__magic_name__ :str ): '''simple docstring''' pass def lowerCamelCase__ ( self :Optional[Any] , __magic_name__ :Union[str, Any]=15 ): '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ): a = self.rust_tokenizer_class.from_pretrained(__magic_name__ , **__magic_name__ ) # Simple input a = """This is a simple input""" a = ["""This is a simple input 1""", """This is a simple input 2"""] a = ("""This is a simple input""", """This is a pair""") a = [ ("""This is a simple input 1""", """This is a simple input 2"""), ("""This is a simple pair 1""", """This is a simple pair 2"""), ] # Simple input tests self.assertRaises(__magic_name__ , tokenizer_r.encode , __magic_name__ , max_length=__magic_name__ , padding="""max_length""" ) # Simple input self.assertRaises(__magic_name__ , tokenizer_r.encode_plus , __magic_name__ , max_length=__magic_name__ , padding="""max_length""" ) # Simple input self.assertRaises( __magic_name__ , tokenizer_r.batch_encode_plus , __magic_name__ , max_length=__magic_name__ , padding="""max_length""" , ) # Pair input self.assertRaises(__magic_name__ , tokenizer_r.encode , __magic_name__ , max_length=__magic_name__ , padding="""max_length""" ) # Pair input self.assertRaises(__magic_name__ , tokenizer_r.encode_plus , __magic_name__ , max_length=__magic_name__ , padding="""max_length""" ) # Pair input self.assertRaises( __magic_name__ , tokenizer_r.batch_encode_plus , __magic_name__ , max_length=__magic_name__ , padding="""max_length""" , ) def lowerCamelCase__ ( self :str ): '''simple docstring''' a = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token="""<pad>""" ) # Simple input a = """This is a simple input""" a = ["""This is a simple input looooooooong""", """This is a simple input"""] a = ("""This is a simple input""", """This is a pair""") a = [ ("""This is a simple input loooooong""", """This is a simple input"""), ("""This is a simple pair loooooong""", """This is a simple pair"""), ] a = tokenizer.pad_token_id a = tokenizer(__magic_name__ , padding="""max_length""" , max_length=30 , return_tensors="""np""" ) a = tokenizer(__magic_name__ , padding=__magic_name__ , truncate=__magic_name__ , return_tensors="""np""" ) a = tokenizer(*__magic_name__ , padding="""max_length""" , max_length=60 , return_tensors="""np""" ) a = tokenizer(__magic_name__ , padding=__magic_name__ , truncate=__magic_name__ , return_tensors="""np""" ) # s # test single string max_length padding self.assertEqual(out_s["""input_ids"""].shape[-1] , 30 ) self.assertTrue(pad_token_id in out_s["""input_ids"""] ) self.assertTrue(0 in out_s["""attention_mask"""] ) # s2 # test automatic padding self.assertEqual(out_sa["""input_ids"""].shape[-1] , 33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] ) self.assertFalse(0 in out_sa["""attention_mask"""][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] ) self.assertTrue(0 in out_sa["""attention_mask"""][1] ) # p # test single pair max_length padding self.assertEqual(out_p["""input_ids"""].shape[-1] , 60 ) self.assertTrue(pad_token_id in out_p["""input_ids"""] ) self.assertTrue(0 in out_p["""attention_mask"""] ) # p2 # test automatic padding pair self.assertEqual(out_pa["""input_ids"""].shape[-1] , 52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] ) self.assertFalse(0 in out_pa["""attention_mask"""][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] ) self.assertTrue(0 in out_pa["""attention_mask"""][1] ) def lowerCamelCase__ ( self :Union[str, Any] ): '''simple docstring''' a = """$$$""" a = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=__magic_name__ , add_bos_token=__magic_name__ ) a = """This is a simple input""" a = ["""This is a simple input 1""", """This is a simple input 2"""] a = tokenizer.bos_token_id a = tokenizer(__magic_name__ ) a = tokenizer(__magic_name__ ) self.assertEqual(out_s.input_ids[0] , __magic_name__ ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) a = tokenizer.decode(out_s.input_ids ) a = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] , __magic_name__ ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) def lowerCamelCase__ ( self :Optional[int] ): '''simple docstring''' pass def lowerCamelCase__ ( self :Any ): '''simple docstring''' a = [self.get_tokenizer(do_lower_case=__magic_name__ , add_bos_token=__magic_name__ )] for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): a = """Encode this.""" a = """This one too please.""" a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ ) encoded_sequence += tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ ) a = tokenizer.encode_plus( __magic_name__ , __magic_name__ , add_special_tokens=__magic_name__ , return_special_tokens_mask=__magic_name__ , ) a = encoded_sequence_dict["""input_ids"""] a = encoded_sequence_dict["""special_tokens_mask"""] self.assertEqual(len(__magic_name__ ) , len(__magic_name__ ) ) a = [ (x if not special_tokens_mask[i] else None) for i, x in enumerate(__magic_name__ ) ] a = [x for x in filtered_sequence if x is not None] self.assertEqual(__magic_name__ , __magic_name__ ) @require_tokenizers class __lowerCAmelCase ( unittest.TestCase ): def lowerCamelCase__ ( self :Optional[int] ): '''simple docstring''' a = AutoTokenizer.from_pretrained("""facebook/opt-350m""" , from_slow=__magic_name__ ) a = """A photo of a cat""" a = tokenizer.encode( __magic_name__ , ) self.assertEqual(__magic_name__ , [2, 250, 1345, 9, 10, 4758] ) tokenizer.save_pretrained("""test_opt""" ) a = AutoTokenizer.from_pretrained("""./test_opt""" ) a = tokenizer.encode( __magic_name__ , ) self.assertEqual(__magic_name__ , [2, 250, 1345, 9, 10, 4758] ) def lowerCamelCase__ ( self :Union[str, Any] ): '''simple docstring''' a = AutoTokenizer.from_pretrained("""facebook/opt-350m""" , use_slow=__magic_name__ ) a = """A photo of a cat""" a = tokenizer.encode( __magic_name__ , ) # Same as above self.assertEqual(__magic_name__ , [2, 250, 1345, 9, 10, 4758] ) @unittest.skip("""This test is failing because of a bug in the fast tokenizer""" ) def lowerCamelCase__ ( self :str ): '''simple docstring''' a = AutoTokenizer.from_pretrained("""facebook/opt-350m""" , from_slow=__magic_name__ ) a = """bos""" a = tokenizer.get_vocab()["""bos"""] a = """A photo of a cat""" a = tokenizer.encode( __magic_name__ , ) # We changed the bos token self.assertEqual(__magic_name__ , [3_1957, 250, 1345, 9, 10, 4758] ) tokenizer.save_pretrained("""./tok""" ) a = AutoTokenizer.from_pretrained("""./tok""" ) self.assertTrue(tokenizer.is_fast ) a = tokenizer.encode( __magic_name__ , ) self.assertEqual(__magic_name__ , [3_1957, 250, 1345, 9, 10, 4758] )

from diffusers.utils.testing_utils import require_onnxruntime @require_onnxruntime class _UpperCamelCase : '''simple docstring''' pass

import json import os import unittest from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer from ...test_tokenization_common import TokenizerTesterMixin class _UpperCamelCase ( _A , unittest.TestCase ): '''simple docstring''' __UpperCamelCase : Tuple = CTRLTokenizer __UpperCamelCase : int = False __UpperCamelCase : List[str] = False def lowerCAmelCase__ ( self : List[str] ): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt UpperCamelCase_: int = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""] UpperCamelCase_: int = dict(zip(snake_case_ , range(len(snake_case_ ) ) ) ) UpperCamelCase_: Union[str, Any] = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""] UpperCamelCase_: Tuple = {"""unk_token""": """<unk>"""} UpperCamelCase_: Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) UpperCamelCase_: Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(snake_case_ ) + """\n""" ) with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp: fp.write("""\n""".join(snake_case_ ) ) def lowerCAmelCase__ ( self : Optional[int] , **snake_case_ : int ): kwargs.update(self.special_tokens_map ) return CTRLTokenizer.from_pretrained(self.tmpdirname , **snake_case_ ) def lowerCAmelCase__ ( self : Optional[int] , snake_case_ : List[str] ): UpperCamelCase_: Dict = """adapt react readapt apt""" UpperCamelCase_: List[str] = """adapt react readapt apt""" return input_text, output_text def lowerCAmelCase__ ( self : List[str] ): UpperCamelCase_: str = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map ) UpperCamelCase_: List[Any] = """adapt react readapt apt""" UpperCamelCase_: Optional[int] = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split() UpperCamelCase_: int = tokenizer.tokenize(snake_case_ ) self.assertListEqual(snake_case_ , snake_case_ ) UpperCamelCase_: List[Any] = tokens + [tokenizer.unk_token] UpperCamelCase_: Union[str, Any] = [0, 1, 2, 4, 5, 1, 0, 3, 6] self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ ) , snake_case_ )

from __future__ import annotations from collections.abc import Iterable, Iterator from dataclasses import dataclass A__ : str = (3, 9, -11, 0, 7, 5, 1, -1) A__ : int = (4, 6, 2, 0, 8, 10, 3, -2) @dataclass class _UpperCAmelCase : """simple docstring""" lowercase__ = 42 lowercase__ = 42 class _UpperCAmelCase : """simple docstring""" def __init__( self : Optional[int], lowerCamelCase : Iterable[int] ): '''simple docstring''' lowercase__ = None for i in sorted(lowerCamelCase, reverse=lowerCamelCase ): lowercase__ = Node(lowerCamelCase, self.head ) def __iter__( self : Union[str, Any] ): '''simple docstring''' lowercase__ = self.head while node: yield node.data lowercase__ = node.next_node def __len__( self : Optional[Any] ): '''simple docstring''' return sum(1 for _ in self ) def __str__( self : Any ): '''simple docstring''' return " -> ".join([str(lowerCamelCase ) for node in self] ) def a ( lowerCamelCase_ , lowerCamelCase_ ): '''simple docstring''' return SortedLinkedList(list(lowerCamelCase_ ) + list(lowerCamelCase_ ) ) if __name__ == "__main__": import doctest doctest.testmod() A__ : int = SortedLinkedList print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

import warnings warnings.warn( 'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: ' '`from accelerate import find_executable_batch_size` to avoid this warning.', FutureWarning, )

from typing import List, Optional, Union import torch from ...models import UNetaDConditionModel, VQModel from ...pipelines import DiffusionPipeline from ...pipelines.pipeline_utils import ImagePipelineOutput from ...schedulers import DDPMScheduler from ...utils import ( is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) lowerCamelCase : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name lowerCamelCase : Dict = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co./datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n' def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase=8 ) -> Optional[int]: snake_case : Dict = height // scale_factor**2 if height % scale_factor**2 != 0: new_height += 1 snake_case : int = width // scale_factor**2 if width % scale_factor**2 != 0: new_width += 1 return new_height * scale_factor, new_width * scale_factor class __lowercase (UpperCamelCase__ ): """simple docstring""" def __init__( self , A , A , A , ) -> int: super().__init__() self.register_modules( unet=A , scheduler=A , movq=A , ) snake_case : Tuple = 2 ** (len(self.movq.config.block_out_channels ) - 1) def UpperCAmelCase ( self , A , A , A , A , A , A ) -> str: if latents is None: snake_case : List[str] = randn_tensor(A , generator=A , device=A , dtype=A ) else: if latents.shape != shape: raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" ) snake_case : Union[str, Any] = latents.to(A ) snake_case : Optional[Any] = latents * scheduler.init_noise_sigma return latents def UpperCAmelCase ( self , A=0 ) -> List[str]: if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("""Please install accelerate via `pip install accelerate`""" ) snake_case : Dict = torch.device(f"""cuda:{gpu_id}""" ) snake_case : Tuple = [ self.unet, self.movq, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(A , A ) def UpperCAmelCase ( self , A=0 ) -> Optional[Any]: if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ): from accelerate import cpu_offload_with_hook else: raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" ) snake_case : int = torch.device(f"""cuda:{gpu_id}""" ) if self.device.type != "cpu": self.to("""cpu""" , silence_dtype_warnings=A ) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) snake_case : Tuple = None for cpu_offloaded_model in [self.unet, self.movq]: snake_case : Tuple = cpu_offload_with_hook(A , A , prev_module_hook=A ) # We'll offload the last model manually. snake_case : Tuple = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def UpperCAmelCase ( self ) -> str: if not hasattr(self.unet , """_hf_hook""" ): return self.device for module in self.unet.modules(): if ( hasattr(A , """_hf_hook""" ) and hasattr(module._hf_hook , """execution_device""" ) and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device ) return self.device @torch.no_grad() @replace_example_docstring(A ) def __call__( self , A , A , A , A = 5_1_2 , A = 5_1_2 , A = 1_0_0 , A = 4.0 , A = 1 , A = None , A = None , A = "pil" , A = True , ) -> Any: snake_case : int = self._execution_device snake_case : Tuple = guidance_scale > 1.0 if isinstance(A , A ): snake_case : str = torch.cat(A , dim=0 ) if isinstance(A , A ): snake_case : int = torch.cat(A , dim=0 ) if isinstance(A , A ): snake_case : Tuple = torch.cat(A , dim=0 ) snake_case : List[Any] = image_embeds.shape[0] * num_images_per_prompt if do_classifier_free_guidance: snake_case : Optional[int] = image_embeds.repeat_interleave(A , dim=0 ) snake_case : Dict = negative_image_embeds.repeat_interleave(A , dim=0 ) snake_case : Optional[Any] = hint.repeat_interleave(A , dim=0 ) snake_case : List[str] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=A ) snake_case : Optional[Any] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=A ) self.scheduler.set_timesteps(A , device=A ) snake_case : Tuple = self.scheduler.timesteps snake_case : Tuple = self.movq.config.latent_channels snake_case : Optional[int] = downscale_height_and_width(A , A , self.movq_scale_factor ) # create initial latent snake_case : str = self.prepare_latents( (batch_size, num_channels_latents, height, width) , image_embeds.dtype , A , A , A , self.scheduler , ) for i, t in enumerate(self.progress_bar(A ) ): # expand the latents if we are doing classifier free guidance snake_case : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents snake_case : Any = {"""image_embeds""": image_embeds, """hint""": hint} snake_case : List[Any] = self.unet( sample=A , timestep=A , encoder_hidden_states=A , added_cond_kwargs=A , return_dict=A , )[0] if do_classifier_free_guidance: snake_case : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 ) snake_case : Tuple = noise_pred.chunk(2 ) snake_case : Optional[Any] = variance_pred.chunk(2 ) snake_case : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) snake_case : Dict = torch.cat([noise_pred, variance_pred_text] , dim=1 ) if not ( hasattr(self.scheduler.config , """variance_type""" ) and self.scheduler.config.variance_type in ["learned", "learned_range"] ): snake_case : int = noise_pred.split(latents.shape[1] , dim=1 ) # compute the previous noisy sample x_t -> x_t-1 snake_case : Dict = self.scheduler.step( A , A , A , generator=A , )[0] # post-processing snake_case : Tuple = self.movq.decode(A , force_not_quantize=A )["""sample"""] if output_type not in ["pt", "np", "pil"]: raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" ) if output_type in ["np", "pil"]: snake_case : Optional[int] = image * 0.5 + 0.5 snake_case : Union[str, Any] = image.clamp(0 , 1 ) snake_case : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy() if output_type == "pil": snake_case : Tuple = self.numpy_to_pil(A ) if not return_dict: return (image,) return ImagePipelineOutput(images=A )

from typing import List, Optional, Union import torch from ...models import UNetaDConditionModel, VQModel from ...pipelines import DiffusionPipeline from ...pipelines.pipeline_utils import ImagePipelineOutput from ...schedulers import DDPMScheduler from ...utils import ( is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) lowerCamelCase : Tuple = logging.get_logger(__name__) # pylint: disable=invalid-name lowerCamelCase : Dict = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co./datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n' def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase=8 ) -> Optional[int]: snake_case : Dict = height // scale_factor**2 if height % scale_factor**2 != 0: new_height += 1 snake_case : int = width // scale_factor**2 if width % scale_factor**2 != 0: new_width += 1 return new_height * scale_factor, new_width * scale_factor class __lowercase (UpperCamelCase__ ): """simple docstring""" def __init__( self , A , A , A , ) -> int: super().__init__() self.register_modules( unet=A , scheduler=A , movq=A , ) snake_case : Tuple = 2 ** (len(self.movq.config.block_out_channels ) - 1) def UpperCAmelCase ( self , A , A , A , A , A , A ) -> str: if latents is None: snake_case : List[str] = randn_tensor(A , generator=A , device=A , dtype=A ) else: if latents.shape != shape: raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" ) snake_case : Union[str, Any] = latents.to(A ) snake_case : Optional[Any] = latents * scheduler.init_noise_sigma return latents def UpperCAmelCase ( self , A=0 ) -> List[str]: if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("""Please install accelerate via `pip install accelerate`""" ) snake_case : Dict = torch.device(f"""cuda:{gpu_id}""" ) snake_case : Tuple = [ self.unet, self.movq, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(A , A ) def UpperCAmelCase ( self , A=0 ) -> Optional[Any]: if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ): from accelerate import cpu_offload_with_hook else: raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" ) snake_case : int = torch.device(f"""cuda:{gpu_id}""" ) if self.device.type != "cpu": self.to("""cpu""" , silence_dtype_warnings=A ) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) snake_case : Tuple = None for cpu_offloaded_model in [self.unet, self.movq]: snake_case , snake_case : Tuple = cpu_offload_with_hook(A , A , prev_module_hook=A ) # We'll offload the last model manually. snake_case : Tuple = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def UpperCAmelCase ( self ) -> str: if not hasattr(self.unet , """_hf_hook""" ): return self.device for module in self.unet.modules(): if ( hasattr(A , """_hf_hook""" ) and hasattr(module._hf_hook , """execution_device""" ) and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device ) return self.device @torch.no_grad() @replace_example_docstring(A ) def __call__( self , A , A , A , A = 5_1_2 , A = 5_1_2 , A = 1_0_0 , A = 4.0 , A = 1 , A = None , A = None , A = "pil" , A = True , ) -> Any: snake_case : int = self._execution_device snake_case : Tuple = guidance_scale > 1.0 if isinstance(A , A ): snake_case : str = torch.cat(A , dim=0 ) if isinstance(A , A ): snake_case : int = torch.cat(A , dim=0 ) if isinstance(A , A ): snake_case : Tuple = torch.cat(A , dim=0 ) snake_case : List[Any] = image_embeds.shape[0] * num_images_per_prompt if do_classifier_free_guidance: snake_case : Optional[int] = image_embeds.repeat_interleave(A , dim=0 ) snake_case : Dict = negative_image_embeds.repeat_interleave(A , dim=0 ) snake_case : Optional[Any] = hint.repeat_interleave(A , dim=0 ) snake_case : List[str] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=A ) snake_case : Optional[Any] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=A ) self.scheduler.set_timesteps(A , device=A ) snake_case : Tuple = self.scheduler.timesteps snake_case : Tuple = self.movq.config.latent_channels snake_case , snake_case : Optional[int] = downscale_height_and_width(A , A , self.movq_scale_factor ) # create initial latent snake_case : str = self.prepare_latents( (batch_size, num_channels_latents, height, width) , image_embeds.dtype , A , A , A , self.scheduler , ) for i, t in enumerate(self.progress_bar(A ) ): # expand the latents if we are doing classifier free guidance snake_case : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents snake_case : Any = {"""image_embeds""": image_embeds, """hint""": hint} snake_case : List[Any] = self.unet( sample=A , timestep=A , encoder_hidden_states=A , added_cond_kwargs=A , return_dict=A , )[0] if do_classifier_free_guidance: snake_case , snake_case : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 ) snake_case , snake_case : Tuple = noise_pred.chunk(2 ) snake_case , snake_case : Optional[Any] = variance_pred.chunk(2 ) snake_case : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) snake_case : Dict = torch.cat([noise_pred, variance_pred_text] , dim=1 ) if not ( hasattr(self.scheduler.config , """variance_type""" ) and self.scheduler.config.variance_type in ["learned", "learned_range"] ): snake_case , snake_case : int = noise_pred.split(latents.shape[1] , dim=1 ) # compute the previous noisy sample x_t -> x_t-1 snake_case : Dict = self.scheduler.step( A , A , A , generator=A , )[0] # post-processing snake_case : Tuple = self.movq.decode(A , force_not_quantize=A )["""sample"""] if output_type not in ["pt", "np", "pil"]: raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" ) if output_type in ["np", "pil"]: snake_case : Optional[int] = image * 0.5 + 0.5 snake_case : Union[str, Any] = image.clamp(0 , 1 ) snake_case : List[str] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy() if output_type == "pil": snake_case : Tuple = self.numpy_to_pil(A ) if not return_dict: return (image,) return ImagePipelineOutput(images=A )

"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) lowercase__ = { """configuration_blip""": [ """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BlipConfig""", """BlipTextConfig""", """BlipVisionConfig""", ], """processing_blip""": ["""BlipProcessor"""], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ = ["""BlipImageProcessor"""] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ = [ """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""", """BlipModel""", """BlipPreTrainedModel""", """BlipForConditionalGeneration""", """BlipForQuestionAnswering""", """BlipVisionModel""", """BlipTextModel""", """BlipForImageTextRetrieval""", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ = [ """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""", """TFBlipModel""", """TFBlipPreTrainedModel""", """TFBlipForConditionalGeneration""", """TFBlipForQuestionAnswering""", """TFBlipVisionModel""", """TFBlipTextModel""", """TFBlipForImageTextRetrieval""", ] if TYPE_CHECKING: from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig from .processing_blip import BlipProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_blip import BlipImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_blip import ( BLIP_PRETRAINED_MODEL_ARCHIVE_LIST, BlipForConditionalGeneration, BlipForImageTextRetrieval, BlipForQuestionAnswering, BlipModel, BlipPreTrainedModel, BlipTextModel, BlipVisionModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_blip import ( TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST, TFBlipForConditionalGeneration, TFBlipForImageTextRetrieval, TFBlipForQuestionAnswering, TFBlipModel, TFBlipPreTrainedModel, TFBlipTextModel, TFBlipVisionModel, ) else: import sys lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring""" def _snake_case ( lowercase__ , lowercase__ ): # "extended trapezoidal rule" # int(f) = dx/2 * (f1 + 2f2 + ... + fn) _lowerCamelCase : List[Any] = (boundary[1] - boundary[0]) / steps _lowerCamelCase : Tuple = boundary[0] _lowerCamelCase : Dict = boundary[1] _lowerCamelCase : List[Any] = make_points(lowercase__ , lowercase__ , lowercase__ ) _lowerCamelCase : List[Any] = 0.0 y += (h / 2.0) * f(lowercase__ ) for i in x_i: # print(i) y += h * f(lowercase__ ) y += (h / 2.0) * f(lowercase__ ) return y def _snake_case ( lowercase__ , lowercase__ , lowercase__ ): _lowerCamelCase : str = a + h while x < (b - h): yield x _lowerCamelCase : int = x + h def _snake_case ( lowercase__ ): # enter your function here _lowerCamelCase : Optional[Any] = (x - 0) * (x - 0) return y def _snake_case ( ): _lowerCamelCase : int = 0.0 # Lower bound of integration _lowerCamelCase : Optional[int] = 1.0 # Upper bound of integration _lowerCamelCase : List[str] = 1_0.0 # define number of steps or resolution _lowerCamelCase : List[Any] = [a, b] # define boundary of integration _lowerCamelCase : Optional[Any] = method_a(lowercase__ , lowercase__ ) print(f'''y = {y}''' ) if __name__ == "__main__": main()

from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging if TYPE_CHECKING: from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType lowerCAmelCase__ = logging.get_logger(__name__) lowerCAmelCase__ = { '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co./microsoft/deberta-v2-xlarge/resolve/main/config.json''', '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co./microsoft/deberta-v2-xxlarge/resolve/main/config.json''', '''microsoft/deberta-v2-xlarge-mnli''': ( '''https://huggingface.co./microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json''' ), '''microsoft/deberta-v2-xxlarge-mnli''': ( '''https://huggingface.co./microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json''' ), } class snake_case__(_UpperCamelCase ): """simple docstring""" lowercase_ = """deberta-v2""" def __init__( self : Dict , SCREAMING_SNAKE_CASE : Any=128_100 , SCREAMING_SNAKE_CASE : str=1_536 , SCREAMING_SNAKE_CASE : List[str]=24 , SCREAMING_SNAKE_CASE : List[str]=24 , SCREAMING_SNAKE_CASE : Any=6_144 , SCREAMING_SNAKE_CASE : Dict="gelu" , SCREAMING_SNAKE_CASE : Tuple=0.1 , SCREAMING_SNAKE_CASE : int=0.1 , SCREAMING_SNAKE_CASE : Any=512 , SCREAMING_SNAKE_CASE : Optional[int]=0 , SCREAMING_SNAKE_CASE : int=0.02 , SCREAMING_SNAKE_CASE : Tuple=1E-7 , SCREAMING_SNAKE_CASE : str=False , SCREAMING_SNAKE_CASE : Optional[int]=-1 , SCREAMING_SNAKE_CASE : List[str]=0 , SCREAMING_SNAKE_CASE : str=True , SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : List[Any]=0 , SCREAMING_SNAKE_CASE : str="gelu" , **SCREAMING_SNAKE_CASE : Union[str, Any] , ): super().__init__(**SCREAMING_SNAKE_CASE ) lowercase__ : List[str] = hidden_size lowercase__ : Union[str, Any] = num_hidden_layers lowercase__ : List[Any] = num_attention_heads lowercase__ : Optional[int] = intermediate_size lowercase__ : int = hidden_act lowercase__ : str = hidden_dropout_prob lowercase__ : List[str] = attention_probs_dropout_prob lowercase__ : Dict = max_position_embeddings lowercase__ : Tuple = type_vocab_size lowercase__ : Optional[Any] = initializer_range lowercase__ : List[str] = relative_attention lowercase__ : str = max_relative_positions lowercase__ : List[str] = pad_token_id lowercase__ : int = position_biased_input # Backwards compatibility if type(SCREAMING_SNAKE_CASE ) == str: lowercase__ : Dict = [x.strip() for x in pos_att_type.lower().split("|" )] lowercase__ : List[Any] = pos_att_type lowercase__ : int = vocab_size lowercase__ : Dict = layer_norm_eps lowercase__ : str = kwargs.get("pooler_hidden_size" , SCREAMING_SNAKE_CASE ) lowercase__ : Optional[int] = pooler_dropout lowercase__ : str = pooler_hidden_act class snake_case__(_UpperCamelCase ): """simple docstring""" @property def snake_case ( self : Optional[Any] ): if self.task == "multiple-choice": lowercase__ : Tuple = {0: "batch", 1: "choice", 2: "sequence"} else: lowercase__ : Any = {0: "batch", 1: "sequence"} if self._config.type_vocab_size > 0: return OrderedDict( [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] ) else: return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] ) @property def snake_case ( self : Tuple ): return 12 def snake_case ( self : Tuple , SCREAMING_SNAKE_CASE : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : int = -1 , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : Optional["TensorType"] = None , SCREAMING_SNAKE_CASE : int = 3 , SCREAMING_SNAKE_CASE : int = 40 , SCREAMING_SNAKE_CASE : int = 40 , SCREAMING_SNAKE_CASE : "PreTrainedTokenizerBase" = None , ): lowercase__ : Dict = super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE ) if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs: del dummy_inputs["token_type_ids"] return dummy_inputs

import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class snake_case__(unittest.TestCase ): """simple docstring""" @property def snake_case ( self : Any ): torch.manual_seed(0 ) lowercase__ : Tuple = UNetaDModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , ) return model @property def snake_case ( self : List[str] ): torch.manual_seed(0 ) lowercase__ : Optional[int] = VQModel( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , ) return model @property def snake_case ( self : Dict ): torch.manual_seed(0 ) lowercase__ : str = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) return CLIPTextModel(SCREAMING_SNAKE_CASE ) def snake_case ( self : str ): lowercase__ : Any = self.dummy_uncond_unet lowercase__ : Dict = DDIMScheduler() lowercase__ : Optional[Any] = self.dummy_vq_model lowercase__ : Union[str, Any] = LDMPipeline(unet=SCREAMING_SNAKE_CASE , vqvae=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE ) ldm.to(SCREAMING_SNAKE_CASE ) ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE ) lowercase__ : int = torch.manual_seed(0 ) lowercase__ : Optional[int] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" ).images lowercase__ : str = torch.manual_seed(0 ) lowercase__ : List[Any] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" , return_dict=SCREAMING_SNAKE_CASE )[0] lowercase__ : Any = image[0, -3:, -3:, -1] lowercase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ : List[Any] = np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172] ) lowercase__ : Optional[Any] = 1E-2 if torch_device != "mps" else 3E-2 assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance @slow @require_torch class snake_case__(unittest.TestCase ): """simple docstring""" def snake_case ( self : Optional[Any] ): lowercase__ : int = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" ) ldm.to(SCREAMING_SNAKE_CASE ) ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE ) lowercase__ : Dict = torch.manual_seed(0 ) lowercase__ : Tuple = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=5 , output_type="numpy" ).images lowercase__ : Tuple = image[0, -3:, -3:, -1] assert image.shape == (1, 256, 256, 3) lowercase__ : Optional[Any] = np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447] ) lowercase__ : int = 1E-2 if torch_device != "mps" else 3E-2 assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance

import os import shutil import tempfile from unittest import TestCase from unittest.mock import patch import numpy as np from datasets import Dataset from transformers.models.realm.configuration_realm import RealmConfig from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ): '''simple docstring''' def UpperCamelCase_ ( self : List[Any] ): __A = tempfile.mkdtemp() __A = 5 # Realm tok __A = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "test", "question", "this", "is", "the", "first", "second", "third", "fourth", "fifth", "record", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] __A = os.path.join(self.tmpdirname ,"realm_tokenizer" ) os.makedirs(A ,exist_ok=A ) __A = os.path.join(A ,VOCAB_FILES_NAMES["vocab_file"] ) with open(self.vocab_file ,"w" ,encoding="utf-8" ) as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) ) __A = os.path.join(self.tmpdirname ,"realm_block_records" ) os.makedirs(A ,exist_ok=A ) def UpperCamelCase_ ( self : int ): return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"realm_tokenizer" ) ) def UpperCamelCase_ ( self : Optional[Any] ): shutil.rmtree(self.tmpdirname ) def UpperCamelCase_ ( self : int ): __A = RealmConfig(num_block_records=self.num_block_records ) return config def UpperCamelCase_ ( self : Union[str, Any] ): __A = Dataset.from_dict( { "id": ["0", "1"], "question": ["foo", "bar"], "answers": [["Foo", "Bar"], ["Bar"]], } ) return dataset def UpperCamelCase_ ( self : List[Any] ): __A = np.array( [ B"This is the first record", B"This is the second record", B"This is the third record", B"This is the fourth record", B"This is the fifth record", B"This is a longer longer longer record", ] ,dtype=A ,) return block_records def UpperCamelCase_ ( self : Tuple ): __A = RealmRetriever( block_records=self.get_dummy_block_records() ,tokenizer=self.get_tokenizer() ,) return retriever def UpperCamelCase_ ( self : Optional[int] ): __A = self.get_config() __A = self.get_dummy_retriever() __A = retriever.tokenizer __A = np.array([0, 3] ,dtype="long" ) __A = tokenizer(["Test question"] ).input_ids __A = tokenizer( ["the fourth"] ,add_special_tokens=A ,return_token_type_ids=A ,return_attention_mask=A ,).input_ids __A = config.reader_seq_len __A , __A , __A , __A = retriever( A ,A ,answer_ids=A ,max_length=A ,return_tensors="np" ) self.assertEqual(len(A ) ,2 ) self.assertEqual(len(A ) ,2 ) self.assertEqual(len(A ) ,2 ) self.assertEqual(concat_inputs.input_ids.shape ,(2, 10) ) self.assertEqual(concat_inputs.attention_mask.shape ,(2, 10) ) self.assertEqual(concat_inputs.token_type_ids.shape ,(2, 10) ) self.assertEqual(concat_inputs.special_tokens_mask.shape ,(2, 10) ) self.assertEqual( tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) ,["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"] ,) self.assertEqual( tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) ,["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"] ,) def UpperCamelCase_ ( self : Dict ): __A = self.get_config() __A = self.get_dummy_retriever() __A = retriever.tokenizer __A = np.array([0, 3, 5] ,dtype="long" ) __A = tokenizer(["Test question"] ).input_ids __A = tokenizer( ["the fourth", "longer longer"] ,add_special_tokens=A ,return_token_type_ids=A ,return_attention_mask=A ,).input_ids __A = config.reader_seq_len __A , __A , __A , __A = retriever( A ,A ,answer_ids=A ,max_length=A ,return_tensors="np" ) self.assertEqual([False, True, True] ,A ) self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] ,A ) self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] ,A ) def UpperCamelCase_ ( self : Tuple ): __A = self.get_dummy_retriever() retriever.save_pretrained(os.path.join(self.tmpdirname ,"realm_block_records" ) ) # Test local path __A = retriever.from_pretrained(os.path.join(self.tmpdirname ,"realm_block_records" ) ) self.assertEqual(retriever.block_records[0] ,B"This is the first record" ) # Test mocked remote path with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download: __A = os.path.join( os.path.join(self.tmpdirname ,"realm_block_records" ) ,_REALM_BLOCK_RECORDS_FILENAME ) __A = RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" ) self.assertEqual(retriever.block_records[0] ,B"This is the first record" )

from __future__ import annotations from fractions import Fraction from math import gcd, sqrt def A__ ( __lowerCamelCase ): SCREAMING_SNAKE_CASE_ = int(number**0.5 ) return number == sq * sq def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ): SCREAMING_SNAKE_CASE_ = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den SCREAMING_SNAKE_CASE_ = x_den * y_den * z_den SCREAMING_SNAKE_CASE_ = gcd(__lowerCamelCase, __lowerCamelCase ) top //= hcf bottom //= hcf return top, bottom def A__ ( __lowerCamelCase = 35 ): SCREAMING_SNAKE_CASE_ = set() SCREAMING_SNAKE_CASE_ = 42 SCREAMING_SNAKE_CASE_ = Fraction(0 ) SCREAMING_SNAKE_CASE_ = 42 for x_num in range(1, order + 1 ): for x_den in range(x_num + 1, order + 1 ): for y_num in range(1, order + 1 ): for y_den in range(y_num + 1, order + 1 ): # n=1 SCREAMING_SNAKE_CASE_ = x_num * y_den + x_den * y_num SCREAMING_SNAKE_CASE_ = x_den * y_den SCREAMING_SNAKE_CASE_ = gcd(__lowerCamelCase, __lowerCamelCase ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE_ = add_three( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ) unique_s.add(__lowerCamelCase ) # n=2 SCREAMING_SNAKE_CASE_ = ( x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num ) SCREAMING_SNAKE_CASE_ = x_den * x_den * y_den * y_den if is_sq(__lowerCamelCase ) and is_sq(__lowerCamelCase ): SCREAMING_SNAKE_CASE_ = int(sqrt(__lowerCamelCase ) ) SCREAMING_SNAKE_CASE_ = int(sqrt(__lowerCamelCase ) ) SCREAMING_SNAKE_CASE_ = gcd(__lowerCamelCase, __lowerCamelCase ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE_ = add_three( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ) unique_s.add(__lowerCamelCase ) # n=-1 SCREAMING_SNAKE_CASE_ = x_num * y_num SCREAMING_SNAKE_CASE_ = x_den * y_num + x_num * y_den SCREAMING_SNAKE_CASE_ = gcd(__lowerCamelCase, __lowerCamelCase ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE_ = add_three( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ) unique_s.add(__lowerCamelCase ) # n=2 SCREAMING_SNAKE_CASE_ = x_num * x_num * y_num * y_num SCREAMING_SNAKE_CASE_ = ( x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den ) if is_sq(__lowerCamelCase ) and is_sq(__lowerCamelCase ): SCREAMING_SNAKE_CASE_ = int(sqrt(__lowerCamelCase ) ) SCREAMING_SNAKE_CASE_ = int(sqrt(__lowerCamelCase ) ) SCREAMING_SNAKE_CASE_ = gcd(__lowerCamelCase, __lowerCamelCase ) z_num //= hcf z_den //= hcf if 0 < z_num < z_den <= order: SCREAMING_SNAKE_CASE_ = add_three( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ) unique_s.add(__lowerCamelCase ) for num, den in unique_s: total += Fraction(__lowerCamelCase, __lowerCamelCase ) return total.denominator + total.numerator if __name__ == "__main__": print(F"""{solution() = }""")

from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _lowerCamelCase = { 'configuration_squeezebert': [ 'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SqueezeBertConfig', 'SqueezeBertOnnxConfig', ], 'tokenization_squeezebert': ['SqueezeBertTokenizer'], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase = ['SqueezeBertTokenizerFast'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase = [ 'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST', 'SqueezeBertForMaskedLM', 'SqueezeBertForMultipleChoice', 'SqueezeBertForQuestionAnswering', 'SqueezeBertForSequenceClassification', 'SqueezeBertForTokenClassification', 'SqueezeBertModel', 'SqueezeBertModule', 'SqueezeBertPreTrainedModel', ] if TYPE_CHECKING: from .configuration_squeezebert import ( SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertOnnxConfig, ) from .tokenization_squeezebert import SqueezeBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) else: import sys _lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

import argparse from tax import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : str ) -> str: UpperCAmelCase_ = AutoConfig.from_pretrained(__UpperCamelCase ) UpperCAmelCase_ = FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase ) UpperCAmelCase_ = checkpoints.load_tax_checkpoint(__UpperCamelCase ) UpperCAmelCase_ = '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp'''] if config.model_type == "t5": UpperCAmelCase_ = '''SelfAttention''' if config.model_type == "longt5" and config.encoder_attention_type == "local": UpperCAmelCase_ = '''LocalSelfAttention''' elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCAmelCase_ = '''TransientGlobalSelfAttention''' else: raise ValueError( '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`''' ''' attribute with a value from [\'local\', \'transient-global].''' ) # Encoder for layer_index in range(config.num_layers ): UpperCAmelCase_ = f'layers_{str(__UpperCamelCase )}' # Self-Attention UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel'''] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale'''] # Layer Normalization UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale'''] if split_mlp_wi: UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel'''] else: UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel'''] # Layer Normalization UpperCAmelCase_ = tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale'''] # Assigning UpperCAmelCase_ = flax_model.params['''encoder''']['''block'''][str(__UpperCamelCase )]['''layer'''] UpperCAmelCase_ = tax_attention_key UpperCAmelCase_ = tax_attention_out UpperCAmelCase_ = tax_attention_query UpperCAmelCase_ = tax_attention_value UpperCAmelCase_ = tax_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCAmelCase_ = tax_global_layer_norm if split_mlp_wi: UpperCAmelCase_ = tax_mlp_wi_a UpperCAmelCase_ = tax_mlp_wi_a else: UpperCAmelCase_ = tax_mlp_wi UpperCAmelCase_ = tax_mlp_wo UpperCAmelCase_ = tax_mlp_layer_norm UpperCAmelCase_ = flax_model_encoder_layer_block # Only for layer 0: UpperCAmelCase_ = tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T UpperCAmelCase_ = tax_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": UpperCAmelCase_ = tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T UpperCAmelCase_ = tax_encoder_global_rel_embedding # Assigning UpperCAmelCase_ = tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale'''] UpperCAmelCase_ = tax_encoder_norm # Decoder for layer_index in range(config.num_layers ): UpperCAmelCase_ = f'layers_{str(__UpperCamelCase )}' # Self-Attention UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel'''] # Layer Normalization UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][ '''scale''' ] # Encoder-Decoder-Attention UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention'''] UpperCAmelCase_ = tax_enc_dec_attention_module['''key''']['''kernel'''] UpperCAmelCase_ = tax_enc_dec_attention_module['''out''']['''kernel'''] UpperCAmelCase_ = tax_enc_dec_attention_module['''query''']['''kernel'''] UpperCAmelCase_ = tax_enc_dec_attention_module['''value''']['''kernel'''] # Layer Normalization UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale'''] # MLP if split_mlp_wi: UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel'''] else: UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel'''] UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel'''] # Layer Normalization UpperCAmelCase_ = tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale'''] # Assigning UpperCAmelCase_ = flax_model.params['''decoder''']['''block'''][str(__UpperCamelCase )]['''layer'''] UpperCAmelCase_ = tax_attention_key UpperCAmelCase_ = tax_attention_out UpperCAmelCase_ = tax_attention_query UpperCAmelCase_ = tax_attention_value UpperCAmelCase_ = tax_pre_attention_layer_norm UpperCAmelCase_ = tax_enc_dec_attention_key UpperCAmelCase_ = tax_enc_dec_attention_out UpperCAmelCase_ = tax_enc_dec_attention_query UpperCAmelCase_ = tax_enc_dec_attention_value UpperCAmelCase_ = tax_cross_layer_norm if split_mlp_wi: UpperCAmelCase_ = tax_mlp_wi_a UpperCAmelCase_ = tax_mlp_wi_a else: UpperCAmelCase_ = tax_mlp_wi UpperCAmelCase_ = tax_mlp_wo UpperCAmelCase_ = txa_mlp_layer_norm UpperCAmelCase_ = flax_model_decoder_layer_block # Decoder Normalization UpperCAmelCase_ = tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale'''] UpperCAmelCase_ = txa_decoder_norm # Only for layer 0: UpperCAmelCase_ = tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T UpperCAmelCase_ = tax_decoder_rel_embedding # Token Embeddings UpperCAmelCase_ = tax_model['''target''']['''token_embedder''']['''embedding'''] UpperCAmelCase_ = txa_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in tax_model["target"]["decoder"]: UpperCAmelCase_ = tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel'''] flax_model.save_pretrained(__UpperCamelCase ) print('''T5X Model was sucessfully converted!''' ) if __name__ == "__main__": _lowerCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.' ) parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.') parser.add_argument( '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.' ) _lowerCamelCase = parser.parse_args() convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

from __future__ import annotations from collections.abc import Iterator from typing import Any class _snake_case : def __init__( self , a) -> List[str]: SCREAMING_SNAKE_CASE = data SCREAMING_SNAKE_CASE = None class _snake_case : def __init__( self) -> Optional[int]: SCREAMING_SNAKE_CASE = None SCREAMING_SNAKE_CASE = None def __iter__( self) -> Iterator[Any]: SCREAMING_SNAKE_CASE = self.head while self.head: yield node.data SCREAMING_SNAKE_CASE = node.next if node == self.head: break def __len__( self) -> int: return sum(1 for _ in self) def __repr__( self) -> Optional[int]: return "->".join(str(a) for item in iter(self)) def SCREAMING_SNAKE_CASE__ ( self , a) -> None: self.insert_nth(len(self) , a) def SCREAMING_SNAKE_CASE__ ( self , a) -> None: self.insert_nth(0 , a) def SCREAMING_SNAKE_CASE__ ( self , a , a) -> None: if index < 0 or index > len(self): raise IndexError('list index out of range.') SCREAMING_SNAKE_CASE = Node(a) if self.head is None: SCREAMING_SNAKE_CASE = new_node # first node points itself SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = new_node elif index == 0: # insert at head SCREAMING_SNAKE_CASE = self.head SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = new_node else: SCREAMING_SNAKE_CASE = self.head for _ in range(index - 1): SCREAMING_SNAKE_CASE = temp.next SCREAMING_SNAKE_CASE = temp.next SCREAMING_SNAKE_CASE = new_node if index == len(self) - 1: # insert at tail SCREAMING_SNAKE_CASE = new_node def SCREAMING_SNAKE_CASE__ ( self) -> Tuple: return self.delete_nth(0) def SCREAMING_SNAKE_CASE__ ( self) -> Any: return self.delete_nth(len(self) - 1) def SCREAMING_SNAKE_CASE__ ( self , a = 0) -> Any: if not 0 <= index < len(self): raise IndexError('list index out of range.') SCREAMING_SNAKE_CASE = self.head if self.head == self.tail: # just one node SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = None elif index == 0: # delete head node SCREAMING_SNAKE_CASE = self.tail.next.next SCREAMING_SNAKE_CASE = self.head.next else: SCREAMING_SNAKE_CASE = self.head for _ in range(index - 1): SCREAMING_SNAKE_CASE = temp.next SCREAMING_SNAKE_CASE = temp.next SCREAMING_SNAKE_CASE = temp.next.next if index == len(self) - 1: # delete at tail SCREAMING_SNAKE_CASE = temp return delete_node.data def SCREAMING_SNAKE_CASE__ ( self) -> bool: return len(self) == 0 def lowerCamelCase__ (): SCREAMING_SNAKE_CASE = CircularLinkedList() assert len(_UpperCAmelCase) == 0 assert circular_linked_list.is_empty() is True assert str(_UpperCAmelCase) == "" try: circular_linked_list.delete_front() raise AssertionError # This should not happen except IndexError: assert True # This should happen try: circular_linked_list.delete_tail() raise AssertionError # This should not happen except IndexError: assert True # This should happen try: circular_linked_list.delete_nth(-1) raise AssertionError except IndexError: assert True try: circular_linked_list.delete_nth(0) raise AssertionError except IndexError: assert True assert circular_linked_list.is_empty() is True for i in range(5): assert len(_UpperCAmelCase) == i circular_linked_list.insert_nth(_UpperCAmelCase , i + 1) assert str(_UpperCAmelCase) == "->".join(str(_UpperCAmelCase) for i in range(1 , 6)) circular_linked_list.insert_tail(6) assert str(_UpperCAmelCase) == "->".join(str(_UpperCAmelCase) for i in range(1 , 7)) circular_linked_list.insert_head(0) assert str(_UpperCAmelCase) == "->".join(str(_UpperCAmelCase) for i in range(0 , 7)) assert circular_linked_list.delete_front() == 0 assert circular_linked_list.delete_tail() == 6 assert str(_UpperCAmelCase) == "->".join(str(_UpperCAmelCase) for i in range(1 , 6)) assert circular_linked_list.delete_nth(2) == 3 circular_linked_list.insert_nth(2 , 3) assert str(_UpperCAmelCase) == "->".join(str(_UpperCAmelCase) for i in range(1 , 6)) assert circular_linked_list.is_empty() is False if __name__ == "__main__": import doctest doctest.testmod()

import os import posixpath import uuid from dataclasses import dataclass from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union import numpy as np import pyarrow as pa import datasets from datasets.arrow_writer import ArrowWriter, ParquetWriter from datasets.config import MAX_SHARD_SIZE from datasets.filesystems import ( is_remote_filesystem, rename, ) from datasets.iterable_dataset import _BaseExamplesIterable from datasets.utils.py_utils import convert_file_size_to_int a_ : Optional[int] = datasets.utils.logging.get_logger(__name__) if TYPE_CHECKING: import pyspark @dataclass class _snake_case ( datasets.BuilderConfig ): _lowercase : Optional[datasets.Features] = None def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , ): import pyspark def generate_fn(): SCREAMING_SNAKE_CASE = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id')) for partition_id in partition_order: SCREAMING_SNAKE_CASE = df_with_partition_id.select('*').where(F'''part_id = {partition_id}''').drop('part_id') SCREAMING_SNAKE_CASE = partition_df.collect() SCREAMING_SNAKE_CASE = 0 for row in rows: yield F'''{partition_id}_{row_id}''', row.asDict() row_id += 1 return generate_fn class _snake_case ( _BaseExamplesIterable ): def __init__( self , a , a=None , ) -> Tuple: SCREAMING_SNAKE_CASE = df SCREAMING_SNAKE_CASE = partition_order or range(self.df.rdd.getNumPartitions()) SCREAMING_SNAKE_CASE = _generate_iterable_examples(self.df , self.partition_order) def __iter__( self) -> Dict: yield from self.generate_examples_fn() def SCREAMING_SNAKE_CASE__ ( self , a) -> "SparkExamplesIterable": SCREAMING_SNAKE_CASE = list(range(self.df.rdd.getNumPartitions())) generator.shuffle(a) return SparkExamplesIterable(self.df , partition_order=a) def SCREAMING_SNAKE_CASE__ ( self , a , a) -> "SparkExamplesIterable": SCREAMING_SNAKE_CASE = self.split_shard_indices_by_worker(a , a) return SparkExamplesIterable(self.df , partition_order=a) @property def SCREAMING_SNAKE_CASE__ ( self) -> int: return len(self.partition_order) class _snake_case ( datasets.DatasetBuilder ): _lowercase : int = SparkConfig def __init__( self , a , a = None , a = None , **a , ) -> List[str]: import pyspark SCREAMING_SNAKE_CASE = pyspark.sql.SparkSession.builder.getOrCreate() SCREAMING_SNAKE_CASE = df SCREAMING_SNAKE_CASE = working_dir super().__init__( cache_dir=a , config_name=str(self.df.semanticHash()) , **a , ) def SCREAMING_SNAKE_CASE__ ( self) -> Dict: # Returns the path of the created file. def create_cache_and_write_probe(a): # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories # already exist. os.makedirs(self._cache_dir , exist_ok=a) SCREAMING_SNAKE_CASE = os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex) # Opening the file in append mode will create a new file unless it already exists, in which case it will not # change the file contents. open(a , 'a') return [probe_file] if self._spark.conf.get('spark.master' , '').startswith('local'): return # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS # accessible to the driver. # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error. if self._cache_dir: SCREAMING_SNAKE_CASE = ( self._spark.sparkContext.parallelize(range(1) , 1).mapPartitions(a).collect() ) if os.path.isfile(probe[0]): return raise ValueError( 'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir') def SCREAMING_SNAKE_CASE__ ( self) -> List[str]: return datasets.DatasetInfo(features=self.config.features) def SCREAMING_SNAKE_CASE__ ( self , a) -> Dict: return [datasets.SplitGenerator(name=datasets.Split.TRAIN)] def SCREAMING_SNAKE_CASE__ ( self , a) -> Optional[int]: import pyspark def get_arrow_batch_size(a): for batch in it: yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}) SCREAMING_SNAKE_CASE = self.df.count() SCREAMING_SNAKE_CASE = df_num_rows if df_num_rows <= 100 else 100 # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample. SCREAMING_SNAKE_CASE = ( self.df.limit(a) .repartition(1) .mapInArrow(a , 'batch_bytes: long') .agg(pyspark.sql.functions.sum('batch_bytes').alias('sample_bytes')) .collect()[0] .sample_bytes / sample_num_rows ) SCREAMING_SNAKE_CASE = approx_bytes_per_row * df_num_rows if approx_total_size > max_shard_size: # Make sure there is at least one row per partition. SCREAMING_SNAKE_CASE = min(a , int(approx_total_size / max_shard_size)) SCREAMING_SNAKE_CASE = self.df.repartition(a) def SCREAMING_SNAKE_CASE__ ( self , a , a , a , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]: import pyspark SCREAMING_SNAKE_CASE = ParquetWriter if file_format == 'parquet' else ArrowWriter SCREAMING_SNAKE_CASE = os.path.join(self._working_dir , os.path.basename(a)) if self._working_dir else fpath SCREAMING_SNAKE_CASE = file_format == 'parquet' # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to # pickling the SparkContext. SCREAMING_SNAKE_CASE = self.config.features SCREAMING_SNAKE_CASE = self._writer_batch_size SCREAMING_SNAKE_CASE = self._fs.storage_options def write_arrow(a): # Within the same SparkContext, no two task attempts will share the same attempt ID. SCREAMING_SNAKE_CASE = pyspark.TaskContext().taskAttemptId() SCREAMING_SNAKE_CASE = next(a , a) if first_batch is None: # Some partitions might not receive any data. return pa.RecordBatch.from_arrays( [[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , ) SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = writer_class( features=a , path=working_fpath.replace('SSSSS' , f'''{shard_id:05d}''').replace('TTTTT' , f'''{task_id:05d}''') , writer_batch_size=a , storage_options=a , embed_local_files=a , ) SCREAMING_SNAKE_CASE = pa.Table.from_batches([first_batch]) writer.write_table(a) for batch in it: if max_shard_size is not None and writer._num_bytes >= max_shard_size: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = writer.finalize() writer.close() yield pa.RecordBatch.from_arrays( [[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , ) shard_id += 1 SCREAMING_SNAKE_CASE = writer_class( features=writer._features , path=working_fpath.replace('SSSSS' , f'''{shard_id:05d}''').replace('TTTTT' , f'''{task_id:05d}''') , writer_batch_size=a , storage_options=a , embed_local_files=a , ) SCREAMING_SNAKE_CASE = pa.Table.from_batches([batch]) writer.write_table(a) if writer._num_bytes > 0: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = writer.finalize() writer.close() yield pa.RecordBatch.from_arrays( [[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , ) if working_fpath != fpath: for file in os.listdir(os.path.dirname(a)): SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(a) , os.path.basename(a)) shutil.move(a , a) SCREAMING_SNAKE_CASE = ( self.df.mapInArrow(a , 'task_id: long, num_examples: long, num_bytes: long') .groupBy('task_id') .agg( pyspark.sql.functions.sum('num_examples').alias('total_num_examples') , pyspark.sql.functions.sum('num_bytes').alias('total_num_bytes') , pyspark.sql.functions.count('num_bytes').alias('num_shards') , pyspark.sql.functions.collect_list('num_examples').alias('shard_lengths') , ) .collect() ) for row in stats: yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths) def SCREAMING_SNAKE_CASE__ ( self , a , a = "arrow" , a = None , a = None , **a , ) -> List[Any]: self._validate_cache_dir() SCREAMING_SNAKE_CASE = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE) self._repartition_df_if_needed(a) SCREAMING_SNAKE_CASE = not is_remote_filesystem(self._fs) SCREAMING_SNAKE_CASE = os.path.join if is_local else posixpath.join SCREAMING_SNAKE_CASE = '-TTTTT-SSSSS-of-NNNNN' SCREAMING_SNAKE_CASE = f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}''' SCREAMING_SNAKE_CASE = path_join(self._output_dir , a) SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = [] for task_id, content in self._prepare_split_single(a , a , a): ( ( SCREAMING_SNAKE_CASE ) , ( SCREAMING_SNAKE_CASE ) , ( SCREAMING_SNAKE_CASE ) , ( SCREAMING_SNAKE_CASE ) , ) = content if num_bytes > 0: total_num_examples += num_examples total_num_bytes += num_bytes total_shards += num_shards task_id_and_num_shards.append((task_id, num_shards)) all_shard_lengths.extend(a) SCREAMING_SNAKE_CASE = total_num_examples SCREAMING_SNAKE_CASE = total_num_bytes # should rename everything at the end logger.debug(f'''Renaming {total_shards} shards.''') if total_shards > 1: SCREAMING_SNAKE_CASE = all_shard_lengths # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a # pickling error due to pickling the SparkContext. SCREAMING_SNAKE_CASE = self._fs # use the -SSSSS-of-NNNNN pattern def _rename_shard( a , a , a , ): rename( a , fpath.replace('SSSSS' , f'''{shard_id:05d}''').replace('TTTTT' , f'''{task_id:05d}''') , fpath.replace('TTTTT-SSSSS' , f'''{global_shard_id:05d}''').replace('NNNNN' , f'''{total_shards:05d}''') , ) SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = 0 for i in range(len(a)): SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = task_id_and_num_shards[i] for shard_id in range(a): args.append([task_id, shard_id, global_shard_id]) global_shard_id += 1 self._spark.sparkContext.parallelize(a , len(a)).map(lambda a: _rename_shard(*a)).collect() else: # don't use any pattern SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = task_id_and_num_shards[0][0] self._rename( fpath.replace('SSSSS' , f'''{shard_id:05d}''').replace('TTTTT' , f'''{task_id:05d}''') , fpath.replace(a , '') , ) def SCREAMING_SNAKE_CASE__ ( self , a , ) -> SparkExamplesIterable: return SparkExamplesIterable(self.df)

def __lowerCAmelCase ( a__ , a__ ) -> str: __a = len(a__ ) __a = len(a__ ) __a = ( first_str_length if first_str_length > second_str_length else second_str_length ) __a = [] for char_count in range(a__ ): if char_count < first_str_length: output_list.append(first_str[char_count] ) if char_count < second_str_length: output_list.append(second_str[char_count] ) return "".join(a__ ) if __name__ == "__main__": print(alternative_string_arrange('AB', 'XYZ'), end=' ')

import functools def __lowerCAmelCase ( a__ , a__ ) -> int: __a = len(a__ ) __a = len(a__ ) @functools.cache def min_distance(a__ , a__ ) -> int: # if first word index is overflow - delete all from the second word if indexa >= len_worda: return len_worda - indexa # if second word index is overflow - delete all from the first word if indexa >= len_worda: return len_worda - indexa __a = int(worda[indexa] != worda[indexa] ) # current letters not identical return min( 1 + min_distance(indexa + 1 , a__ ) , 1 + min_distance(a__ , indexa + 1 ) , diff + min_distance(indexa + 1 , indexa + 1 ) , ) return min_distance(0 , 0 ) if __name__ == "__main__": import doctest doctest.testmod()

"""simple docstring""" import argparse import os import torch from diffusers import ( CMStochasticIterativeScheduler, ConsistencyModelPipeline, UNetaDModel, ) lowerCAmelCase__ = { '''sample_size''': 32, '''in_channels''': 3, '''out_channels''': 3, '''layers_per_block''': 2, '''num_class_embeds''': 1_000, '''block_out_channels''': [32, 64], '''attention_head_dim''': 8, '''down_block_types''': [ '''ResnetDownsampleBlock2D''', '''AttnDownBlock2D''', ], '''up_block_types''': [ '''AttnUpBlock2D''', '''ResnetUpsampleBlock2D''', ], '''resnet_time_scale_shift''': '''scale_shift''', '''upsample_type''': '''resnet''', '''downsample_type''': '''resnet''', } lowerCAmelCase__ = { '''sample_size''': 64, '''in_channels''': 3, '''out_channels''': 3, '''layers_per_block''': 3, '''num_class_embeds''': 1_000, '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4], '''attention_head_dim''': 64, '''down_block_types''': [ '''ResnetDownsampleBlock2D''', '''AttnDownBlock2D''', '''AttnDownBlock2D''', '''AttnDownBlock2D''', ], '''up_block_types''': [ '''AttnUpBlock2D''', '''AttnUpBlock2D''', '''AttnUpBlock2D''', '''ResnetUpsampleBlock2D''', ], '''resnet_time_scale_shift''': '''scale_shift''', '''upsample_type''': '''resnet''', '''downsample_type''': '''resnet''', } lowerCAmelCase__ = { '''sample_size''': 256, '''in_channels''': 3, '''out_channels''': 3, '''layers_per_block''': 2, '''num_class_embeds''': None, '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4], '''attention_head_dim''': 64, '''down_block_types''': [ '''ResnetDownsampleBlock2D''', '''ResnetDownsampleBlock2D''', '''ResnetDownsampleBlock2D''', '''AttnDownBlock2D''', '''AttnDownBlock2D''', '''AttnDownBlock2D''', ], '''up_block_types''': [ '''AttnUpBlock2D''', '''AttnUpBlock2D''', '''AttnUpBlock2D''', '''ResnetUpsampleBlock2D''', '''ResnetUpsampleBlock2D''', '''ResnetUpsampleBlock2D''', ], '''resnet_time_scale_shift''': '''default''', '''upsample_type''': '''resnet''', '''downsample_type''': '''resnet''', } lowerCAmelCase__ = { '''num_train_timesteps''': 40, '''sigma_min''': 0.002, '''sigma_max''': 80.0, } lowerCAmelCase__ = { '''num_train_timesteps''': 201, '''sigma_min''': 0.002, '''sigma_max''': 80.0, } lowerCAmelCase__ = { '''num_train_timesteps''': 151, '''sigma_min''': 0.002, '''sigma_max''': 80.0, } def a__ ( SCREAMING_SNAKE_CASE : int ): '''simple docstring''' if isinstance(__a , __a ): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise argparse.ArgumentTypeError("boolean value expected" ) def a__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Union[str, Any]=False ): '''simple docstring''' lowerCAmelCase : Optional[Any] = checkpoint[f"""{old_prefix}.in_layers.0.weight"""] lowerCAmelCase : Optional[int] = checkpoint[f"""{old_prefix}.in_layers.0.bias"""] lowerCAmelCase : List[str] = checkpoint[f"""{old_prefix}.in_layers.2.weight"""] lowerCAmelCase : str = checkpoint[f"""{old_prefix}.in_layers.2.bias"""] lowerCAmelCase : Any = checkpoint[f"""{old_prefix}.emb_layers.1.weight"""] lowerCAmelCase : Any = checkpoint[f"""{old_prefix}.emb_layers.1.bias"""] lowerCAmelCase : int = checkpoint[f"""{old_prefix}.out_layers.0.weight"""] lowerCAmelCase : Union[str, Any] = checkpoint[f"""{old_prefix}.out_layers.0.bias"""] lowerCAmelCase : Union[str, Any] = checkpoint[f"""{old_prefix}.out_layers.3.weight"""] lowerCAmelCase : str = checkpoint[f"""{old_prefix}.out_layers.3.bias"""] if has_skip: lowerCAmelCase : Optional[Any] = checkpoint[f"""{old_prefix}.skip_connection.weight"""] lowerCAmelCase : List[str] = checkpoint[f"""{old_prefix}.skip_connection.bias"""] return new_checkpoint def a__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any]=None ): '''simple docstring''' lowerCAmelCase : List[str] = checkpoint[f"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 ) lowerCAmelCase : List[str] = checkpoint[f"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 ) lowerCAmelCase : Dict = checkpoint[f"""{old_prefix}.norm.weight"""] lowerCAmelCase : str = checkpoint[f"""{old_prefix}.norm.bias"""] lowerCAmelCase : List[Any] = weight_q.squeeze(-1 ).squeeze(-1 ) lowerCAmelCase : Union[str, Any] = bias_q.squeeze(-1 ).squeeze(-1 ) lowerCAmelCase : Tuple = weight_k.squeeze(-1 ).squeeze(-1 ) lowerCAmelCase : Optional[int] = bias_k.squeeze(-1 ).squeeze(-1 ) lowerCAmelCase : Tuple = weight_v.squeeze(-1 ).squeeze(-1 ) lowerCAmelCase : List[str] = bias_v.squeeze(-1 ).squeeze(-1 ) lowerCAmelCase : Optional[int] = ( checkpoint[f"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 ) ) lowerCAmelCase : Optional[Any] = checkpoint[f"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 ) return new_checkpoint def a__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Tuple ): '''simple docstring''' lowerCAmelCase : Optional[Any] = torch.load(__a , map_location="cpu" ) lowerCAmelCase : List[str] = {} lowerCAmelCase : Any = checkpoint['time_embed.0.weight'] lowerCAmelCase : Any = checkpoint['time_embed.0.bias'] lowerCAmelCase : List[Any] = checkpoint['time_embed.2.weight'] lowerCAmelCase : Any = checkpoint['time_embed.2.bias'] if unet_config["num_class_embeds"] is not None: lowerCAmelCase : List[Any] = checkpoint['label_emb.weight'] lowerCAmelCase : Any = checkpoint['input_blocks.0.0.weight'] lowerCAmelCase : Optional[Any] = checkpoint['input_blocks.0.0.bias'] lowerCAmelCase : List[Any] = unet_config['down_block_types'] lowerCAmelCase : int = unet_config['layers_per_block'] lowerCAmelCase : Union[str, Any] = unet_config['attention_head_dim'] lowerCAmelCase : Union[str, Any] = unet_config['block_out_channels'] lowerCAmelCase : Union[str, Any] = 1 lowerCAmelCase : List[str] = channels_list[0] for i, layer_type in enumerate(__a ): lowerCAmelCase : Optional[Any] = channels_list[i] lowerCAmelCase : List[Any] = current_channels != prev_channels if layer_type == "ResnetDownsampleBlock2D": for j in range(__a ): lowerCAmelCase : List[Any] = f"""down_blocks.{i}.resnets.{j}""" lowerCAmelCase : Tuple = f"""input_blocks.{current_layer}.0""" lowerCAmelCase : List[Any] = True if j == 0 and downsample_block_has_skip else False lowerCAmelCase : List[Any] = convert_resnet(__a , __a , __a , __a , has_skip=__a ) current_layer += 1 elif layer_type == "AttnDownBlock2D": for j in range(__a ): lowerCAmelCase : Optional[Any] = f"""down_blocks.{i}.resnets.{j}""" lowerCAmelCase : List[Any] = f"""input_blocks.{current_layer}.0""" lowerCAmelCase : Union[str, Any] = True if j == 0 and downsample_block_has_skip else False lowerCAmelCase : str = convert_resnet(__a , __a , __a , __a , has_skip=__a ) lowerCAmelCase : Tuple = f"""down_blocks.{i}.attentions.{j}""" lowerCAmelCase : Optional[int] = f"""input_blocks.{current_layer}.1""" lowerCAmelCase : Dict = convert_attention( __a , __a , __a , __a , __a ) current_layer += 1 if i != len(__a ) - 1: lowerCAmelCase : Tuple = f"""down_blocks.{i}.downsamplers.0""" lowerCAmelCase : str = f"""input_blocks.{current_layer}.0""" lowerCAmelCase : List[Any] = convert_resnet(__a , __a , __a , __a ) current_layer += 1 lowerCAmelCase : List[Any] = current_channels # hardcoded the mid-block for now lowerCAmelCase : Any = 'mid_block.resnets.0' lowerCAmelCase : Optional[Any] = 'middle_block.0' lowerCAmelCase : Optional[Any] = convert_resnet(__a , __a , __a , __a ) lowerCAmelCase : Union[str, Any] = 'mid_block.attentions.0' lowerCAmelCase : Any = 'middle_block.1' lowerCAmelCase : Tuple = convert_attention(__a , __a , __a , __a , __a ) lowerCAmelCase : Optional[int] = 'mid_block.resnets.1' lowerCAmelCase : Dict = 'middle_block.2' lowerCAmelCase : Optional[Any] = convert_resnet(__a , __a , __a , __a ) lowerCAmelCase : str = 0 lowerCAmelCase : str = unet_config['up_block_types'] for i, layer_type in enumerate(__a ): if layer_type == "ResnetUpsampleBlock2D": for j in range(layers_per_block + 1 ): lowerCAmelCase : List[str] = f"""up_blocks.{i}.resnets.{j}""" lowerCAmelCase : str = f"""output_blocks.{current_layer}.0""" lowerCAmelCase : Dict = convert_resnet(__a , __a , __a , __a , has_skip=__a ) current_layer += 1 if i != len(__a ) - 1: lowerCAmelCase : Dict = f"""up_blocks.{i}.upsamplers.0""" lowerCAmelCase : Union[str, Any] = f"""output_blocks.{current_layer-1}.1""" lowerCAmelCase : Dict = convert_resnet(__a , __a , __a , __a ) elif layer_type == "AttnUpBlock2D": for j in range(layers_per_block + 1 ): lowerCAmelCase : Union[str, Any] = f"""up_blocks.{i}.resnets.{j}""" lowerCAmelCase : int = f"""output_blocks.{current_layer}.0""" lowerCAmelCase : List[str] = convert_resnet(__a , __a , __a , __a , has_skip=__a ) lowerCAmelCase : Union[str, Any] = f"""up_blocks.{i}.attentions.{j}""" lowerCAmelCase : int = f"""output_blocks.{current_layer}.1""" lowerCAmelCase : List[Any] = convert_attention( __a , __a , __a , __a , __a ) current_layer += 1 if i != len(__a ) - 1: lowerCAmelCase : str = f"""up_blocks.{i}.upsamplers.0""" lowerCAmelCase : int = f"""output_blocks.{current_layer-1}.2""" lowerCAmelCase : List[str] = convert_resnet(__a , __a , __a , __a ) lowerCAmelCase : List[str] = checkpoint['out.0.weight'] lowerCAmelCase : int = checkpoint['out.0.bias'] lowerCAmelCase : Optional[int] = checkpoint['out.2.weight'] lowerCAmelCase : Optional[int] = checkpoint['out.2.bias'] return new_checkpoint if __name__ == "__main__": lowerCAmelCase__ = argparse.ArgumentParser() parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''') parser.add_argument( '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.''' ) parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''') lowerCAmelCase__ = parser.parse_args() lowerCAmelCase__ = strabool(args.class_cond) lowerCAmelCase__ = os.path.basename(args.unet_path) print(F"Checkpoint: {ckpt_name}") # Get U-Net config if "imagenet64" in ckpt_name: lowerCAmelCase__ = IMAGENET_64_UNET_CONFIG elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): lowerCAmelCase__ = LSUN_256_UNET_CONFIG elif "test" in ckpt_name: lowerCAmelCase__ = TEST_UNET_CONFIG else: raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.") if not args.class_cond: lowerCAmelCase__ = None lowerCAmelCase__ = con_pt_to_diffuser(args.unet_path, unet_config) lowerCAmelCase__ = UNetaDModel(**unet_config) image_unet.load_state_dict(converted_unet_ckpt) # Get scheduler config if "cd" in ckpt_name or "test" in ckpt_name: lowerCAmelCase__ = CD_SCHEDULER_CONFIG elif "ct" in ckpt_name and "imagenet64" in ckpt_name: lowerCAmelCase__ = CT_IMAGENET_64_SCHEDULER_CONFIG elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): lowerCAmelCase__ = CT_LSUN_256_SCHEDULER_CONFIG else: raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.") lowerCAmelCase__ = CMStochasticIterativeScheduler(**scheduler_config) lowerCAmelCase__ = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler) consistency_model.save_pretrained(args.dump_path)

'''simple docstring''' import importlib import inspect import json import os import re import shutil import sys from pathlib import Path from typing import Dict, Optional, Union from urllib import request from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info from packaging import version from .. import __version__ from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging __lowerCAmelCase = ( """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py""" ) __lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name def UpperCAmelCase_ (): """simple docstring""" _a : Optional[int] = 'https://pypi.org/pypi/diffusers/json' _a : int = json.loads(request.urlopen(__a ).read() )['releases'].keys() return sorted(__a , key=lambda __a : version.Version(__a ) ) def UpperCAmelCase_ (): """simple docstring""" if HF_MODULES_CACHE in sys.path: return sys.path.append(__a ) os.makedirs(__a , exist_ok=__a ) _a : str = Path(__a ) / '__init__.py' if not init_path.exists(): init_path.touch() def UpperCAmelCase_ (__a : Union[str, os.PathLike] ): """simple docstring""" init_hf_modules() _a : Dict = Path(__a ) / name # If the parent module does not exist yet, recursively create it. if not dynamic_module_path.parent.exists(): create_dynamic_module(dynamic_module_path.parent ) os.makedirs(__a , exist_ok=__a ) _a : Optional[int] = dynamic_module_path / '__init__.py' if not init_path.exists(): init_path.touch() def UpperCAmelCase_ (__a : str ): """simple docstring""" with open(__a , 'r' , encoding='utf-8' ) as f: _a : int = f.read() # Imports of the form `import .xxx` _a : Tuple = re.findall('^\s*import\s+\.(\S+)\s*$' , __a , flags=re.MULTILINE ) # Imports of the form `from .xxx import yyy` relative_imports += re.findall('^\s*from\s+\.(\S+)\s+import' , __a , flags=re.MULTILINE ) # Unique-ify return list(set(__a ) ) def UpperCAmelCase_ (__a : Any ): """simple docstring""" _a : Optional[int] = False _a : Optional[int] = [module_file] _a : List[str] = [] # Let's recurse through all relative imports while not no_change: _a : str = [] for f in files_to_check: new_imports.extend(get_relative_imports(__a ) ) _a : Union[str, Any] = Path(__a ).parent _a : str = [str(module_path / m ) for m in new_imports] _a : Tuple = [f for f in new_import_files if f not in all_relative_imports] _a : Dict = [f"""{f}.py""" for f in new_import_files] _a : List[str] = len(__a ) == 0 all_relative_imports.extend(__a ) return all_relative_imports def UpperCAmelCase_ (__a : Tuple ): """simple docstring""" with open(__a , 'r' , encoding='utf-8' ) as f: _a : Dict = f.read() # Imports of the form `import xxx` _a : Optional[int] = re.findall('^\s*import\s+(\S+)\s*$' , __a , flags=re.MULTILINE ) # Imports of the form `from xxx import yyy` imports += re.findall('^\s*from\s+(\S+)\s+import' , __a , flags=re.MULTILINE ) # Only keep the top-level module _a : List[str] = [imp.split('.' )[0] for imp in imports if not imp.startswith('.' )] # Unique-ify and test we got them all _a : Optional[int] = list(set(__a ) ) _a : List[str] = [] for imp in imports: try: importlib.import_module(__a ) except ImportError: missing_packages.append(__a ) if len(__a ) > 0: raise ImportError( 'This modeling file requires the following packages that were not found in your environment: ' f"""{', '.join(__a )}. Run `pip install {' '.join(__a )}`""" ) return get_relative_imports(__a ) def UpperCAmelCase_ (__a : Any , __a : str ): """simple docstring""" _a : Any = module_path.replace(os.path.sep , '.' ) _a : Union[str, Any] = importlib.import_module(__a ) if class_name is None: return find_pipeline_class(__a ) return getattr(__a , __a ) def UpperCAmelCase_ (__a : Optional[int] ): """simple docstring""" from ..pipelines import DiffusionPipeline _a : List[str] = dict(inspect.getmembers(__a , inspect.isclass ) ) _a : str = None for cls_name, cls in cls_members.items(): if ( cls_name != DiffusionPipeline.__name__ and issubclass(cls , __a ) and cls.__module__.split('.' )[0] != "diffusers" ): if pipeline_class is not None: raise ValueError( f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:""" f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in""" f""" {loaded_module}.""" ) _a : Any = cls return pipeline_class def UpperCAmelCase_ (__a : Union[str, os.PathLike] , __a : str , __a : Optional[Union[str, os.PathLike]] = None , __a : bool = False , __a : bool = False , __a : Optional[Dict[str, str]] = None , __a : Optional[Union[bool, str]] = None , __a : Optional[str] = None , __a : bool = False , ): """simple docstring""" _a : str = str(__a ) _a : Optional[Any] = os.path.join(__a , __a ) if os.path.isfile(__a ): _a : Tuple = module_file_or_url _a : Optional[Any] = 'local' elif pretrained_model_name_or_path.count('/' ) == 0: _a : int = get_diffusers_versions() # cut ".dev0" _a : Any = 'v' + '.'.join(__version__.split('.' )[:3] ) # retrieve github version that matches if revision is None: _a : Any = latest_version if latest_version[1:] in available_versions else 'main' logger.info(f"""Defaulting to latest_version: {revision}.""" ) elif revision in available_versions: _a : Any = f"""v{revision}""" elif revision == "main": _a : Optional[int] = revision else: raise ValueError( f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of""" f""" {', '.join(available_versions + ['main'] )}.""" ) # community pipeline on GitHub _a : Tuple = COMMUNITY_PIPELINES_URL.format(revision=__a , pipeline=__a ) try: _a : Any = cached_download( __a , cache_dir=__a , force_download=__a , proxies=__a , resume_download=__a , local_files_only=__a , use_auth_token=__a , ) _a : List[Any] = 'git' _a : Any = pretrained_model_name_or_path + '.py' except EnvironmentError: logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" ) raise else: try: # Load from URL or cache if already cached _a : Optional[Any] = hf_hub_download( __a , __a , cache_dir=__a , force_download=__a , proxies=__a , resume_download=__a , local_files_only=__a , use_auth_token=__a , ) _a : List[Any] = os.path.join('local' , '--'.join(pretrained_model_name_or_path.split('/' ) ) ) except EnvironmentError: logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" ) raise # Check we have all the requirements in our environment _a : Optional[int] = check_imports(__a ) # Now we move the module inside our cached dynamic modules. _a : Optional[Any] = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule create_dynamic_module(__a ) _a : Any = Path(__a ) / full_submodule if submodule == "local" or submodule == "git": # We always copy local files (we could hash the file to see if there was a change, and give them the name of # that hash, to only copy when there is a modification but it seems overkill for now). # The only reason we do the copy is to avoid putting too many folders in sys.path. shutil.copy(__a , submodule_path / module_file ) for module_needed in modules_needed: _a : Dict = f"""{module_needed}.py""" shutil.copy(os.path.join(__a , __a ) , submodule_path / module_needed ) else: # Get the commit hash # TODO: we will get this info in the etag soon, so retrieve it from there and not here. if isinstance(__a , __a ): _a : Optional[Any] = use_auth_token elif use_auth_token is True: _a : List[Any] = HfFolder.get_token() else: _a : Dict = None _a : int = model_info(__a , revision=__a , token=__a ).sha # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the # benefit of versioning. _a : Optional[int] = submodule_path / commit_hash _a : str = full_submodule + os.path.sep + commit_hash create_dynamic_module(__a ) if not (submodule_path / module_file).exists(): shutil.copy(__a , submodule_path / module_file ) # Make sure we also have every file with relative for module_needed in modules_needed: if not (submodule_path / module_needed).exists(): get_cached_module_file( __a , f"""{module_needed}.py""" , cache_dir=__a , force_download=__a , resume_download=__a , proxies=__a , use_auth_token=__a , revision=__a , local_files_only=__a , ) return os.path.join(__a , __a ) def UpperCAmelCase_ (__a : Union[str, os.PathLike] , __a : str , __a : Optional[str] = None , __a : Optional[Union[str, os.PathLike]] = None , __a : bool = False , __a : bool = False , __a : Optional[Dict[str, str]] = None , __a : Optional[Union[bool, str]] = None , __a : Optional[str] = None , __a : bool = False , **__a : str , ): """simple docstring""" _a : Dict = get_cached_module_file( __a , __a , cache_dir=__a , force_download=__a , resume_download=__a , proxies=__a , use_auth_token=__a , revision=__a , local_files_only=__a , ) return get_class_in_module(__a , final_module.replace('.py' , '' ) )

"""simple docstring""" import copy from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto.configuration_auto import AutoConfig if TYPE_CHECKING: from ... import PreTrainedTokenizerBase, TensorType __A = logging.get_logger(__name__) class _snake_case ( a__ ): snake_case__ = "vision-encoder-decoder" snake_case__ = True def __init__( self : Dict , **UpperCAmelCase : Dict ): super().__init__(**UpperCAmelCase ) if "encoder" not in kwargs or "decoder" not in kwargs: raise ValueError( F"""A configuraton of type {self.model_type} cannot be instantiated because """ F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" ) __lowerCamelCase : Dict = kwargs.pop("encoder" ) __lowerCamelCase : Any = encoder_config.pop("model_type" ) __lowerCamelCase : str = kwargs.pop("decoder" ) __lowerCamelCase : Optional[Any] = decoder_config.pop("model_type" ) __lowerCamelCase : Tuple = AutoConfig.for_model(UpperCAmelCase , **UpperCAmelCase ) __lowerCamelCase : Tuple = AutoConfig.for_model(UpperCAmelCase , **UpperCAmelCase ) __lowerCamelCase : Dict = True @classmethod def lowerCamelCase__ ( cls : List[Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , **UpperCAmelCase : Optional[int] ): logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" ) __lowerCamelCase : Tuple = True __lowerCamelCase : Optional[int] = True return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **UpperCAmelCase ) def lowerCamelCase__ ( self : Optional[int] ): __lowerCamelCase : int = copy.deepcopy(self.__dict__ ) __lowerCamelCase : List[Any] = self.encoder.to_dict() __lowerCamelCase : Union[str, Any] = self.decoder.to_dict() __lowerCamelCase : Dict = self.__class__.model_type return output class _snake_case ( a__ ): snake_case__ = version.parse("1.11" ) @property def lowerCamelCase__ ( self : Optional[int] ): return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def lowerCamelCase__ ( self : List[str] ): return 1E-4 @property def lowerCamelCase__ ( self : str ): return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} ) class _snake_case ( a__ ): @property def lowerCamelCase__ ( self : List[Any] ): __lowerCamelCase : Optional[int] = OrderedDict() __lowerCamelCase : int = {0: 'batch', 1: 'past_decoder_sequence + sequence'} __lowerCamelCase : Tuple = {0: 'batch', 1: 'past_decoder_sequence + sequence'} __lowerCamelCase : Any = {0: 'batch', 1: 'encoder_sequence'} return common_inputs def lowerCamelCase__ ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Tuple = -1 , UpperCAmelCase : Union[str, Any] = -1 , UpperCAmelCase : int = False , UpperCAmelCase : List[Any] = None , ): import torch __lowerCamelCase : str = OrderedDict() __lowerCamelCase : Union[str, Any] = super().generate_dummy_inputs( UpperCAmelCase , batch_size=UpperCAmelCase , seq_length=UpperCAmelCase , is_pair=UpperCAmelCase , framework=UpperCAmelCase ) __lowerCamelCase : Optional[int] = dummy_input['input_ids'].shape __lowerCamelCase : Optional[Any] = (batch, encoder_sequence, self._config.encoder_hidden_size) __lowerCamelCase : Optional[int] = dummy_input.pop("input_ids" ) __lowerCamelCase : str = dummy_input.pop("attention_mask" ) __lowerCamelCase : int = torch.zeros(UpperCAmelCase ) return common_inputs class _snake_case ( a__ ): @property def lowerCamelCase__ ( self : Tuple ): pass def lowerCamelCase__ ( self : Union[str, Any] , UpperCAmelCase : Tuple ): return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase ) def lowerCamelCase__ ( self : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : Any = "default" ): __lowerCamelCase : str = encoder_config.hidden_size return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase , UpperCAmelCase )

"""simple docstring""" def lowercase_ ( _lowerCamelCase: int = 4000000 ) -> int: '''simple docstring''' __lowerCamelCase : Tuple = [0, 1] __lowerCamelCase : Union[str, Any] = 0 while fib[i] <= n: fib.append(fib[i] + fib[i + 1] ) if fib[i + 2] > n: break i += 1 __lowerCamelCase : Tuple = 0 for j in range(len(_lowerCamelCase ) - 1 ): if fib[j] % 2 == 0: total += fib[j] return total if __name__ == "__main__": print(F"""{solution() = }""")

import argparse import json import pickle from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig from transformers.utils import logging logging.set_verbosity_info() lowerCamelCase_ = logging.get_logger(__name__) def __magic_name__ ( __a : List[str] ): '''simple docstring''' UpperCamelCase__ = SwinConfig.from_pretrained( """microsoft/swin-tiny-patch4-window7-224""" , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] ) UpperCamelCase__ = MaskFormerConfig(backbone_config=__a ) UpperCamelCase__ = """huggingface/label-files""" if "ade20k-full" in model_name: # this should be ok UpperCamelCase__ = 847 UpperCamelCase__ = """maskformer-ade20k-full-id2label.json""" elif "ade" in model_name: # this should be ok UpperCamelCase__ = 150 UpperCamelCase__ = """ade20k-id2label.json""" elif "coco-stuff" in model_name: # this should be ok UpperCamelCase__ = 171 UpperCamelCase__ = """maskformer-coco-stuff-id2label.json""" elif "coco" in model_name: # TODO UpperCamelCase__ = 133 UpperCamelCase__ = """coco-panoptic-id2label.json""" elif "cityscapes" in model_name: # this should be ok UpperCamelCase__ = 19 UpperCamelCase__ = """cityscapes-id2label.json""" elif "vistas" in model_name: # this should be ok UpperCamelCase__ = 65 UpperCamelCase__ = """mapillary-vistas-id2label.json""" UpperCamelCase__ = json.load(open(hf_hub_download(__a , __a , repo_type="""dataset""" ) , """r""" ) ) UpperCamelCase__ = {int(__a ): v for k, v in idalabel.items()} return config def __magic_name__ ( __a : Union[str, Any] ): '''simple docstring''' UpperCamelCase__ = [] # stem # fmt: off rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") ) rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") ) rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") ) rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") ) # stages for i in range(len(config.backbone_config.depths ) ): for j in range(config.backbone_config.depths[i] ): rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.attn.proj.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.norm2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight") ) rename_keys.append((f"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias") ) if i < 3: rename_keys.append((f"backbone.layers.{i}.downsample.reduction.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight") ) rename_keys.append((f"backbone.layers.{i}.downsample.norm.weight", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight") ) rename_keys.append((f"backbone.layers.{i}.downsample.norm.bias", f"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias") ) rename_keys.append((f"backbone.norm{i}.weight", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight") ) rename_keys.append((f"backbone.norm{i}.bias", f"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias") ) # FPN rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") ) rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") ) rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") ) for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ): rename_keys.append((f"sem_seg_head.adapter_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight") ) rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight") ) rename_keys.append((f"sem_seg_head.adapter_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias") ) rename_keys.append((f"sem_seg_head.layer_{source_index}.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight") ) rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.weight", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight") ) rename_keys.append((f"sem_seg_head.layer_{source_index}.norm.bias", f"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias") ) rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") ) rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") ) # Transformer decoder for idx in range(config.decoder_config.decoder_layers ): # self-attention out projection rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias") ) # cross-attention out projection rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias") ) # MLP 1 rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", f"model.transformer_module.decoder.layers.{idx}.fc1.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", f"model.transformer_module.decoder.layers.{idx}.fc1.bias") ) # MLP 2 rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", f"model.transformer_module.decoder.layers.{idx}.fc2.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", f"model.transformer_module.decoder.layers.{idx}.fc2.bias") ) # layernorm 1 (self-attention layernorm) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", f"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias") ) # layernorm 2 (cross-attention layernorm) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", f"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias") ) # layernorm 3 (final layernorm) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight") ) rename_keys.append((f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", f"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias") ) rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") ) rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") ) # heads on top rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") ) rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") ) rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") ) rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") ) rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") ) for i in range(3 ): rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.weight", f"mask_embedder.{i}.0.weight") ) rename_keys.append((f"sem_seg_head.predictor.mask_embed.layers.{i}.bias", f"mask_embedder.{i}.0.bias") ) # fmt: on return rename_keys def __magic_name__ ( __a : Any , __a : List[str] , __a : Union[str, Any] ): '''simple docstring''' UpperCamelCase__ = dct.pop(__a ) UpperCamelCase__ = val def __magic_name__ ( __a : Union[str, Any] , __a : List[Any] ): '''simple docstring''' UpperCamelCase__ = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )] for i in range(len(backbone_config.depths ) ): UpperCamelCase__ = num_features[i] for j in range(backbone_config.depths[i] ): # fmt: off # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias) UpperCamelCase__ = state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight" ) UpperCamelCase__ = state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.bias" ) # next, add query, keys and values (in that order) to the state dict UpperCamelCase__ = in_proj_weight[:dim, :] UpperCamelCase__ = in_proj_bias[: dim] UpperCamelCase__ = in_proj_weight[ dim : dim * 2, : ] UpperCamelCase__ = in_proj_bias[ dim : dim * 2 ] UpperCamelCase__ = in_proj_weight[ -dim :, : ] UpperCamelCase__ = in_proj_bias[-dim :] # fmt: on def __magic_name__ ( __a : Tuple , __a : int ): '''simple docstring''' UpperCamelCase__ = config.decoder_config.hidden_size for idx in range(config.decoder_config.decoder_layers ): # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias) UpperCamelCase__ = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight" ) UpperCamelCase__ = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias" ) # next, add query, keys and values (in that order) to the state dict UpperCamelCase__ = in_proj_weight[: hidden_size, :] UpperCamelCase__ = in_proj_bias[:config.hidden_size] UpperCamelCase__ = in_proj_weight[hidden_size : hidden_size * 2, :] UpperCamelCase__ = in_proj_bias[hidden_size : hidden_size * 2] UpperCamelCase__ = in_proj_weight[-hidden_size :, :] UpperCamelCase__ = in_proj_bias[-hidden_size :] # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias) UpperCamelCase__ = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight" ) UpperCamelCase__ = state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias" ) # next, add query, keys and values (in that order) to the state dict UpperCamelCase__ = in_proj_weight[: hidden_size, :] UpperCamelCase__ = in_proj_bias[:config.hidden_size] UpperCamelCase__ = in_proj_weight[hidden_size : hidden_size * 2, :] UpperCamelCase__ = in_proj_bias[hidden_size : hidden_size * 2] UpperCamelCase__ = in_proj_weight[-hidden_size :, :] UpperCamelCase__ = in_proj_bias[-hidden_size :] # fmt: on def __magic_name__ ( ): '''simple docstring''' UpperCamelCase__ = """http://images.cocodataset.org/val2017/000000039769.jpg""" UpperCamelCase__ = Image.open(requests.get(__a , stream=__a ).raw ) return im @torch.no_grad() def __magic_name__ ( __a : str , __a : Optional[Any] , __a : Any , __a : Tuple = False ): '''simple docstring''' UpperCamelCase__ = get_maskformer_config(__a ) # load original state_dict with open(__a , """rb""" ) as f: UpperCamelCase__ = pickle.load(__a ) UpperCamelCase__ = data["""model"""] # for name, param in state_dict.items(): # print(name, param.shape) # rename keys UpperCamelCase__ = create_rename_keys(__a ) for src, dest in rename_keys: rename_key(__a , __a , __a ) read_in_swin_q_k_v(__a , config.backbone_config ) read_in_decoder_q_k_v(__a , __a ) # update to torch tensors for key, value in state_dict.items(): UpperCamelCase__ = torch.from_numpy(__a ) # load 🤗 model UpperCamelCase__ = MaskFormerForInstanceSegmentation(__a ) model.eval() for name, param in model.named_parameters(): print(__a , param.shape ) UpperCamelCase__ , UpperCamelCase__ = model.load_state_dict(__a , strict=__a ) assert missing_keys == [ "model.pixel_level_module.encoder.model.layernorm.weight", "model.pixel_level_module.encoder.model.layernorm.bias", ] assert len(__a ) == 0, f"Unexpected keys: {unexpected_keys}" # verify results UpperCamelCase__ = prepare_img() if "vistas" in model_name: UpperCamelCase__ = 65 elif "cityscapes" in model_name: UpperCamelCase__ = 65_535 else: UpperCamelCase__ = 255 UpperCamelCase__ = True if """ade""" in model_name else False UpperCamelCase__ = MaskFormerImageProcessor(ignore_index=__a , reduce_labels=__a ) UpperCamelCase__ = image_processor(__a , return_tensors="""pt""" ) UpperCamelCase__ = model(**__a ) print("""Logits:""" , outputs.class_queries_logits[0, :3, :3] ) if model_name == "maskformer-swin-tiny-ade": UpperCamelCase__ = torch.tensor( [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]] ) assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , __a , atol=1E-4 ) print("""Looks ok!""" ) if pytorch_dump_folder_path is not None: print(f"Saving model and image processor to {pytorch_dump_folder_path}" ) Path(__a ).mkdir(exist_ok=__a ) model.save_pretrained(__a ) image_processor.save_pretrained(__a ) if push_to_hub: print("""Pushing model and image processor to the hub...""" ) model.push_to_hub(f"nielsr/{model_name}" ) image_processor.push_to_hub(f"nielsr/{model_name}" ) if __name__ == "__main__": lowerCamelCase_ = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default='''maskformer-swin-tiny-ade''', type=str, help=('''Name of the MaskFormer model you\'d like to convert''',), ) parser.add_argument( '''--checkpoint_path''', default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''', type=str, help='''Path to the original state dict (.pth file).''', ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.''' ) parser.add_argument( '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.''' ) lowerCamelCase_ = parser.parse_args() convert_maskformer_checkpoint( args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub )

'''simple docstring''' import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import XLMRobertaTokenizerFast from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class UpperCamelCase__ ( SCREAMING_SNAKE_CASE , unittest.TestCase): UpperCAmelCase__ : str = KandinskyImgaImgPipeline UpperCAmelCase__ : Optional[int] = ['prompt', 'image_embeds', 'negative_image_embeds', 'image'] UpperCAmelCase__ : Union[str, Any] = [ 'prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds', 'image', ] UpperCAmelCase__ : Union[str, Any] = [ 'generator', 'height', 'width', 'strength', 'guidance_scale', 'negative_prompt', 'num_inference_steps', 'return_dict', 'guidance_scale', 'num_images_per_prompt', 'output_type', 'return_dict', ] UpperCAmelCase__ : Any = False @property def lowercase_ ( self :Tuple ) -> Any: '''simple docstring''' return 32 @property def lowercase_ ( self :Optional[int] ) -> str: '''simple docstring''' return 32 @property def lowercase_ ( self :Optional[Any] ) -> str: '''simple docstring''' return self.time_input_dim @property def lowercase_ ( self :Optional[Any] ) -> Any: '''simple docstring''' return self.time_input_dim * 4 @property def lowercase_ ( self :Optional[Any] ) -> Union[str, Any]: '''simple docstring''' return 100 @property def lowercase_ ( self :Tuple ) -> Tuple: '''simple docstring''' __A = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' ) return tokenizer @property def lowercase_ ( self :Union[str, Any] ) -> List[str]: '''simple docstring''' torch.manual_seed(0 ) __A = MCLIPConfig( numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_005 , ) __A = MultilingualCLIP(_A ) __A = text_encoder.eval() return text_encoder @property def lowercase_ ( self :Optional[int] ) -> Tuple: '''simple docstring''' torch.manual_seed(0 ) __A = { 'in_channels': 4, # Out channels is double in channels because predicts mean and variance 'out_channels': 8, 'addition_embed_type': 'text_image', 'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'), 'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'), 'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn', 'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2), 'layers_per_block': 1, 'encoder_hid_dim': self.text_embedder_hidden_size, 'encoder_hid_dim_type': 'text_image_proj', 'cross_attention_dim': self.cross_attention_dim, 'attention_head_dim': 4, 'resnet_time_scale_shift': 'scale_shift', 'class_embed_type': None, } __A = UNetaDConditionModel(**_A ) return model @property def lowercase_ ( self :Optional[Any] ) -> Optional[Any]: '''simple docstring''' return { "block_out_channels": [32, 64], "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": [ "AttnUpDecoderBlock2D", "UpDecoderBlock2D", ], "vq_embed_dim": 4, } @property def lowercase_ ( self :Optional[int] ) -> Any: '''simple docstring''' torch.manual_seed(0 ) __A = VQModel(**self.dummy_movq_kwargs ) return model def lowercase_ ( self :List[str] ) -> str: '''simple docstring''' __A = self.dummy_text_encoder __A = self.dummy_tokenizer __A = self.dummy_unet __A = self.dummy_movq __A = { 'num_train_timesteps': 1_000, 'beta_schedule': 'linear', 'beta_start': 0.00_085, 'beta_end': 0.012, 'clip_sample': False, 'set_alpha_to_one': False, 'steps_offset': 0, 'prediction_type': 'epsilon', 'thresholding': False, } __A = DDIMScheduler(**_A ) __A = { 'text_encoder': text_encoder, 'tokenizer': tokenizer, 'unet': unet, 'scheduler': scheduler, 'movq': movq, } return components def lowercase_ ( self :Dict , _A :Union[str, Any] , _A :Optional[int]=0 ) -> str: '''simple docstring''' __A = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_A ) ).to(_A ) __A = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_A ) # create init_image __A = floats_tensor((1, 3, 64, 64) , rng=random.Random(_A ) ).to(_A ) __A = image.cpu().permute(0 , 2 , 3 , 1 )[0] __A = Image.fromarray(np.uinta(_A ) ).convert('RGB' ).resize((256, 256) ) if str(_A ).startswith('mps' ): __A = torch.manual_seed(_A ) else: __A = torch.Generator(device=_A ).manual_seed(_A ) __A = { 'prompt': 'horse', 'image': init_image, 'image_embeds': image_embeds, 'negative_image_embeds': negative_image_embeds, 'generator': generator, 'height': 64, 'width': 64, 'num_inference_steps': 10, 'guidance_scale': 7.0, 'strength': 0.2, 'output_type': 'np', } return inputs def lowercase_ ( self :Optional[Any] ) -> Optional[int]: '''simple docstring''' __A = 'cpu' __A = self.get_dummy_components() __A = self.pipeline_class(**_A ) __A = pipe.to(_A ) pipe.set_progress_bar_config(disable=_A ) __A = pipe(**self.get_dummy_inputs(_A ) ) __A = output.images __A = pipe( **self.get_dummy_inputs(_A ) , return_dict=_A , )[0] __A = image[0, -3:, -3:, -1] __A = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) __A = np.array( [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}' assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}' @slow @require_torch_gpu class UpperCamelCase__ ( unittest.TestCase): def lowercase_ ( self :Union[str, Any] ) -> Dict: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def lowercase_ ( self :Dict ) -> Optional[int]: '''simple docstring''' __A = load_numpy( 'https://huggingface.co./datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/kandinsky_img2img_frog.npy' ) __A = load_image( 'https://huggingface.co./datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' ) __A = 'A red cartoon frog, 4k' __A = KandinskyPriorPipeline.from_pretrained( 'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa ) pipe_prior.to(_A ) __A = KandinskyImgaImgPipeline.from_pretrained( 'kandinsky-community/kandinsky-2-1' , torch_dtype=torch.floataa ) __A = pipeline.to(_A ) pipeline.set_progress_bar_config(disable=_A ) __A = torch.Generator(device='cpu' ).manual_seed(0 ) __A , __A = pipe_prior( _A , generator=_A , num_inference_steps=5 , negative_prompt='' , ).to_tuple() __A = pipeline( _A , image=_A , image_embeds=_A , negative_image_embeds=_A , generator=_A , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , ) __A = output.images[0] assert image.shape == (768, 768, 3) assert_mean_pixel_difference(_A , _A )

"""simple docstring""" import argparse import os import re lowercase_ = "src/transformers" # Pattern that looks at the indentation in a line. lowercase_ = re.compile(r"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. lowercase_ = re.compile(r"^\s*\"([^\"]+)\":") # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. lowercase_ = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]") # Pattern that matches `"key",` and puts `key` in group 0. lowercase_ = re.compile(r"^\s*\"([^\"]+)\",\s*$") # Pattern that matches any `[stuff]` and puts `stuff` in group 0. lowercase_ = re.compile(r"\[([^\]]+)\]") def lowercase ( lowerCAmelCase__ : Optional[Any] ) -> Optional[int]: __a = _re_indent.search(lowerCAmelCase__ ) return "" if search is None else search.groups()[0] def lowercase ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Any="" , lowerCAmelCase__ : Tuple=None , lowerCAmelCase__ : Union[str, Any]=None ) -> Optional[Any]: __a = 0 __a = code.split('''\n''' ) if start_prompt is not None: while not lines[index].startswith(lowerCAmelCase__ ): index += 1 __a = ['''\n'''.join(lines[:index] )] else: __a = [] # We split into blocks until we get to the `end_prompt` (or the end of the block). __a = [lines[index]] index += 1 while index < len(lowerCAmelCase__ ) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase__ )): if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level: if len(lowerCAmelCase__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ): current_block.append(lines[index] ) blocks.append('''\n'''.join(lowerCAmelCase__ ) ) if index < len(lowerCAmelCase__ ) - 1: __a = [lines[index + 1]] index += 1 else: __a = [] else: blocks.append('''\n'''.join(lowerCAmelCase__ ) ) __a = [lines[index]] else: current_block.append(lines[index] ) index += 1 # Adds current block if it's nonempty. if len(lowerCAmelCase__ ) > 0: blocks.append('''\n'''.join(lowerCAmelCase__ ) ) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(lowerCAmelCase__ ): blocks.append('''\n'''.join(lines[index:] ) ) return blocks def lowercase ( lowerCAmelCase__ : str ) -> Dict: def _inner(lowerCAmelCase__ : List[str] ): return key(lowerCAmelCase__ ).lower().replace('''_''' , '''''' ) return _inner def lowercase ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str=None ) -> int: # If no key is provided, we use a noop. def noop(lowerCAmelCase__ : int ): return x if key is None: __a = noop # Constants are all uppercase, they go first. __a = [obj for obj in objects if key(lowerCAmelCase__ ).isupper()] # Classes are not all uppercase but start with a capital, they go second. __a = [obj for obj in objects if key(lowerCAmelCase__ )[0].isupper() and not key(lowerCAmelCase__ ).isupper()] # Functions begin with a lowercase, they go last. __a = [obj for obj in objects if not key(lowerCAmelCase__ )[0].isupper()] __a = ignore_underscore(lowerCAmelCase__ ) return sorted(lowerCAmelCase__ , key=lowerCAmelCase__ ) + sorted(lowerCAmelCase__ , key=lowerCAmelCase__ ) + sorted(lowerCAmelCase__ , key=lowerCAmelCase__ ) def lowercase ( lowerCAmelCase__ : List[str] ) -> Tuple: # This inner function sort imports between [ ]. def _replace(lowerCAmelCase__ : Union[str, Any] ): __a = match.groups()[0] if "," not in imports: return f'''[{imports}]''' __a = [part.strip().replace('''"''' , '''''' ) for part in imports.split(''',''' )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: __a = keys[:-1] return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(lowerCAmelCase__ )] ) + "]" __a = import_statement.split('''\n''' ) if len(lowerCAmelCase__ ) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. __a = 2 if lines[1].strip() == '''[''' else 1 __a = [(i, _re_strip_line.search(lowerCAmelCase__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )] __a = sort_objects(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : x[1] ) __a = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] ) elif len(lowerCAmelCase__ ) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1] ) is not None: __a = _re_bracket_content.sub(_replace , lines[1] ) else: __a = [part.strip().replace('''"''' , '''''' ) for part in lines[1].split(''',''' )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: __a = keys[:-1] __a = get_indent(lines[1] ) + ''', '''.join([f'''"{k}"''' for k in sort_objects(lowerCAmelCase__ )] ) return "\n".join(lowerCAmelCase__ ) else: # Finally we have to deal with imports fitting on one line __a = _re_bracket_content.sub(_replace , lowerCAmelCase__ ) return import_statement def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Tuple=True ) -> str: with open(lowerCAmelCase__ , encoding='''utf-8''' ) as f: __a = f.read() if "_import_structure" not in code: return # Blocks of indent level 0 __a = split_code_in_indented_blocks( lowerCAmelCase__ , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' ) # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt). for block_idx in range(1 , len(lowerCAmelCase__ ) - 1 ): # Check if the block contains some `_import_structure`s thingy to sort. __a = main_blocks[block_idx] __a = block.split('''\n''' ) # Get to the start of the imports. __a = 0 while line_idx < len(lowerCAmelCase__ ) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: __a = len(lowerCAmelCase__ ) else: line_idx += 1 if line_idx >= len(lowerCAmelCase__ ): continue # Ignore beginning and last line: they don't contain anything. __a = '''\n'''.join(block_lines[line_idx:-1] ) __a = get_indent(block_lines[1] ) # Slit the internal block into blocks of indent level 1. __a = split_code_in_indented_blocks(lowerCAmelCase__ , indent_level=lowerCAmelCase__ ) # We have two categories of import key: list or _import_structure[key].append/extend __a = _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or just comments. __a = [(pattern.search(lowerCAmelCase__ ).groups()[0] if pattern.search(lowerCAmelCase__ ) is not None else None) for b in internal_blocks] # We only sort the lines with a key. __a = [(i, key) for i, key in enumerate(lowerCAmelCase__ ) if key is not None] __a = [x[0] for x in sorted(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : x[1] )] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. __a = 0 __a = [] for i in range(len(lowerCAmelCase__ ) ): if keys[i] is None: reorderded_blocks.append(internal_blocks[i] ) else: __a = sort_objects_in_import(internal_blocks[sorted_indices[count]] ) reorderded_blocks.append(lowerCAmelCase__ ) count += 1 # And we put our main block back together with its first and last line. __a = '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] ) if code != "\n".join(lowerCAmelCase__ ): if check_only: return True else: print(f'''Overwriting {file}.''' ) with open(lowerCAmelCase__ , '''w''' , encoding='''utf-8''' ) as f: f.write('''\n'''.join(lowerCAmelCase__ ) ) def lowercase ( lowerCAmelCase__ : Optional[int]=True ) -> Optional[int]: __a = [] for root, _, files in os.walk(lowerCAmelCase__ ): if "__init__.py" in files: __a = sort_imports(os.path.join(lowerCAmelCase__ , '''__init__.py''' ) , check_only=lowerCAmelCase__ ) if result: __a = [os.path.join(lowerCAmelCase__ , '''__init__.py''' )] if len(lowerCAmelCase__ ) > 0: raise ValueError(f'''Would overwrite {len(lowerCAmelCase__ )} files, run `make style`.''' ) if __name__ == "__main__": lowercase_ = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") lowercase_ = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)

"""simple docstring""" import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf if is_tf_available(): import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from transformers import GradientAccumulator, create_optimizer @require_tf class __lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' def __UpperCAmelCase ( self , _a , _a , _a ): self.assertEqual(len(_a ) , len(_a ) ) for a, b in zip(_a , _a ): self.assertAlmostEqual(_a , _a , delta=_a ) def __UpperCAmelCase ( self ): __a = GradientAccumulator() accumulator([tf.constant([1.0, 2.0] )] ) accumulator([tf.constant([-2.0, 1.0] )] ) accumulator([tf.constant([-1.0, 2.0] )] ) with self.assertRaises(_a ): accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] ) self.assertEqual(accumulator.step , 3 ) self.assertEqual(len(accumulator.gradients ) , 1 ) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 ) accumulator.reset() self.assertEqual(accumulator.step , 0 ) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 ) def __UpperCAmelCase ( self ): __a = None ops.enable_eager_execution_internal() __a = tf.config.list_physical_devices('''CPU''' ) if len(_a ) == 1: tf.config.set_logical_device_configuration( physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] ) __a = tf.config.list_logical_devices(device_type='''CPU''' ) __a = tf.distribute.MirroredStrategy(devices=devices[:2] ) with strategy.scope(): __a = GradientAccumulator() __a = tf.Variable([4.0, 3.0] ) __a , __a = create_optimizer(5E-5 , 10 , 5 ) __a = tf.Variable([0.0, 0.0] , trainable=_a ) def accumulate_on_replica(_a ): accumulator([gradient] ) def apply_on_replica(): optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) ) @tf.function def accumulate(_a , _a ): with strategy.scope(): __a = strategy.experimental_local_results(_a ) local_variables[0].assign(_a ) local_variables[1].assign(_a ) strategy.run(_a , args=(gradient_placeholder,) ) @tf.function def apply_grad(): with strategy.scope(): strategy.run(_a ) def _check_local_values(_a , _a ): __a = strategy.experimental_local_results(accumulator._gradients[0] ) self.assertListAlmostEqual(values[0].value() , _a , tol=1E-2 ) self.assertListAlmostEqual(values[1].value() , _a , tol=1E-2 ) accumulate([1.0, 2.0] , [-1.0, 1.0] ) accumulate([3.0, -1.0] , [-1.0, -1.0] ) accumulate([-2.0, 2.0] , [3.0, -2.0] ) self.assertEqual(accumulator.step , 3 ) _check_local_values([2.0, 3.0] , [1.0, -2.0] ) apply_grad() self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 ) accumulator.reset() self.assertEqual(accumulator.step , 0 ) _check_local_values([0.0, 0.0] , [0.0, 0.0] )

"""simple docstring""" def _a ( _SCREAMING_SNAKE_CASE = 1_000 ) -> int: snake_case_ = -1 snake_case_ = 0 for a in range(1 , n // 3 ): # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c snake_case_ = (n * n - 2 * a * n) // (2 * n - 2 * a) snake_case_ = n - a - b if c * c == (a * a + b * b): snake_case_ = a * b * c if candidate >= product: snake_case_ = candidate return product if __name__ == "__main__": print(f"""{solution() = }""")

"""simple docstring""" from typing import Optional import torch import torch.utils.checkpoint from torch import Tensor, nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACTaFN from ...modeling_outputs import ( BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.backbone_utils import BackboneMixin from .configuration_resnet import ResNetConfig __lowerCamelCase = logging.get_logger(__name__) # General docstring __lowerCamelCase = "ResNetConfig" # Base docstring __lowerCamelCase = "microsoft/resnet-50" __lowerCamelCase = [1, 20_48, 7, 7] # Image classification docstring __lowerCamelCase = "microsoft/resnet-50" __lowerCamelCase = "tiger cat" __lowerCamelCase = [ "microsoft/resnet-50", # See all resnet models at https://huggingface.co./models?filter=resnet ] class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = 3 ,__UpperCAmelCase = 1 ,__UpperCAmelCase = "relu" ) -> Any: super().__init__() A__ = nn.Convad( __UpperCAmelCase ,__UpperCAmelCase ,kernel_size=__UpperCAmelCase ,stride=__UpperCAmelCase ,padding=kernel_size // 2 ,bias=__UpperCAmelCase ) A__ = nn.BatchNormad(__UpperCAmelCase ) A__ = ACTaFN[activation] if activation is not None else nn.Identity() def snake_case__ ( self ,__UpperCAmelCase ) -> Tensor: A__ = self.convolution(__UpperCAmelCase ) A__ = self.normalization(__UpperCAmelCase ) A__ = self.activation(__UpperCAmelCase ) return hidden_state class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ) -> Any: super().__init__() A__ = ResNetConvLayer( config.num_channels ,config.embedding_size ,kernel_size=7 ,stride=2 ,activation=config.hidden_act ) A__ = nn.MaxPoolad(kernel_size=3 ,stride=2 ,padding=1 ) A__ = config.num_channels def snake_case__ ( self ,__UpperCAmelCase ) -> Tensor: A__ = pixel_values.shape[1] if num_channels != self.num_channels: raise ValueError( 'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' ) A__ = self.embedder(__UpperCAmelCase ) A__ = self.pooler(__UpperCAmelCase ) return embedding class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = 2 ) -> Optional[Any]: super().__init__() A__ = nn.Convad(__UpperCAmelCase ,__UpperCAmelCase ,kernel_size=1 ,stride=__UpperCAmelCase ,bias=__UpperCAmelCase ) A__ = nn.BatchNormad(__UpperCAmelCase ) def snake_case__ ( self ,__UpperCAmelCase ) -> Tensor: A__ = self.convolution(__UpperCAmelCase ) A__ = self.normalization(__UpperCAmelCase ) return hidden_state class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = 1 ,__UpperCAmelCase = "relu" ) -> int: super().__init__() A__ = in_channels != out_channels or stride != 1 A__ = ( ResNetShortCut(__UpperCAmelCase ,__UpperCAmelCase ,stride=__UpperCAmelCase ) if should_apply_shortcut else nn.Identity() ) A__ = nn.Sequential( ResNetConvLayer(__UpperCAmelCase ,__UpperCAmelCase ,stride=__UpperCAmelCase ) ,ResNetConvLayer(__UpperCAmelCase ,__UpperCAmelCase ,activation=__UpperCAmelCase ) ,) A__ = ACTaFN[activation] def snake_case__ ( self ,__UpperCAmelCase ) -> Union[str, Any]: A__ = hidden_state A__ = self.layer(__UpperCAmelCase ) A__ = self.shortcut(__UpperCAmelCase ) hidden_state += residual A__ = self.activation(__UpperCAmelCase ) return hidden_state class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = 1 ,__UpperCAmelCase = "relu" ,__UpperCAmelCase = 4 ) -> int: super().__init__() A__ = in_channels != out_channels or stride != 1 A__ = out_channels // reduction A__ = ( ResNetShortCut(__UpperCAmelCase ,__UpperCAmelCase ,stride=__UpperCAmelCase ) if should_apply_shortcut else nn.Identity() ) A__ = nn.Sequential( ResNetConvLayer(__UpperCAmelCase ,__UpperCAmelCase ,kernel_size=1 ) ,ResNetConvLayer(__UpperCAmelCase ,__UpperCAmelCase ,stride=__UpperCAmelCase ) ,ResNetConvLayer(__UpperCAmelCase ,__UpperCAmelCase ,kernel_size=1 ,activation=__UpperCAmelCase ) ,) A__ = ACTaFN[activation] def snake_case__ ( self ,__UpperCAmelCase ) -> Optional[Any]: A__ = hidden_state A__ = self.layer(__UpperCAmelCase ) A__ = self.shortcut(__UpperCAmelCase ) hidden_state += residual A__ = self.activation(__UpperCAmelCase ) return hidden_state class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase = 2 ,__UpperCAmelCase = 2 ,) -> Any: super().__init__() A__ = ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer A__ = nn.Sequential( # downsampling is done in the first layer with stride of 2 layer(__UpperCAmelCase ,__UpperCAmelCase ,stride=__UpperCAmelCase ,activation=config.hidden_act ) ,*[layer(__UpperCAmelCase ,__UpperCAmelCase ,activation=config.hidden_act ) for _ in range(depth - 1 )] ,) def snake_case__ ( self ,__UpperCAmelCase ) -> Tensor: A__ = input for layer in self.layers: A__ = layer(__UpperCAmelCase ) return hidden_state class UpperCamelCase__( nn.Module ): def __init__( self ,__UpperCAmelCase ) -> Optional[Any]: super().__init__() A__ = nn.ModuleList([] ) # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input self.stages.append( ResNetStage( __UpperCAmelCase ,config.embedding_size ,config.hidden_sizes[0] ,stride=2 if config.downsample_in_first_stage else 1 ,depth=config.depths[0] ,) ) A__ = zip(config.hidden_sizes ,config.hidden_sizes[1:] ) for (in_channels, out_channels), depth in zip(__UpperCAmelCase ,config.depths[1:] ): self.stages.append(ResNetStage(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,depth=__UpperCAmelCase ) ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase = False ,__UpperCAmelCase = True ) -> BaseModelOutputWithNoAttention: A__ = () if output_hidden_states else None for stage_module in self.stages: if output_hidden_states: A__ = hidden_states + (hidden_state,) A__ = stage_module(__UpperCAmelCase ) if output_hidden_states: A__ = hidden_states + (hidden_state,) if not return_dict: return tuple(v for v in [hidden_state, hidden_states] if v is not None ) return BaseModelOutputWithNoAttention( last_hidden_state=__UpperCAmelCase ,hidden_states=__UpperCAmelCase ,) class UpperCamelCase__( __A ): lowerCAmelCase__ : str = ResNetConfig lowerCAmelCase__ : str = 'resnet' lowerCAmelCase__ : int = 'pixel_values' lowerCAmelCase__ : Any = True def snake_case__ ( self ,__UpperCAmelCase ) -> List[Any]: if isinstance(__UpperCAmelCase ,nn.Convad ): nn.init.kaiming_normal_(module.weight ,mode='fan_out' ,nonlinearity='relu' ) elif isinstance(__UpperCAmelCase ,(nn.BatchNormad, nn.GroupNorm) ): nn.init.constant_(module.weight ,1 ) nn.init.constant_(module.bias ,0 ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase=False ) -> Any: if isinstance(__UpperCAmelCase ,__UpperCAmelCase ): A__ = value __lowerCamelCase = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n" __lowerCamelCase = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n" @add_start_docstrings( 'The bare ResNet model outputting raw features without any specific head on top.' , __A , ) class UpperCamelCase__( __A ): def __init__( self ,__UpperCAmelCase ) -> Union[str, Any]: super().__init__(__UpperCAmelCase ) A__ = config A__ = ResNetEmbeddings(__UpperCAmelCase ) A__ = ResNetEncoder(__UpperCAmelCase ) A__ = nn.AdaptiveAvgPoolad((1, 1) ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(__UpperCAmelCase ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC ,output_type=__UpperCAmelCase ,config_class=_CONFIG_FOR_DOC ,modality='vision' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase = None ,__UpperCAmelCase = None ) -> BaseModelOutputWithPoolingAndNoAttention: A__ = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) A__ = return_dict if return_dict is not None else self.config.use_return_dict A__ = self.embedder(__UpperCAmelCase ) A__ = self.encoder( __UpperCAmelCase ,output_hidden_states=__UpperCAmelCase ,return_dict=__UpperCAmelCase ) A__ = encoder_outputs[0] A__ = self.pooler(__UpperCAmelCase ) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=__UpperCAmelCase ,pooler_output=__UpperCAmelCase ,hidden_states=encoder_outputs.hidden_states ,) @add_start_docstrings( '\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , __A , ) class UpperCamelCase__( __A ): def __init__( self ,__UpperCAmelCase ) -> Tuple: super().__init__(__UpperCAmelCase ) A__ = config.num_labels A__ = ResNetModel(__UpperCAmelCase ) # classification head A__ = nn.Sequential( nn.Flatten() ,nn.Linear(config.hidden_sizes[-1] ,config.num_labels ) if config.num_labels > 0 else nn.Identity() ,) # initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(__UpperCAmelCase ) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=__UpperCAmelCase ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,) def snake_case__ ( self ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,) -> ImageClassifierOutputWithNoAttention: A__ = return_dict if return_dict is not None else self.config.use_return_dict A__ = self.resnet(__UpperCAmelCase ,output_hidden_states=__UpperCAmelCase ,return_dict=__UpperCAmelCase ) A__ = outputs.pooler_output if return_dict else outputs[1] A__ = self.classifier(__UpperCAmelCase ) A__ = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: A__ = 'regression' elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): A__ = 'single_label_classification' else: A__ = 'multi_label_classification' if self.config.problem_type == "regression": A__ = MSELoss() if self.num_labels == 1: A__ = loss_fct(logits.squeeze() ,labels.squeeze() ) else: A__ = loss_fct(__UpperCAmelCase ,__UpperCAmelCase ) elif self.config.problem_type == "single_label_classification": A__ = CrossEntropyLoss() A__ = loss_fct(logits.view(-1 ,self.num_labels ) ,labels.view(-1 ) ) elif self.config.problem_type == "multi_label_classification": A__ = BCEWithLogitsLoss() A__ = loss_fct(__UpperCAmelCase ,__UpperCAmelCase ) if not return_dict: A__ = (logits,) + outputs[2:] return (loss,) + output if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase ,logits=__UpperCAmelCase ,hidden_states=outputs.hidden_states ) @add_start_docstrings( '\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n ' , __A , ) class UpperCamelCase__( __A , __A ): def __init__( self ,__UpperCAmelCase ) -> Optional[Any]: super().__init__(__UpperCAmelCase ) super()._init_backbone(__UpperCAmelCase ) A__ = [config.embedding_size] + config.hidden_sizes A__ = ResNetEmbeddings(__UpperCAmelCase ) A__ = ResNetEncoder(__UpperCAmelCase ) # initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(__UpperCAmelCase ) @replace_return_docstrings(output_type=__UpperCAmelCase ,config_class=_CONFIG_FOR_DOC ) def snake_case__ ( self ,__UpperCAmelCase ,__UpperCAmelCase = None ,__UpperCAmelCase = None ) -> BackboneOutput: A__ = return_dict if return_dict is not None else self.config.use_return_dict A__ = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) A__ = self.embedder(__UpperCAmelCase ) A__ = self.encoder(__UpperCAmelCase ,output_hidden_states=__UpperCAmelCase ,return_dict=__UpperCAmelCase ) A__ = outputs.hidden_states A__ = () for idx, stage in enumerate(self.stage_names ): if stage in self.out_features: feature_maps += (hidden_states[idx],) if not return_dict: A__ = (feature_maps,) if output_hidden_states: output += (outputs.hidden_states,) return output return BackboneOutput( feature_maps=__UpperCAmelCase ,hidden_states=outputs.hidden_states if output_hidden_states else None ,attentions=__UpperCAmelCase ,)

'''simple docstring''' import argparse from pathlib import Path from typing import Dict, OrderedDict, Tuple import torch from audiocraft.models import MusicGen from transformers import ( AutoFeatureExtractor, AutoTokenizer, EncodecModel, MusicgenDecoderConfig, MusicgenForConditionalGeneration, MusicgenProcessor, TaEncoderModel, ) from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM from transformers.utils import logging logging.set_verbosity_info() UpperCamelCase_ = logging.get_logger(__name__) UpperCamelCase_ = ["model.decoder.embed_positions.weights"] def lowercase__( __UpperCamelCase: Tuple ): """simple docstring""" if "emb" in name: SCREAMING_SNAKE_CASE : Dict = name.replace('emb' ,'model.decoder.embed_tokens' ) if "transformer" in name: SCREAMING_SNAKE_CASE : Optional[Any] = name.replace('transformer' ,'model.decoder' ) if "cross_attention" in name: SCREAMING_SNAKE_CASE : Optional[Any] = name.replace('cross_attention' ,'encoder_attn' ) if "linear1" in name: SCREAMING_SNAKE_CASE : Any = name.replace('linear1' ,'fc1' ) if "linear2" in name: SCREAMING_SNAKE_CASE : Optional[Any] = name.replace('linear2' ,'fc2' ) if "norm1" in name: SCREAMING_SNAKE_CASE : List[str] = name.replace('norm1' ,'self_attn_layer_norm' ) if "norm_cross" in name: SCREAMING_SNAKE_CASE : int = name.replace('norm_cross' ,'encoder_attn_layer_norm' ) if "norm2" in name: SCREAMING_SNAKE_CASE : str = name.replace('norm2' ,'final_layer_norm' ) if "out_norm" in name: SCREAMING_SNAKE_CASE : int = name.replace('out_norm' ,'model.decoder.layer_norm' ) if "linears" in name: SCREAMING_SNAKE_CASE : Tuple = name.replace('linears' ,'lm_heads' ) if "condition_provider.conditioners.description.output_proj" in name: SCREAMING_SNAKE_CASE : List[str] = name.replace('condition_provider.conditioners.description.output_proj' ,'enc_to_dec_proj' ) return name def lowercase__( __UpperCamelCase: OrderedDict ,__UpperCamelCase: int ): """simple docstring""" SCREAMING_SNAKE_CASE : str = list(state_dict.keys() ) SCREAMING_SNAKE_CASE : Union[str, Any] = {} for key in keys: SCREAMING_SNAKE_CASE : Optional[Any] = state_dict.pop(__UpperCamelCase ) SCREAMING_SNAKE_CASE : Any = rename_keys(__UpperCamelCase ) if "in_proj_weight" in key: # split fused qkv proj SCREAMING_SNAKE_CASE : Optional[Any] = val[:hidden_size, :] SCREAMING_SNAKE_CASE : Optional[int] = val[hidden_size : 2 * hidden_size, :] SCREAMING_SNAKE_CASE : Optional[Any] = val[-hidden_size:, :] elif "enc_to_dec_proj" in key: SCREAMING_SNAKE_CASE : Union[str, Any] = val else: SCREAMING_SNAKE_CASE : Optional[Any] = val return state_dict, enc_dec_proj_state_dict def lowercase__( __UpperCamelCase: str ): """simple docstring""" if checkpoint == "small": # default config values SCREAMING_SNAKE_CASE : Tuple = 10_24 SCREAMING_SNAKE_CASE : List[str] = 24 SCREAMING_SNAKE_CASE : Optional[Any] = 16 elif checkpoint == "medium": SCREAMING_SNAKE_CASE : int = 15_36 SCREAMING_SNAKE_CASE : str = 48 SCREAMING_SNAKE_CASE : int = 24 elif checkpoint == "large": SCREAMING_SNAKE_CASE : Any = 20_48 SCREAMING_SNAKE_CASE : int = 48 SCREAMING_SNAKE_CASE : Optional[int] = 32 else: raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." ) SCREAMING_SNAKE_CASE : Dict = MusicgenDecoderConfig( hidden_size=__UpperCamelCase ,ffn_dim=hidden_size * 4 ,num_hidden_layers=__UpperCamelCase ,num_attention_heads=__UpperCamelCase ,) return config @torch.no_grad() def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: Any=None ,__UpperCamelCase: int=None ,__UpperCamelCase: int="cpu" ): """simple docstring""" SCREAMING_SNAKE_CASE : Dict = MusicGen.get_pretrained(__UpperCamelCase ,device=__UpperCamelCase ) SCREAMING_SNAKE_CASE : List[str] = decoder_config_from_checkpoint(__UpperCamelCase ) SCREAMING_SNAKE_CASE : List[Any] = fairseq_model.lm.state_dict() SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = rename_state_dict( __UpperCamelCase ,hidden_size=decoder_config.hidden_size ) SCREAMING_SNAKE_CASE : str = TaEncoderModel.from_pretrained('t5-base' ) SCREAMING_SNAKE_CASE : List[Any] = EncodecModel.from_pretrained('facebook/encodec_32khz' ) SCREAMING_SNAKE_CASE : Optional[int] = MusicgenForCausalLM(__UpperCamelCase ).eval() # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = decoder.load_state_dict(__UpperCamelCase ,strict=__UpperCamelCase ) for key in missing_keys.copy(): if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS: missing_keys.remove(__UpperCamelCase ) if len(__UpperCamelCase ) > 0: raise ValueError(f"Missing key(s) in state_dict: {missing_keys}" ) if len(__UpperCamelCase ) > 0: raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}" ) # init the composite model SCREAMING_SNAKE_CASE : List[Any] = MusicgenForConditionalGeneration(text_encoder=__UpperCamelCase ,audio_encoder=__UpperCamelCase ,decoder=__UpperCamelCase ) # load the pre-trained enc-dec projection (from the decoder state dict) model.enc_to_dec_proj.load_state_dict(__UpperCamelCase ) # check we can do a forward pass SCREAMING_SNAKE_CASE : Any = torch.arange(0 ,8 ,dtype=torch.long ).reshape(2 ,-1 ) SCREAMING_SNAKE_CASE : Optional[Any] = input_ids.reshape(2 * 4 ,-1 ) with torch.no_grad(): SCREAMING_SNAKE_CASE : Optional[int] = model(input_ids=__UpperCamelCase ,decoder_input_ids=__UpperCamelCase ).logits if logits.shape != (8, 1, 20_48): raise ValueError('Incorrect shape for logits' ) # now construct the processor SCREAMING_SNAKE_CASE : str = AutoTokenizer.from_pretrained('t5-base' ) SCREAMING_SNAKE_CASE : Union[str, Any] = AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz' ,padding_side='left' ) SCREAMING_SNAKE_CASE : List[str] = MusicgenProcessor(feature_extractor=__UpperCamelCase ,tokenizer=__UpperCamelCase ) # set the appropriate bos/pad token ids SCREAMING_SNAKE_CASE : Any = 20_48 SCREAMING_SNAKE_CASE : str = 20_48 # set other default generation config params SCREAMING_SNAKE_CASE : int = int(30 * audio_encoder.config.frame_rate ) SCREAMING_SNAKE_CASE : Dict = True SCREAMING_SNAKE_CASE : str = 3.0 if pytorch_dump_folder is not None: Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase ) logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}" ) model.save_pretrained(__UpperCamelCase ) processor.save_pretrained(__UpperCamelCase ) if repo_id: logger.info(f"Pushing model {checkpoint} to {repo_id}" ) model.push_to_hub(__UpperCamelCase ) processor.push_to_hub(__UpperCamelCase ) if __name__ == "__main__": UpperCamelCase_ = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint", default="small", type=str, help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.", ) parser.add_argument( "--pytorch_dump_folder", required=True, default=None, type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) parser.add_argument( "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda." ) UpperCamelCase_ = parser.parse_args() convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

'''simple docstring''' from sklearn.metrics import matthews_corrcoef import datasets UpperCamelCase_ = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n" UpperCamelCase_ = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n" UpperCamelCase_ = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class _a ( datasets.Metric ): '''simple docstring''' def UpperCamelCase_ ( self ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features( { 'predictions': datasets.Value('int32' ), 'references': datasets.Value('int32' ), } ), reference_urls=[ 'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html' ], ) def UpperCamelCase_ ( self, A, A, A=None ): '''simple docstring''' return { "matthews_correlation": float(matthews_corrcoef(A, A, sample_weight=A ) ), }

"""simple docstring""" import pickle import numpy as np from matplotlib import pyplot as plt class UpperCAmelCase_ : def __init__( self , a , a , a , a , a , a=0.2 , a=0.2 ) -> Dict: lowercase__ : Any = bp_numa lowercase__ : Optional[int] = bp_numa lowercase__ : Tuple = bp_numa lowercase__ : Optional[Any] = conva_get[:2] lowercase__ : Optional[int] = conva_get[2] lowercase__ : Optional[Any] = size_pa lowercase__ : Union[str, Any] = rate_w lowercase__ : Union[str, Any] = rate_t lowercase__ : List[Any] = [ np.mat(-1 * np.random.rand(self.conva[0] , self.conva[0] ) + 0.5 ) for i in range(self.conva[1] ) ] lowercase__ : Optional[Any] = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 ) lowercase__ : int = np.mat(-1 * np.random.rand(self.num_bpa , self.num_bpa ) + 0.5 ) lowercase__ : Any = -2 * np.random.rand(self.conva[1] ) + 1 lowercase__ : int = -2 * np.random.rand(self.num_bpa ) + 1 lowercase__ : int = -2 * np.random.rand(self.num_bpa ) + 1 def _UpperCAmelCase ( self , a ) -> Union[str, Any]: # save model dict with pickle lowercase__ : Optional[Any] = { 'num_bp1': self.num_bpa, 'num_bp2': self.num_bpa, 'num_bp3': self.num_bpa, 'conv1': self.conva, 'step_conv1': self.step_conva, 'size_pooling1': self.size_poolinga, 'rate_weight': self.rate_weight, 'rate_thre': self.rate_thre, 'w_conv1': self.w_conva, 'wkj': self.wkj, 'vji': self.vji, 'thre_conv1': self.thre_conva, 'thre_bp2': self.thre_bpa, 'thre_bp3': self.thre_bpa, } with open(a , 'wb' ) as f: pickle.dump(a , a ) print(f"""Model saved： {save_path}""" ) @classmethod def _UpperCAmelCase ( cls , a ) -> Any: # read saved model with open(a , 'rb' ) as f: lowercase__ : Optional[int] = pickle.load(a ) # noqa: S301 lowercase__ : Optional[int] = model_dic.get('conv1' ) conv_get.append(model_dic.get('step_conv1' ) ) lowercase__ : List[Any] = model_dic.get('size_pooling1' ) lowercase__ : Tuple = model_dic.get('num_bp1' ) lowercase__ : int = model_dic.get('num_bp2' ) lowercase__ : int = model_dic.get('num_bp3' ) lowercase__ : Union[str, Any] = model_dic.get('rate_weight' ) lowercase__ : Tuple = model_dic.get('rate_thre' ) # create model instance lowercase__ : Tuple = CNN(a , a , a , a , a , a , a ) # modify model parameter lowercase__ : str = model_dic.get('w_conv1' ) lowercase__ : Optional[int] = model_dic.get('wkj' ) lowercase__ : Tuple = model_dic.get('vji' ) lowercase__ : str = model_dic.get('thre_conv1' ) lowercase__ : Union[str, Any] = model_dic.get('thre_bp2' ) lowercase__ : List[str] = model_dic.get('thre_bp3' ) return conv_ins def _UpperCAmelCase ( self , a ) -> str: return 1 / (1 + np.exp(-1 * x )) def _UpperCAmelCase ( self , a ) -> Any: return round(a , 3 ) def _UpperCAmelCase ( self , a , a , a , a , a ) -> List[str]: # convolution process lowercase__ : int = convs[0] lowercase__ : Optional[Any] = convs[1] lowercase__ : int = np.shape(a )[0] # get the data slice of original image data, data_focus lowercase__ : Optional[Any] = [] for i_focus in range(0 , size_data - size_conv + 1 , a ): for j_focus in range(0 , size_data - size_conv + 1 , a ): lowercase__ : Optional[int] = data[ i_focus : i_focus + size_conv, j_focus : j_focus + size_conv ] data_focus.append(a ) # calculate the feature map of every single kernel, and saved as list of matrix lowercase__ : Union[str, Any] = [] lowercase__ : Dict = int((size_data - size_conv) / conv_step + 1 ) for i_map in range(a ): lowercase__ : Any = [] for i_focus in range(len(a ) ): lowercase__ : Tuple = ( np.sum(np.multiply(data_focus[i_focus] , w_convs[i_map] ) ) - thre_convs[i_map] ) featuremap.append(self.sig(a ) ) lowercase__ : Optional[Any] = np.asmatrix(a ).reshape( a , a ) data_featuremap.append(a ) # expanding the data slice to One dimenssion lowercase__ : str = [] for each_focus in data_focus: focusa_list.extend(self.Expand_Mat(a ) ) lowercase__ : int = np.asarray(a ) return focus_list, data_featuremap def _UpperCAmelCase ( self , a , a , a="average_pool" ) -> str: # pooling process lowercase__ : List[str] = len(featuremaps[0] ) lowercase__ : List[str] = int(size_map / size_pooling ) lowercase__ : str = [] for i_map in range(len(a ) ): lowercase__ : List[str] = featuremaps[i_map] lowercase__ : Optional[int] = [] for i_focus in range(0 , a , a ): for j_focus in range(0 , a , a ): lowercase__ : List[Any] = feature_map[ i_focus : i_focus + size_pooling, j_focus : j_focus + size_pooling, ] if pooling_type == "average_pool": # average pooling map_pooled.append(np.average(a ) ) elif pooling_type == "max_pooling": # max pooling map_pooled.append(np.max(a ) ) lowercase__ : List[Any] = np.asmatrix(a ).reshape(a , a ) featuremap_pooled.append(a ) return featuremap_pooled def _UpperCAmelCase ( self , a ) -> List[str]: # expanding three dimension data to one dimension list lowercase__ : Any = [] for i in range(len(a ) ): lowercase__ : Optional[int] = np.shape(data[i] ) lowercase__ : int = data[i].reshape(1 , shapes[0] * shapes[1] ) lowercase__ : str = data_listed.getA().tolist()[0] data_expanded.extend(a ) lowercase__ : int = np.asarray(a ) return data_expanded def _UpperCAmelCase ( self , a ) -> Dict: # expanding matrix to one dimension list lowercase__ : Dict = np.asarray(a ) lowercase__ : Union[str, Any] = np.shape(a ) lowercase__ : Optional[Any] = data_mat.reshape(1 , shapes[0] * shapes[1] ) return data_expanded def _UpperCAmelCase ( self , a , a , a , a , a ) -> List[Any]: lowercase__ : Dict = [] lowercase__ : int = 0 for i_map in range(a ): lowercase__ : str = np.ones((size_map, size_map) ) for i in range(0 , a , a ): for j in range(0 , a , a ): lowercase__ : Optional[Any] = pd_pool[ i_pool ] lowercase__ : Union[str, Any] = i_pool + 1 lowercase__ : List[Any] = np.multiply( a , np.multiply(out_map[i_map] , (1 - out_map[i_map]) ) ) pd_all.append(a ) return pd_all def _UpperCAmelCase ( self , a , a , a , a , a , a=bool ) -> str: # model traning print('----------------------Start Training-------------------------' ) print((' - - Shape: Train_Data ', np.shape(a )) ) print((' - - Shape: Teach_Data ', np.shape(a )) ) lowercase__ : int = 0 lowercase__ : List[Any] = [] lowercase__ : Union[str, Any] = 1_0_0_0_0 while rp < n_repeat and mse >= error_accuracy: lowercase__ : List[Any] = 0 print(f"""-------------Learning Time {rp}--------------""" ) for p in range(len(a ) ): # print('------------Learning Image: %d--------------'%p) lowercase__ : Optional[int] = np.asmatrix(datas_train[p] ) lowercase__ : int = np.asarray(datas_teach[p] ) lowercase__ , lowercase__ : Union[str, Any] = self.convolute( a , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , ) lowercase__ : Optional[Any] = self.pooling(a , self.size_poolinga ) lowercase__ : Tuple = np.shape(a ) lowercase__ : List[str] = self._expand(a ) lowercase__ : Optional[int] = data_bp_input lowercase__ : Optional[Any] = np.dot(a , self.vji.T ) - self.thre_bpa lowercase__ : str = self.sig(a ) lowercase__ : Tuple = np.dot(a , self.wkj.T ) - self.thre_bpa lowercase__ : Any = self.sig(a ) # --------------Model Leaning ------------------------ # calculate error and gradient--------------- lowercase__ : int = np.multiply( (data_teach - bp_outa) , np.multiply(a , (1 - bp_outa) ) ) lowercase__ : Any = np.multiply( np.dot(a , self.wkj ) , np.multiply(a , (1 - bp_outa) ) ) lowercase__ : Optional[int] = np.dot(a , self.vji ) lowercase__ : Union[str, Any] = pd_i_all / (self.size_poolinga * self.size_poolinga) lowercase__ : Any = pd_conva_pooled.T.getA().tolist() lowercase__ : List[str] = self._calculate_gradient_from_pool( a , a , shape_featuremapa[0] , shape_featuremapa[1] , self.size_poolinga , ) # weight and threshold learning process--------- # convolution layer for k_conv in range(self.conva[1] ): lowercase__ : Optional[int] = self._expand_mat(pd_conva_all[k_conv] ) lowercase__ : Tuple = self.rate_weight * np.dot(a , a ) lowercase__ : Union[str, Any] = self.w_conva[k_conv] + delta_w.reshape( (self.conva[0], self.conva[0]) ) lowercase__ : Any = ( self.thre_conva[k_conv] - np.sum(pd_conva_all[k_conv] ) * self.rate_thre ) # all connected layer lowercase__ : Tuple = self.wkj + pd_k_all.T * bp_outa * self.rate_weight lowercase__ : Tuple = self.vji + pd_j_all.T * bp_outa * self.rate_weight lowercase__ : Tuple = self.thre_bpa - pd_k_all * self.rate_thre lowercase__ : Optional[Any] = self.thre_bpa - pd_j_all * self.rate_thre # calculate the sum error of all single image lowercase__ : Dict = np.sum(abs(data_teach - bp_outa ) ) error_count += errors # print(' ----Teach ',data_teach) # print(' ----BP_output ',bp_out3) lowercase__ : str = rp + 1 lowercase__ : List[str] = error_count / patterns all_mse.append(a ) def draw_error(): lowercase__ : Any = [error_accuracy for i in range(int(n_repeat * 1.2 ) )] plt.plot(a , '+-' ) plt.plot(a , 'r--' ) plt.xlabel('Learning Times' ) plt.ylabel('All_mse' ) plt.grid(a , alpha=0.5 ) plt.show() print('------------------Training Complished---------------------' ) print((' - - Training epoch: ', rp, f""" - - Mse: {mse:.6f}""") ) if draw_e: draw_error() return mse def _UpperCAmelCase ( self , a ) -> List[Any]: # model predict lowercase__ : Optional[int] = [] print('-------------------Start Testing-------------------------' ) print((' - - Shape: Test_Data ', np.shape(a )) ) for p in range(len(a ) ): lowercase__ : List[str] = np.asmatrix(datas_test[p] ) lowercase__ , lowercase__ : Tuple = self.convolute( a , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , ) lowercase__ : Any = self.pooling(a , self.size_poolinga ) lowercase__ : Union[str, Any] = self._expand(a ) lowercase__ : Optional[Any] = data_bp_input lowercase__ : str = bp_outa * self.vji.T - self.thre_bpa lowercase__ : Optional[Any] = self.sig(a ) lowercase__ : Dict = bp_outa * self.wkj.T - self.thre_bpa lowercase__ : List[str] = self.sig(a ) produce_out.extend(bp_outa.getA().tolist() ) lowercase__ : Optional[int] = [list(map(self.do_round , a ) ) for each in produce_out] return np.asarray(a ) def _UpperCAmelCase ( self , a ) -> List[str]: # return the data of image after convoluting process so we can check it out lowercase__ : Any = np.asmatrix(a ) lowercase__ , lowercase__ : str = self.convolute( a , self.conva , self.w_conva , self.thre_conva , conv_step=self.step_conva , ) lowercase__ : Tuple = self.pooling(a , self.size_poolinga ) return data_conveda, data_pooleda if __name__ == "__main__": pass

"""simple docstring""" import argparse import torch from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel from transformers.utils import logging logging.set_verbosity_info() def a_ ( _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ): '''simple docstring''' lowercase__ : int = FunnelConfig.from_json_file(_lowerCAmelCase ) print(f"""Building PyTorch model from configuration: {config}""" ) lowercase__ : List[Any] = FunnelBaseModel(_lowerCAmelCase ) if base_model else FunnelModel(_lowerCAmelCase ) # Load weights from tf checkpoint load_tf_weights_in_funnel(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) # Save pytorch-model print(f"""Save PyTorch model to {pytorch_dump_path}""" ) torch.save(model.state_dict() , _lowerCAmelCase ) if __name__ == "__main__": _UpperCamelCase : Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path." ) parser.add_argument( "--config_file", default=None, type=str, required=True, help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not." ) _UpperCamelCase : List[str] = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model )

'''simple docstring''' # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class __lowercase ( lowerCAmelCase__ , lowerCAmelCase__ ): '''simple docstring''' a : int = 1 @register_to_config def __init__(self ,_lowerCamelCase=2000 ,_lowerCamelCase=0.1 ,_lowerCamelCase=20 ,_lowerCamelCase=1E-3 ) -> List[str]: '''simple docstring''' __lowercase = None __lowercase = None __lowercase = None def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase = None ) -> Dict: '''simple docstring''' __lowercase = torch.linspace(1 ,self.config.sampling_eps ,_lowerCamelCase ,device=_lowerCamelCase ) def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase=None ) -> List[Any]: '''simple docstring''' if self.timesteps is None: raise ValueError( '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score __lowercase = ( -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) __lowercase = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) __lowercase = std.flatten() while len(std.shape ) < len(score.shape ): __lowercase = std.unsqueeze(-1 ) __lowercase = -score / std # compute __lowercase = -1.0 / len(self.timesteps ) __lowercase = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) __lowercase = beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): __lowercase = beta_t.unsqueeze(-1 ) __lowercase = -0.5 * beta_t * x __lowercase = torch.sqrt(_lowerCamelCase ) __lowercase = drift - diffusion**2 * score __lowercase = x + drift * dt # add noise __lowercase = randn_tensor(x.shape ,layout=x.layout ,generator=_lowerCamelCase ,device=x.device ,dtype=x.dtype ) __lowercase = x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__(self ) -> Optional[int]: '''simple docstring''' return self.config.num_train_timesteps

'''simple docstring''' import os import random import sys from . import cryptomath_module as cryptoMath # noqa: N812 from . import rabin_miller as rabinMiller # noqa: N812 def _lowerCAmelCase ( ): print('''Making key files...''' ) make_key_files('''rsa''' , 1_0_2_4 ) print('''Key files generation successful.''' ) def _lowerCAmelCase ( lowerCamelCase_ : int ): print('''Generating prime p...''' ) __lowercase = rabinMiller.generate_large_prime(lowerCamelCase_ ) print('''Generating prime q...''' ) __lowercase = rabinMiller.generate_large_prime(lowerCamelCase_ ) __lowercase = p * q print('''Generating e that is relatively prime to (p - 1) * (q - 1)...''' ) while True: __lowercase = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) ) if cryptoMath.gcd(lowerCamelCase_ , (p - 1) * (q - 1) ) == 1: break print('''Calculating d that is mod inverse of e...''' ) __lowercase = cryptoMath.find_mod_inverse(lowerCamelCase_ , (p - 1) * (q - 1) ) __lowercase = (n, e) __lowercase = (n, d) return (public_key, private_key) def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : int ): if os.path.exists(f"{name}_pubkey.txt" ) or os.path.exists(f"{name}_privkey.txt" ): print('''\nWARNING:''' ) print( f"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n" '''Use a different name or delete these files and re-run this program.''' ) sys.exit() __lowercase , __lowercase = generate_key(lowerCamelCase_ ) print(f"\nWriting public key to file {name}_pubkey.txt..." ) with open(f"{name}_pubkey.txt" , '''w''' ) as out_file: out_file.write(f"{key_size},{public_key[0]},{public_key[1]}" ) print(f"Writing private key to file {name}_privkey.txt..." ) with open(f"{name}_privkey.txt" , '''w''' ) as out_file: out_file.write(f"{key_size},{private_key[0]},{private_key[1]}" ) if __name__ == "__main__": main()

import argparse import shutil from pathlib import Path from tqdm import tqdm from transformers import AutoTokenizer def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : List[str] , __lowerCAmelCase : Any , __lowerCAmelCase : int=1_0_2_4 ): a__ , a__ = [], [] a__ = list(zip(UpperCamelCase__ , UpperCamelCase__ ) ) a__ , a__ = sorted_examples[0] def is_too_big(__lowerCAmelCase : Union[str, Any] ): return tok(UpperCamelCase__ , return_tensors='pt' ).input_ids.shape[1] > max_tokens for src, tgt in tqdm(sorted_examples[1:] ): a__ = new_src + ' ' + src a__ = new_tgt + ' ' + tgt if is_too_big(UpperCamelCase__ ) or is_too_big(UpperCamelCase__ ): # cant fit, finalize example finished_src.append(UpperCamelCase__ ) finished_tgt.append(UpperCamelCase__ ) a__ , a__ = src, tgt else: # can fit, keep adding a__ , a__ = cand_src, cand_tgt # cleanup if new_src: assert new_tgt finished_src.append(UpperCamelCase__ ) finished_tgt.append(UpperCamelCase__ ) return finished_src, finished_tgt def __lowercase ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] ): a__ = Path(UpperCamelCase__ ) save_path.mkdir(exist_ok=UpperCamelCase__ ) for split in ["train"]: a__ , a__ = data_dir / F'{split}.source', data_dir / F'{split}.target' a__ = [x.rstrip() for x in Path(UpperCamelCase__ ).open().readlines()] a__ = [x.rstrip() for x in Path(UpperCamelCase__ ).open().readlines()] a__ , a__ = pack_examples(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) print(F'packed {split} split from {len(UpperCamelCase__ )} examples -> {len(UpperCamelCase__ )}.' ) Path(save_path / F'{split}.source' ).open('w' ).write('\n'.join(UpperCamelCase__ ) ) Path(save_path / F'{split}.target' ).open('w' ).write('\n'.join(UpperCamelCase__ ) ) for split in ["val", "test"]: a__ , a__ = data_dir / F'{split}.source', data_dir / F'{split}.target' shutil.copyfile(UpperCamelCase__ , save_path / F'{split}.source' ) shutil.copyfile(UpperCamelCase__ , save_path / F'{split}.target' ) def __lowercase ( ): a__ = argparse.ArgumentParser() parser.add_argument('--tok_name' , type=UpperCamelCase__ , help='like facebook/bart-large-cnn,t5-base, etc.' ) parser.add_argument('--max_seq_len' , type=UpperCamelCase__ , default=1_2_8 ) parser.add_argument('--data_dir' , type=UpperCamelCase__ ) parser.add_argument('--save_path' , type=UpperCamelCase__ ) a__ = parser.parse_args() a__ = AutoTokenizer.from_pretrained(args.tok_name ) return pack_data_dir(UpperCamelCase__ , Path(args.data_dir ) , args.max_seq_len , args.save_path ) if __name__ == "__main__": packer_cli()

import cva import numpy as np class A_ : def __init__( self , _A , _A ): '''simple docstring''' if k in (0.04, 0.06): UpperCAmelCase = k UpperCAmelCase = window_size else: raise ValueError('''invalid k value''' ) def __str__( self ): '''simple docstring''' return str(self.k ) def _lowercase ( self , _A ): '''simple docstring''' UpperCAmelCase = cva.imread(_A , 0 ) UpperCAmelCase , UpperCAmelCase = img.shape UpperCAmelCase = [] UpperCAmelCase = img.copy() UpperCAmelCase = cva.cvtColor(_A , cva.COLOR_GRAY2RGB ) UpperCAmelCase , UpperCAmelCase = np.gradient(_A ) UpperCAmelCase = dx**2 UpperCAmelCase = dy**2 UpperCAmelCase = dx * dy UpperCAmelCase = 0.04 UpperCAmelCase = self.window_size // 2 for y in range(_A , h - offset ): for x in range(_A , w - offset ): UpperCAmelCase = ixx[ y - offset : y + offset + 1, x - offset : x + offset + 1 ].sum() UpperCAmelCase = iyy[ y - offset : y + offset + 1, x - offset : x + offset + 1 ].sum() UpperCAmelCase = ixy[ y - offset : y + offset + 1, x - offset : x + offset + 1 ].sum() UpperCAmelCase = (wxx * wyy) - (wxy**2) UpperCAmelCase = wxx + wyy UpperCAmelCase = det - k * (trace**2) # Can change the value if r > 0.5: corner_list.append([x, y, r] ) color_img.itemset((y, x, 0) , 0 ) color_img.itemset((y, x, 1) , 0 ) color_img.itemset((y, x, 2) , 2_5_5 ) return color_img, corner_list if __name__ == "__main__": __A : Tuple = HarrisCorner(0.04, 3) __A , __A : List[Any] = edge_detect.detect("path_to_image") cva.imwrite("detect.png", color_img)

import numpy as np from matplotlib import pyplot as plt from sklearn import datasets def _lowerCAmelCase ( __lowerCAmelCase ) -> List[str]: """simple docstring""" return 1 / (1 + np.exp(-z )) def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: """simple docstring""" return (-y * np.log(SCREAMING_SNAKE_CASE_ ) - (1 - y) * np.log(1 - h )).mean() def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str: """simple docstring""" snake_case__ : List[Any] = np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) return np.sum(y * scores - np.log(1 + np.exp(SCREAMING_SNAKE_CASE_ ) ) ) def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=70000 ) -> Dict: """simple docstring""" snake_case__ : List[Any] = np.zeros(x.shape[1] ) for iterations in range(SCREAMING_SNAKE_CASE_ ): snake_case__ : Dict = np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) snake_case__ : List[str] = sigmoid_function(SCREAMING_SNAKE_CASE_ ) snake_case__ : Any = np.dot(x.T , h - y ) / y.size snake_case__ : int = theta - alpha * gradient # updating the weights snake_case__ : Optional[Any] = np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) snake_case__ : Optional[Any] = sigmoid_function(SCREAMING_SNAKE_CASE_ ) snake_case__ : Any = cost_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) if iterations % 100 == 0: print(f"""loss: {j} \t""" ) # printing the loss after every 100 iterations return theta # In[68]: if __name__ == "__main__": A__ = datasets.load_iris() A__ = iris.data[:, :2] A__ = (iris.target != 0) * 1 A__ = 0.1 A__ = logistic_reg(alpha, x, y, max_iterations=7_0000) print('''theta: ''', theta) # printing the theta i.e our weights vector def _lowerCAmelCase ( __lowerCAmelCase ) -> Dict: """simple docstring""" return sigmoid_function( np.dot(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ) # predicting the value of probability from the logistic regression algorithm plt.figure(figsize=(10, 6)) plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''') plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''') ((A__) , (A__)) = (x[:, 0].min(), x[:, 0].max()) ((A__) , (A__)) = (x[:, 1].min(), x[:, 1].max()) ((A__) , (A__)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max)) A__ = np.c_[xxa.ravel(), xxa.ravel()] A__ = predict_prob(grid).reshape(xxa.shape) plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''') plt.legend() plt.show()

from sklearn.metrics import mean_squared_error import datasets A__ = '''\ @article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} } ''' A__ = '''\ Mean Squared Error(MSE) is the average of the square of difference between the predicted and actual values. ''' A__ = ''' Args: predictions: array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. references: array-like of shape (n_samples,) or (n_samples, n_outputs) Ground truth (correct) target values. sample_weight: array-like of shape (n_samples,), default=None Sample weights. multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average" Defines aggregating of multiple output values. Array-like value defines weights used to average errors. "raw_values" : Returns a full set of errors in case of multioutput input. "uniform_average" : Errors of all outputs are averaged with uniform weight. squared : bool, default=True If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value. Returns: mse : mean squared error. Examples: >>> mse_metric = datasets.load_metric("mse") >>> predictions = [2.5, 0.0, 2, 8] >>> references = [3, -0.5, 2, 7] >>> results = mse_metric.compute(predictions=predictions, references=references) >>> print(results) {\'mse\': 0.375} >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False) >>> print(rmse_result) {\'mse\': 0.6123724356957945} If you\'re using multi-dimensional lists, then set the config as follows : >>> mse_metric = datasets.load_metric("mse", "multilist") >>> predictions = [[0.5, 1], [-1, 1], [7, -6]] >>> references = [[0, 2], [-1, 2], [8, -5]] >>> results = mse_metric.compute(predictions=predictions, references=references) >>> print(results) {\'mse\': 0.7083333333333334} >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\') >>> print(results) # doctest: +NORMALIZE_WHITESPACE {\'mse\': array([0.41666667, 1. ])} ''' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class a ( datasets.Metric ): def __lowerCamelCase ( self :List[Any] ): return datasets.MetricInfo( description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(self._get_feature_types() ) ,reference_urls=[ '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html''' ] ,) def __lowerCamelCase ( self :Tuple ): if self.config_name == "multilist": return { "predictions": datasets.Sequence(datasets.Value('''float''' ) ), "references": datasets.Sequence(datasets.Value('''float''' ) ), } else: return { "predictions": datasets.Value('''float''' ), "references": datasets.Value('''float''' ), } def __lowerCamelCase ( self :List[str] ,__lowercase :Optional[int] ,__lowercase :int ,__lowercase :Any=None ,__lowercase :List[str]="uniform_average" ,__lowercase :List[Any]=True ): snake_case__ : Union[str, Any] = mean_squared_error( __lowercase ,__lowercase ,sample_weight=__lowercase ,multioutput=__lowercase ,squared=__lowercase ) return {"mse": mse}

"""simple docstring""" from typing import Dict from .base import GenericTensor, Pipeline class _UpperCAmelCase ( _lowerCAmelCase ): def a ( self : Tuple , _lowercase : Dict=None , _lowercase : str=None , _lowercase : Union[str, Any]=None , **_lowercase : Tuple ): if tokenize_kwargs is None: __UpperCAmelCase = {} if truncation is not None: if "truncation" in tokenize_kwargs: raise ValueError( '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' ) __UpperCAmelCase = truncation __UpperCAmelCase = tokenize_kwargs __UpperCAmelCase = {} if return_tensors is not None: __UpperCAmelCase = return_tensors return preprocess_params, {}, postprocess_params def a ( self : int , _lowercase : Optional[Any] , **_lowercase : Union[str, Any] ): __UpperCAmelCase = self.framework __UpperCAmelCase = self.tokenizer(_lowercase , return_tensors=_lowercase , **_lowercase ) return model_inputs def a ( self : List[str] , _lowercase : Tuple ): __UpperCAmelCase = self.model(**_lowercase ) return model_outputs def a ( self : int , _lowercase : Tuple , _lowercase : str=False ): # [0] is the first available tensor, logits or last_hidden_state. if return_tensors: return model_outputs[0] if self.framework == "pt": return model_outputs[0].tolist() elif self.framework == "tf": return model_outputs[0].numpy().tolist() def __call__( self : List[Any] , *_lowercase : Optional[Any] , **_lowercase : Union[str, Any] ): return super().__call__(*_lowercase , **_lowercase )

"""simple docstring""" # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import os import platform import numpy as np import psutil import torch from accelerate import __version__ as version from accelerate.commands.config import default_config_file, load_config_from_file from ..utils import is_npu_available, is_xpu_available def lowercase__ ( snake_case_ :Union[str, Any]=None ): if subparsers is not None: __UpperCAmelCase = subparsers.add_parser('''env''' ) else: __UpperCAmelCase = argparse.ArgumentParser('''Accelerate env command''' ) parser.add_argument( '''--config_file''' , default=snake_case_ , help='''The config file to use for the default values in the launching script.''' ) if subparsers is not None: parser.set_defaults(func=snake_case_ ) return parser def lowercase__ ( snake_case_ :List[Any] ): __UpperCAmelCase = torch.__version__ __UpperCAmelCase = torch.cuda.is_available() __UpperCAmelCase = is_xpu_available() __UpperCAmelCase = is_npu_available() __UpperCAmelCase = '''Not found''' # Get the default from the config file. if args.config_file is not None or os.path.isfile(snake_case_ ): __UpperCAmelCase = load_config_from_file(args.config_file ).to_dict() __UpperCAmelCase = { '''`Accelerate` version''': version, '''Platform''': platform.platform(), '''Python version''': platform.python_version(), '''Numpy version''': np.__version__, '''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''', '''PyTorch XPU available''': str(snake_case_ ), '''PyTorch NPU available''': str(snake_case_ ), '''System RAM''': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''', } if pt_cuda_available: __UpperCAmelCase = torch.cuda.get_device_name() print('''\nCopy-and-paste the text below in your GitHub issue\n''' ) print('''\n'''.join([F'''- {prop}: {val}''' for prop, val in info.items()] ) ) print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:''' ) __UpperCAmelCase = ( '''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] ) if isinstance(snake_case_ , snake_case_ ) else F'''\t{accelerate_config}''' ) print(snake_case_ ) __UpperCAmelCase = accelerate_config return info def lowercase__ ( ): __UpperCAmelCase = env_command_parser() __UpperCAmelCase = parser.parse_args() env_command(snake_case_ ) return 0 if __name__ == "__main__": raise SystemExit(main())

import os import posixpath import uuid from dataclasses import dataclass from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union import numpy as np import pyarrow as pa import datasets from datasets.arrow_writer import ArrowWriter, ParquetWriter from datasets.config import MAX_SHARD_SIZE from datasets.filesystems import ( is_remote_filesystem, rename, ) from datasets.iterable_dataset import _BaseExamplesIterable from datasets.utils.py_utils import convert_file_size_to_int _lowerCamelCase = datasets.utils.logging.get_logger(__name__) if TYPE_CHECKING: import pyspark @dataclass class a ( datasets.BuilderConfig ): '''simple docstring''' lowerCAmelCase : Optional[datasets.Features] = None def SCREAMING_SNAKE_CASE ( __UpperCamelCase : "pyspark.sql.DataFrame" , __UpperCamelCase : List[int] , ) -> List[Any]: import pyspark def generate_fn(): UpperCAmelCase_ = df.select('''*''' , pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) ) for partition_id in partition_order: UpperCAmelCase_ = df_with_partition_id.select('''*''' ).where(f'part_id = {partition_id}' ).drop('''part_id''' ) UpperCAmelCase_ = partition_df.collect() UpperCAmelCase_ = 0 for row in rows: yield f'{partition_id}_{row_id}', row.asDict() row_id += 1 return generate_fn class a ( _BaseExamplesIterable ): '''simple docstring''' def __init__( self : int , __snake_case : "pyspark.sql.DataFrame" , __snake_case : Optional[int]=None , ): UpperCAmelCase_ = df UpperCAmelCase_ = partition_order or range(self.df.rdd.getNumPartitions() ) UpperCAmelCase_ = _generate_iterable_examples(self.df , self.partition_order ) def __iter__( self : List[str] ): yield from self.generate_examples_fn() def lowerCamelCase_ ( self : Optional[Any] , __snake_case : np.random.Generator ): UpperCAmelCase_ = list(range(self.df.rdd.getNumPartitions() ) ) generator.shuffle(__snake_case ) return SparkExamplesIterable(self.df , partition_order=__snake_case ) def lowerCamelCase_ ( self : Dict , __snake_case : int , __snake_case : int ): UpperCAmelCase_ = self.split_shard_indices_by_worker(__snake_case , __snake_case ) return SparkExamplesIterable(self.df , partition_order=__snake_case ) @property def lowerCamelCase_ ( self : Tuple ): return len(self.partition_order ) class a ( datasets.DatasetBuilder ): '''simple docstring''' lowerCAmelCase : Union[str, Any] = SparkConfig def __init__( self : int , __snake_case : "pyspark.sql.DataFrame" , __snake_case : str = None , __snake_case : str = None , **__snake_case : Tuple , ): import pyspark UpperCAmelCase_ = pyspark.sql.SparkSession.builder.getOrCreate() UpperCAmelCase_ = df UpperCAmelCase_ = working_dir super().__init__( cache_dir=__snake_case , config_name=str(self.df.semanticHash() ) , **__snake_case , ) def lowerCamelCase_ ( self : int ): # Returns the path of the created file. def create_cache_and_write_probe(__snake_case : Union[str, Any] ): # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories # already exist. os.makedirs(self._cache_dir , exist_ok=__snake_case ) UpperCAmelCase_ = os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex ) # Opening the file in append mode will create a new file unless it already exists, in which case it will not # change the file contents. open(__snake_case , '''a''' ) return [probe_file] if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ): return # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS # accessible to the driver. # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error. if self._cache_dir: UpperCAmelCase_ = ( self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(__snake_case ).collect() ) if os.path.isfile(probe[0] ): return raise ValueError( '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' ) def lowerCamelCase_ ( self : int ): return datasets.DatasetInfo(features=self.config.features ) def lowerCamelCase_ ( self : Optional[Any] , __snake_case : datasets.download.download_manager.DownloadManager ): return [datasets.SplitGenerator(name=datasets.Split.TRAIN )] def lowerCamelCase_ ( self : str , __snake_case : Union[str, Any] ): import pyspark def get_arrow_batch_size(__snake_case : str ): for batch in it: yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} ) UpperCAmelCase_ = self.df.count() UpperCAmelCase_ = df_num_rows if df_num_rows <= 1_00 else 1_00 # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample. UpperCAmelCase_ = ( self.df.limit(__snake_case ) .repartition(1 ) .mapInArrow(__snake_case , '''batch_bytes: long''' ) .agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) ) .collect()[0] .sample_bytes / sample_num_rows ) UpperCAmelCase_ = approx_bytes_per_row * df_num_rows if approx_total_size > max_shard_size: # Make sure there is at least one row per partition. UpperCAmelCase_ = min(__snake_case , int(approx_total_size / max_shard_size ) ) UpperCAmelCase_ = self.df.repartition(__snake_case ) def lowerCamelCase_ ( self : Optional[int] , __snake_case : str , __snake_case : str , __snake_case : int , ): import pyspark UpperCAmelCase_ = ParquetWriter if file_format == '''parquet''' else ArrowWriter UpperCAmelCase_ = os.path.join(self._working_dir , os.path.basename(__snake_case ) ) if self._working_dir else fpath UpperCAmelCase_ = file_format == '''parquet''' # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to # pickling the SparkContext. UpperCAmelCase_ = self.config.features UpperCAmelCase_ = self._writer_batch_size UpperCAmelCase_ = self._fs.storage_options def write_arrow(__snake_case : Union[str, Any] ): # Within the same SparkContext, no two task attempts will share the same attempt ID. UpperCAmelCase_ = pyspark.TaskContext().taskAttemptId() UpperCAmelCase_ = next(__snake_case , __snake_case ) if first_batch is None: # Some partitions might not receive any data. return pa.RecordBatch.from_arrays( [[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , ) UpperCAmelCase_ = 0 UpperCAmelCase_ = writer_class( features=__snake_case , path=working_fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , writer_batch_size=__snake_case , storage_options=__snake_case , embed_local_files=__snake_case , ) UpperCAmelCase_ = pa.Table.from_batches([first_batch] ) writer.write_table(__snake_case ) for batch in it: if max_shard_size is not None and writer._num_bytes >= max_shard_size: UpperCAmelCase_ , UpperCAmelCase_ = writer.finalize() writer.close() yield pa.RecordBatch.from_arrays( [[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , ) shard_id += 1 UpperCAmelCase_ = writer_class( features=writer._features , path=working_fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , writer_batch_size=__snake_case , storage_options=__snake_case , embed_local_files=__snake_case , ) UpperCAmelCase_ = pa.Table.from_batches([batch] ) writer.write_table(__snake_case ) if writer._num_bytes > 0: UpperCAmelCase_ , UpperCAmelCase_ = writer.finalize() writer.close() yield pa.RecordBatch.from_arrays( [[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , ) if working_fpath != fpath: for file in os.listdir(os.path.dirname(__snake_case ) ): UpperCAmelCase_ = os.path.join(os.path.dirname(__snake_case ) , os.path.basename(__snake_case ) ) shutil.move(__snake_case , __snake_case ) UpperCAmelCase_ = ( self.df.mapInArrow(__snake_case , '''task_id: long, num_examples: long, num_bytes: long''' ) .groupBy('''task_id''' ) .agg( pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , ) .collect() ) for row in stats: yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths) def lowerCamelCase_ ( self : List[str] , __snake_case : "datasets.SplitGenerator" , __snake_case : str = "arrow" , __snake_case : Optional[Union[str, int]] = None , __snake_case : Optional[int] = None , **__snake_case : int , ): self._validate_cache_dir() UpperCAmelCase_ = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE ) self._repartition_df_if_needed(__snake_case ) UpperCAmelCase_ = not is_remote_filesystem(self._fs ) UpperCAmelCase_ = os.path.join if is_local else posixpath.join UpperCAmelCase_ = '''-TTTTT-SSSSS-of-NNNNN''' UpperCAmelCase_ = F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}' UpperCAmelCase_ = path_join(self._output_dir , __snake_case ) UpperCAmelCase_ = 0 UpperCAmelCase_ = 0 UpperCAmelCase_ = 0 UpperCAmelCase_ = [] UpperCAmelCase_ = [] for task_id, content in self._prepare_split_single(__snake_case , __snake_case , __snake_case ): ( ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ) = content if num_bytes > 0: total_num_examples += num_examples total_num_bytes += num_bytes total_shards += num_shards task_id_and_num_shards.append((task_id, num_shards) ) all_shard_lengths.extend(__snake_case ) UpperCAmelCase_ = total_num_examples UpperCAmelCase_ = total_num_bytes # should rename everything at the end logger.debug(F'Renaming {total_shards} shards.' ) if total_shards > 1: UpperCAmelCase_ = all_shard_lengths # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a # pickling error due to pickling the SparkContext. UpperCAmelCase_ = self._fs # use the -SSSSS-of-NNNNN pattern def _rename_shard( __snake_case : int , __snake_case : int , __snake_case : int , ): rename( __snake_case , fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , fpath.replace('''TTTTT-SSSSS''' , F'{global_shard_id:05d}' ).replace('''NNNNN''' , F'{total_shards:05d}' ) , ) UpperCAmelCase_ = [] UpperCAmelCase_ = 0 for i in range(len(__snake_case ) ): UpperCAmelCase_ , UpperCAmelCase_ = task_id_and_num_shards[i] for shard_id in range(__snake_case ): args.append([task_id, shard_id, global_shard_id] ) global_shard_id += 1 self._spark.sparkContext.parallelize(__snake_case , len(__snake_case ) ).map(lambda __snake_case : _rename_shard(*__snake_case ) ).collect() else: # don't use any pattern UpperCAmelCase_ = 0 UpperCAmelCase_ = task_id_and_num_shards[0][0] self._rename( fpath.replace('''SSSSS''' , F'{shard_id:05d}' ).replace('''TTTTT''' , F'{task_id:05d}' ) , fpath.replace(__snake_case , '''''' ) , ) def lowerCamelCase_ ( self : Optional[Any] , __snake_case : "datasets.SplitGenerator" , ): return SparkExamplesIterable(self.df )

from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _lowerCamelCase = { 'configuration_conditional_detr': [ 'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConditionalDetrConfig', 'ConditionalDetrOnnxConfig', ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase = ['ConditionalDetrFeatureExtractor'] _lowerCamelCase = ['ConditionalDetrImageProcessor'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase = [ 'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST', 'ConditionalDetrForObjectDetection', 'ConditionalDetrForSegmentation', 'ConditionalDetrModel', 'ConditionalDetrPreTrainedModel', ] if TYPE_CHECKING: from .configuration_conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, ConditionalDetrConfig, ConditionalDetrOnnxConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor from .image_processing_conditional_detr import ConditionalDetrImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrModel, ConditionalDetrPreTrainedModel, ) else: import sys _lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

'''simple docstring''' from typing import List, Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_herbert import HerbertTokenizer __lowerCAmelCase = logging.get_logger(__name__) __lowerCAmelCase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''} __lowerCAmelCase = { '''vocab_file''': { '''allegro/herbert-base-cased''': '''https://huggingface.co./allegro/herbert-base-cased/resolve/main/vocab.json''' }, '''merges_file''': { '''allegro/herbert-base-cased''': '''https://huggingface.co./allegro/herbert-base-cased/resolve/main/merges.txt''' }, } __lowerCAmelCase = {'''allegro/herbert-base-cased''': 514} __lowerCAmelCase = {} class __magic_name__ ( _UpperCamelCase ): lowerCAmelCase : str = VOCAB_FILES_NAMES lowerCAmelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP lowerCAmelCase : List[Any] = PRETRAINED_INIT_CONFIGURATION lowerCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCAmelCase : Any = HerbertTokenizer def __init__( self : Dict ,_UpperCAmelCase : Dict=None ,_UpperCAmelCase : int=None ,_UpperCAmelCase : List[Any]=None ,_UpperCAmelCase : Optional[int]="<s>" ,_UpperCAmelCase : str="<unk>" ,_UpperCAmelCase : Dict="<pad>" ,_UpperCAmelCase : List[Any]="<mask>" ,_UpperCAmelCase : Optional[int]="</s>" ,**_UpperCAmelCase : Union[str, Any] ,): super().__init__( _UpperCAmelCase ,_UpperCAmelCase ,tokenizer_file=_UpperCAmelCase ,cls_token=_UpperCAmelCase ,unk_token=_UpperCAmelCase ,pad_token=_UpperCAmelCase ,mask_token=_UpperCAmelCase ,sep_token=_UpperCAmelCase ,**_UpperCAmelCase ,) def __lowercase ( self : str ,_UpperCAmelCase : List[int] ,_UpperCAmelCase : Optional[List[int]] = None ): _a : List[Any] = [self.cls_token_id] _a : Union[str, Any] = [self.sep_token_id] if token_ids_a is None: return cls + token_ids_a + sep return cls + token_ids_a + sep + token_ids_a + sep def __lowercase ( self : Optional[int] ,_UpperCAmelCase : List[int] ,_UpperCAmelCase : Optional[List[int]] = None ,_UpperCAmelCase : bool = False ): if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase ,token_ids_a=_UpperCAmelCase ,already_has_special_tokens=_UpperCAmelCase ) if token_ids_a is None: return [1] + ([0] * len(_UpperCAmelCase )) + [1] return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1] def __lowercase ( self : int ,_UpperCAmelCase : List[int] ,_UpperCAmelCase : Optional[List[int]] = None ): _a : Tuple = [self.sep_token_id] _a : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def __lowercase ( self : List[str] ,_UpperCAmelCase : str ,_UpperCAmelCase : Optional[str] = None ): _a : Union[str, Any] = self._tokenizer.model.save(_UpperCAmelCase ,name=_UpperCAmelCase ) return tuple(_UpperCAmelCase )

'''simple docstring''' from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase__ : Any = logging.get_logger(__name__) UpperCamelCase__ : Optional[int] = { 'distilbert-base-uncased': 'https://huggingface.co./distilbert-base-uncased/resolve/main/config.json', 'distilbert-base-uncased-distilled-squad': ( 'https://huggingface.co./distilbert-base-uncased-distilled-squad/resolve/main/config.json' ), 'distilbert-base-cased': 'https://huggingface.co./distilbert-base-cased/resolve/main/config.json', 'distilbert-base-cased-distilled-squad': ( 'https://huggingface.co./distilbert-base-cased-distilled-squad/resolve/main/config.json' ), 'distilbert-base-german-cased': 'https://huggingface.co./distilbert-base-german-cased/resolve/main/config.json', 'distilbert-base-multilingual-cased': ( 'https://huggingface.co./distilbert-base-multilingual-cased/resolve/main/config.json' ), 'distilbert-base-uncased-finetuned-sst-2-english': ( 'https://huggingface.co./distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json' ), } class _lowerCAmelCase ( __A ): """simple docstring""" lowerCamelCase = '''distilbert''' lowerCamelCase = { '''hidden_size''': '''dim''', '''num_attention_heads''': '''n_heads''', '''num_hidden_layers''': '''n_layers''', } def __init__( self , _lowerCamelCase=3_0522 , _lowerCamelCase=512 , _lowerCamelCase=False , _lowerCamelCase=6 , _lowerCamelCase=12 , _lowerCamelCase=768 , _lowerCamelCase=4 * 768 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase="gelu" , _lowerCamelCase=0.02 , _lowerCamelCase=0.1 , _lowerCamelCase=0.2 , _lowerCamelCase=0 , **_lowerCamelCase , ) -> Optional[Any]: A_ : Tuple = vocab_size A_ : List[Any] = max_position_embeddings A_ : int = sinusoidal_pos_embds A_ : int = n_layers A_ : str = n_heads A_ : Optional[int] = dim A_ : int = hidden_dim A_ : Tuple = dropout A_ : List[Any] = attention_dropout A_ : int = activation A_ : Dict = initializer_range A_ : List[Any] = qa_dropout A_ : int = seq_classif_dropout super().__init__(**_lowerCamelCase , pad_token_id=_lowerCamelCase ) class _lowerCAmelCase ( __A ): """simple docstring""" @property def UpperCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": A_ : Union[str, Any] = {0: """batch""", 1: """choice""", 2: """sequence"""} else: A_ : int = {0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ] )

'''simple docstring''' import re from filelock import FileLock try: import nltk _UpperCAmelCase : str = True except (ImportError, ModuleNotFoundError): _UpperCAmelCase : List[Any] = False if NLTK_AVAILABLE: with FileLock(""".lock""") as lock: nltk.download("""punkt""", quiet=True) def __magic_name__( lowerCamelCase): re.sub('''<n>''', '''''', lowerCamelCase) # remove pegasus newline char assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)" return "\n".join(nltk.sent_tokenize(lowerCamelCase))

'''simple docstring''' from typing import Callable, Dict, Optional, Tuple import torch from torch import nn from torch.distributions import ( AffineTransform, Distribution, Independent, NegativeBinomial, Normal, StudentT, TransformedDistribution, ) class a__ ( __A ): """simple docstring""" def __init__(self , __lowercase , __lowercase=None , __lowercase=None , __lowercase=0 ): __lowerCAmelCase = 1.0 if scale is None else scale __lowerCAmelCase = 0.0 if loc is None else loc super().__init__(__lowercase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=__lowercase )] ) @property def _snake_case (self ): return self.base_dist.mean * self.scale + self.loc @property def _snake_case (self ): return self.base_dist.variance * self.scale**2 @property def _snake_case (self ): return self.variance.sqrt() class a__ ( nn.Module ): """simple docstring""" def __init__(self , __lowercase , __lowercase , __lowercase , **__lowercase ): super().__init__(**__lowercase ) __lowerCAmelCase = args_dim __lowerCAmelCase = nn.ModuleList([nn.Linear(__lowercase , __lowercase ) for dim in args_dim.values()] ) __lowerCAmelCase = domain_map def _snake_case (self , __lowercase ): __lowerCAmelCase = [proj(__lowercase ) for proj in self.proj] return self.domain_map(*__lowercase ) class a__ ( nn.Module ): """simple docstring""" def __init__(self , __lowercase ): super().__init__() __lowerCAmelCase = function def _snake_case (self , __lowercase , *__lowercase ): return self.function(__lowercase , *__lowercase ) class a__ : """simple docstring""" __UpperCamelCase : type __UpperCamelCase : int __UpperCamelCase : Dict[str, int] def __init__(self , __lowercase = 1 ): __lowerCAmelCase = dim __lowerCAmelCase = {k: dim * self.args_dim[k] for k in self.args_dim} def _snake_case (self , __lowercase ): if self.dim == 1: return self.distribution_class(*__lowercase ) else: return Independent(self.distribution_class(*__lowercase ) , 1 ) def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None , ): __lowerCAmelCase = self._base_distribution(__lowercase ) if loc is None and scale is None: return distr else: return AffineTransformed(__lowercase , loc=__lowercase , scale=__lowercase , event_dim=self.event_dim ) @property def _snake_case (self ): return () if self.dim == 1 else (self.dim,) @property def _snake_case (self ): return len(self.event_shape ) @property def _snake_case (self ): return 0.0 def _snake_case (self , __lowercase ): return ParameterProjection( in_features=__lowercase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , ) def _snake_case (self , *__lowercase ): raise NotImplementedError() @staticmethod def _snake_case (__lowercase ): return (x + torch.sqrt(torch.square(__lowercase ) + 4.0 )) / 2.0 class a__ ( __A ): """simple docstring""" __UpperCamelCase : Dict[str, int] = {"df": 1, "loc": 1, "scale": 1} __UpperCamelCase : type = StudentT @classmethod def _snake_case (cls , __lowercase , __lowercase , __lowercase ): __lowerCAmelCase = cls.squareplus(__lowercase ).clamp_min(torch.finfo(scale.dtype ).eps ) __lowerCAmelCase = 2.0 + cls.squareplus(__lowercase ) return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 ) class a__ ( __A ): """simple docstring""" __UpperCamelCase : Dict[str, int] = {"loc": 1, "scale": 1} __UpperCamelCase : type = Normal @classmethod def _snake_case (cls , __lowercase , __lowercase ): __lowerCAmelCase = cls.squareplus(__lowercase ).clamp_min(torch.finfo(scale.dtype ).eps ) return loc.squeeze(-1 ), scale.squeeze(-1 ) class a__ ( __A ): """simple docstring""" __UpperCamelCase : Dict[str, int] = {"total_count": 1, "logits": 1} __UpperCamelCase : type = NegativeBinomial @classmethod def _snake_case (cls , __lowercase , __lowercase ): __lowerCAmelCase = cls.squareplus(__lowercase ) return total_count.squeeze(-1 ), logits.squeeze(-1 ) def _snake_case (self , __lowercase ): __lowerCAmelCase , __lowerCAmelCase = distr_args if self.dim == 1: return self.distribution_class(total_count=__lowercase , logits=__lowercase ) else: return Independent(self.distribution_class(total_count=__lowercase , logits=__lowercase ) , 1 ) def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None ): __lowerCAmelCase , __lowerCAmelCase = distr_args if scale is not None: # See scaling property of Gamma. logits += scale.log() return self._base_distribution((total_count, logits) )

from ...utils import ( OptionalDependencyNotAvailable, is_flax_available, is_torch_available, is_transformers_available, ) try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .multicontrolnet import MultiControlNetModel from .pipeline_controlnet import StableDiffusionControlNetPipeline from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline if is_transformers_available() and is_flax_available(): from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline

import argparse import json import os import torch from transformers.file_utils import has_file from diffusers import UNetaDConditionModel, UNetaDModel SCREAMING_SNAKE_CASE :Union[str, Any] = False SCREAMING_SNAKE_CASE :Any = True SCREAMING_SNAKE_CASE :Tuple = False if __name__ == "__main__": SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser() parser.add_argument( '--repo_path', default=None, type=str, required=True, help='The config json file corresponding to the architecture.', ) parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.') SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args() SCREAMING_SNAKE_CASE :Dict = { 'image_size': 'sample_size', 'num_res_blocks': 'layers_per_block', 'block_channels': 'block_out_channels', 'down_blocks': 'down_block_types', 'up_blocks': 'up_block_types', 'downscale_freq_shift': 'freq_shift', 'resnet_num_groups': 'norm_num_groups', 'resnet_act_fn': 'act_fn', 'resnet_eps': 'norm_eps', 'num_head_channels': 'attention_head_dim', } SCREAMING_SNAKE_CASE :Optional[int] = { 'time_steps': 'time_proj', 'mid': 'mid_block', 'downsample_blocks': 'down_blocks', 'upsample_blocks': 'up_blocks', } SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet' with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader: SCREAMING_SNAKE_CASE :Dict = reader.read() SCREAMING_SNAKE_CASE :List[str] = json.loads(text) if do_only_config: for key in config_parameters_to_change.keys(): config.pop(key, None) if has_file(args.repo_path, 'config.json'): SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config) else: SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel SCREAMING_SNAKE_CASE :List[str] = class_name(**config) if do_only_config: model.save_config(os.path.join(args.repo_path, subfolder)) SCREAMING_SNAKE_CASE :List[str] = dict(model.config) if do_only_renaming: for key, value in config_parameters_to_change.items(): if key in config: SCREAMING_SNAKE_CASE :Optional[Any] = config[key] del config[key] SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']] SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']] if do_only_weights: SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin')) SCREAMING_SNAKE_CASE :Any = {} for param_key, param_value in state_dict.items(): if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'): continue SCREAMING_SNAKE_CASE :List[str] = False for key, new_key in key_parameters_to_change.items(): if not has_changed and param_key.split('.')[0] == key: SCREAMING_SNAKE_CASE :List[Any] = param_value SCREAMING_SNAKE_CASE :str = True if not has_changed: SCREAMING_SNAKE_CASE :List[str] = param_value model.load_state_dict(new_state_dict) model.save_pretrained(os.path.join(args.repo_path, subfolder))

'''simple docstring''' import json import os import unittest from transformers.models.roc_bert.tokenization_roc_bert import ( VOCAB_FILES_NAMES, RoCBertBasicTokenizer, RoCBertTokenizer, RoCBertWordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english @require_tokenizers class __snake_case ( _SCREAMING_SNAKE_CASE ,unittest.TestCase): """simple docstring""" lowercase = RoCBertTokenizer lowercase = None lowercase = False lowercase = True lowercase = filter_non_english def __lowercase ( self : List[Any] ) -> Union[str, Any]: super().setUp() lowerCAmelCase_ : Dict = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""] lowerCAmelCase_ : Union[str, Any] = {} lowerCAmelCase_ : Union[str, Any] = {} for i, value in enumerate(lowerCamelCase ): lowerCAmelCase_ : int = i lowerCAmelCase_ : Optional[Any] = i lowerCAmelCase_ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) lowerCAmelCase_ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_shape_file"""] ) lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_pronunciation_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) with open(self.word_shape_file , """w""" , encoding="""utf-8""" ) as word_shape_writer: json.dump(lowerCamelCase , lowerCamelCase , ensure_ascii=lowerCamelCase ) with open(self.word_pronunciation_file , """w""" , encoding="""utf-8""" ) as word_pronunciation_writer: json.dump(lowerCamelCase , lowerCamelCase , ensure_ascii=lowerCamelCase ) def __lowercase ( self : List[str] ) -> Tuple: lowerCAmelCase_ : List[str] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file ) lowerCAmelCase_ : str = tokenizer.tokenize("""你好[SEP]你是谁""" ) self.assertListEqual(lowerCamelCase , ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] ) self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] ) self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase ) , [5, 6, 2, 5, 7, 8] ) def __lowercase ( self : Dict ) -> str: lowerCAmelCase_ : List[Any] = RoCBertBasicTokenizer() self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] ) def __lowercase ( self : List[str] ) -> Tuple: lowerCAmelCase_ : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def __lowercase ( self : Dict ) -> Optional[int]: lowerCAmelCase_ : Tuple = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] ) def __lowercase ( self : Optional[Any] ) -> Any: lowerCAmelCase_ : List[Any] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def __lowercase ( self : Union[str, Any] ) -> List[str]: lowerCAmelCase_ : Optional[int] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def __lowercase ( self : int ) -> Optional[Any]: lowerCAmelCase_ : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def __lowercase ( self : List[Any] ) -> Union[str, Any]: lowerCAmelCase_ : str = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def __lowercase ( self : Union[str, Any] ) -> str: lowerCAmelCase_ : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , strip_accents=lowerCamelCase ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def __lowercase ( self : Tuple ) -> Any: lowerCAmelCase_ : Dict = RoCBertBasicTokenizer(do_lower_case=lowerCamelCase , never_split=["""[UNK]"""] ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] ) def __lowercase ( self : List[str] ) -> str: lowerCAmelCase_ : str = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""] lowerCAmelCase_ : Dict = {} for i, token in enumerate(lowerCamelCase ): lowerCAmelCase_ : Any = i lowerCAmelCase_ : Optional[Any] = RoCBertWordpieceTokenizer(vocab=lowerCamelCase , unk_token="""[UNK]""" ) self.assertListEqual(tokenizer.tokenize("""""" ) , [] ) self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] ) self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] ) def __lowercase ( self : Optional[int] ) -> List[Any]: self.assertTrue(_is_whitespace(""" """ ) ) self.assertTrue(_is_whitespace("""\t""" ) ) self.assertTrue(_is_whitespace("""\r""" ) ) self.assertTrue(_is_whitespace("""\n""" ) ) self.assertTrue(_is_whitespace("""\u00A0""" ) ) self.assertFalse(_is_whitespace("""A""" ) ) self.assertFalse(_is_whitespace("""-""" ) ) def __lowercase ( self : Tuple ) -> Dict: self.assertTrue(_is_control("""\u0005""" ) ) self.assertFalse(_is_control("""A""" ) ) self.assertFalse(_is_control(""" """ ) ) self.assertFalse(_is_control("""\t""" ) ) self.assertFalse(_is_control("""\r""" ) ) def __lowercase ( self : str ) -> Tuple: self.assertTrue(_is_punctuation("""-""" ) ) self.assertTrue(_is_punctuation("""$""" ) ) self.assertTrue(_is_punctuation("""`""" ) ) self.assertTrue(_is_punctuation(""".""" ) ) self.assertFalse(_is_punctuation("""A""" ) ) self.assertFalse(_is_punctuation(""" """ ) ) def __lowercase ( self : str ) -> Optional[Any]: lowerCAmelCase_ : int = self.get_tokenizer() # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340 self.assertListEqual([tokenizer.tokenize(lowerCamelCase ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) if self.test_rust_tokenizer: lowerCAmelCase_ : Tuple = self.get_rust_tokenizer() self.assertListEqual( [rust_tokenizer.tokenize(lowerCamelCase ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) def __lowercase ( self : Optional[Any] ) -> Dict: for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ): lowerCAmelCase_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase ) lowerCAmelCase_ : Any = F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.' lowerCAmelCase_ : int = tokenizer_r.encode_plus( lowerCamelCase , return_attention_mask=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_offsets_mapping=lowerCamelCase , add_special_tokens=lowerCamelCase , ) lowerCAmelCase_ : Any = tokenizer_r.do_lower_case if hasattr(lowerCamelCase , """do_lower_case""" ) else False lowerCAmelCase_ : Dict = ( [ ((0, 0), tokenizer_r.cls_token), ((0, 1), """A"""), ((1, 2), ""","""), ((3, 5), """na"""), ((5, 6), """##ï"""), ((6, 8), """##ve"""), ((9, 15), tokenizer_r.mask_token), ((16, 21), """Allen"""), ((21, 23), """##NL"""), ((23, 24), """##P"""), ((25, 33), """sentence"""), ((33, 34), """."""), ((0, 0), tokenizer_r.sep_token), ] if not do_lower_case else [ ((0, 0), tokenizer_r.cls_token), ((0, 1), """a"""), ((1, 2), ""","""), ((3, 8), """naive"""), ((9, 15), tokenizer_r.mask_token), ((16, 21), """allen"""), ((21, 23), """##nl"""), ((23, 24), """##p"""), ((25, 33), """sentence"""), ((33, 34), """."""), ((0, 0), tokenizer_r.sep_token), ] ) self.assertEqual( [e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) ) self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] ) def __lowercase ( self : List[Any] ) -> Tuple: lowerCAmelCase_ : Optional[Any] = ["""的""", """人""", """有"""] lowerCAmelCase_ : int = """""".join(lowerCamelCase ) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ): lowerCAmelCase_ : str = True lowerCAmelCase_ : List[str] = self.tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase ) lowerCAmelCase_ : Tuple = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase ) lowerCAmelCase_ : Union[str, Any] = tokenizer_p.encode(lowerCamelCase , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : int = tokenizer_r.encode(lowerCamelCase , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : Any = tokenizer_r.convert_ids_to_tokens(lowerCamelCase ) lowerCAmelCase_ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(lowerCamelCase ) # it is expected that each Chinese character is not preceded by "##" self.assertListEqual(lowerCamelCase , lowerCamelCase ) self.assertListEqual(lowerCamelCase , lowerCamelCase ) lowerCAmelCase_ : Dict = False lowerCAmelCase_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase ) lowerCAmelCase_ : Any = self.tokenizer_class.from_pretrained(lowerCamelCase , **lowerCamelCase ) lowerCAmelCase_ : Tuple = tokenizer_r.encode(lowerCamelCase , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : List[Any] = tokenizer_p.encode(lowerCamelCase , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : Any = tokenizer_r.convert_ids_to_tokens(lowerCamelCase ) lowerCAmelCase_ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(lowerCamelCase ) # it is expected that only the first Chinese character is not preceded by "##". lowerCAmelCase_ : Dict = [ F'##{token}' if idx != 0 else token for idx, token in enumerate(lowerCamelCase ) ] self.assertListEqual(lowerCamelCase , lowerCamelCase ) self.assertListEqual(lowerCamelCase , lowerCamelCase ) @slow def __lowercase ( self : Union[str, Any] ) -> Tuple: lowerCAmelCase_ : Optional[Any] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file ) lowerCAmelCase_ : int = tokenizer.encode("""你好""" , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : Optional[int] = tokenizer.encode("""你是谁""" , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : List[Any] = tokenizer.build_inputs_with_special_tokens(lowerCamelCase ) lowerCAmelCase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowerCamelCase , lowerCamelCase ) assert encoded_sentence == [1] + text + [2] assert encoded_pair == [1] + text + [2] + text_a + [2] def __lowercase ( self : Any ) -> List[str]: lowerCAmelCase_ : List[Any] = self.get_tokenizers(do_lower_case=lowerCamelCase ) for tokenizer in tokenizers: with self.subTest(F'{tokenizer.__class__.__name__}' ): lowerCAmelCase_ : List[Any] = """你好，你是谁""" lowerCAmelCase_ : Any = tokenizer.tokenize(lowerCamelCase ) lowerCAmelCase_ : List[str] = tokenizer.convert_tokens_to_ids(lowerCamelCase ) lowerCAmelCase_ : Optional[Any] = tokenizer.convert_tokens_to_shape_ids(lowerCamelCase ) lowerCAmelCase_ : int = tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase ) lowerCAmelCase_ : List[str] = tokenizer.prepare_for_model( lowerCamelCase , lowerCamelCase , lowerCamelCase , add_special_tokens=lowerCamelCase ) lowerCAmelCase_ : Dict = tokenizer.encode_plus(lowerCamelCase , add_special_tokens=lowerCamelCase ) self.assertEqual(lowerCamelCase , lowerCamelCase )

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available __A : List[Any] = { "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"], "tokenization_roc_bert": ["RoCBertTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: pass try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __A : Union[str, Any] = [ "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RoCBertForCausalLM", "RoCBertForMaskedLM", "RoCBertForMultipleChoice", "RoCBertForPreTraining", "RoCBertForQuestionAnswering", "RoCBertForSequenceClassification", "RoCBertForTokenClassification", "RoCBertLayer", "RoCBertModel", "RoCBertPreTrainedModel", "load_tf_weights_in_roc_bert", ] if TYPE_CHECKING: from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig from .tokenization_roc_bert import RoCBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: raise OptionalDependencyNotAvailable() try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roc_bert import ( ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, RoCBertForCausalLM, RoCBertForMaskedLM, RoCBertForMultipleChoice, RoCBertForPreTraining, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertLayer, RoCBertModel, RoCBertPreTrainedModel, load_tf_weights_in_roc_bert, ) else: import sys __A : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

"""simple docstring""" import logging import torch from accelerate import Accelerator from arguments import EvaluationArguments from datasets import load_dataset from torch.utils.data import IterableDataset from torch.utils.data.dataloader import DataLoader from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed class lowerCAmelCase__ ( lowercase ): '''simple docstring''' def __init__( self , lowercase , lowercase , lowercase=1024 , lowercase=1024 , lowercase=3.6 ): _lowerCamelCase : Union[str, Any] = tokenizer _lowerCamelCase : Any = tokenizer.bos_token_id _lowerCamelCase : Optional[int] = dataset _lowerCamelCase : Optional[int] = seq_length _lowerCamelCase : Union[str, Any] = seq_length * chars_per_token * num_of_sequences def __iter__( self ): _lowerCamelCase : Optional[int] = iter(self.dataset ) _lowerCamelCase : Dict = True while more_examples: _lowerCamelCase, _lowerCamelCase : Dict = [], 0 while True: if buffer_len >= self.input_characters: break try: buffer.append(next(lowercase )['content'] ) buffer_len += len(buffer[-1] ) except StopIteration: _lowerCamelCase : Optional[Any] = False break _lowerCamelCase : Optional[Any] = tokenizer(lowercase , truncation=lowercase )['input_ids'] _lowerCamelCase : Any = [] for tokenized_input in tokenized_inputs: all_token_ids.extend(tokenized_input + [self.concat_token_id] ) for i in range(0 , len(lowercase ) , self.seq_length ): _lowerCamelCase : List[str] = all_token_ids[i : i + self.seq_length] if len(lowercase ) == self.seq_length: yield torch.tensor(lowercase ) def _snake_case ( lowercase__ ): _lowerCamelCase : List[Any] = {'streaming': True} _lowerCamelCase : str = load_dataset(args.dataset_name , split='train' , **lowercase__ ) _lowerCamelCase : Dict = ConstantLengthDataset(lowercase__ , lowercase__ , seq_length=args.seq_length ) _lowerCamelCase : str = DataLoader(lowercase__ , batch_size=args.batch_size ) return eval_dataloader def _snake_case ( lowercase__ ): model.eval() _lowerCamelCase : Tuple = [] for step, batch in enumerate(lowercase__ ): with torch.no_grad(): _lowerCamelCase : str = model(lowercase__ , labels=lowercase__ ) _lowerCamelCase : Optional[Any] = outputs.loss.repeat(args.batch_size ) losses.append(accelerator.gather(lowercase__ ) ) if args.max_eval_steps > 0 and step >= args.max_eval_steps: break _lowerCamelCase : str = torch.mean(torch.cat(lowercase__ ) ) try: _lowerCamelCase : Any = torch.exp(lowercase__ ) except OverflowError: _lowerCamelCase : int = float('inf' ) return loss.item(), perplexity.item() # Setup Accelerator lowercase__ = Accelerator() # Parse configuration lowercase__ = HfArgumentParser(EvaluationArguments) lowercase__ = parser.parse_args() set_seed(args.seed) # Logging lowercase__ = logging.getLogger(__name__) logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO ) # Load model and tokenizer lowercase__ = AutoModelForCausalLM.from_pretrained(args.model_ckpt) lowercase__ = AutoTokenizer.from_pretrained(args.model_ckpt) # Load dataset and dataloader lowercase__ = create_dataloader(args) # Prepare everything with our `accelerator`. lowercase__ , lowercase__ = accelerator.prepare(model, eval_dataloader) # Evaluate and save the last checkpoint logger.info("""Evaluating and saving model after training""") lowercase__ , lowercase__ = evaluate(args) logger.info(F"loss/eval: {eval_loss}, perplexity: {perplexity}")

def __UpperCamelCase ( _A : int ) ->int: """simple docstring""" assert ( isinstance(_A , _A ) and number_of_steps > 0 ), f'number_of_steps needs to be positive integer, your input {number_of_steps}' if number_of_steps == 1: return 1 lowerCamelCase_ , lowerCamelCase_ =1, 1 for _ in range(number_of_steps - 1 ): lowerCamelCase_ , lowerCamelCase_ =current + previous, current return current if __name__ == "__main__": import doctest doctest.testmod()

import json import os import unittest from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer from ...test_tokenization_common import TokenizerTesterMixin class A__ ( __snake_case , unittest.TestCase ): _UpperCAmelCase :List[str] = CTRLTokenizer _UpperCAmelCase :Any = False _UpperCAmelCase :Tuple = False def __UpperCamelCase( self ): '''simple docstring''' super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt UpperCamelCase : Union[str, Any] = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"] UpperCamelCase : Tuple = dict(zip(A_ , range(len(A_ ) ) ) ) UpperCamelCase : Optional[Any] = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""] UpperCamelCase : Any = {"unk_token": "<unk>"} UpperCamelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] ) UpperCamelCase : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] ) with open(self.vocab_file , "w" , encoding="utf-8" ) as fp: fp.write(json.dumps(A_ ) + "\n" ) with open(self.merges_file , "w" , encoding="utf-8" ) as fp: fp.write("\n".join(A_ ) ) def __UpperCamelCase( self , **A_ ): '''simple docstring''' kwargs.update(self.special_tokens_map ) return CTRLTokenizer.from_pretrained(self.tmpdirname , **A_ ) def __UpperCamelCase( self , A_ ): '''simple docstring''' UpperCamelCase : Any = "adapt react readapt apt" UpperCamelCase : str = "adapt react readapt apt" return input_text, output_text def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : List[Any] = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map ) UpperCamelCase : int = "adapt react readapt apt" UpperCamelCase : Dict = "adapt re@@ a@@ c@@ t re@@ adapt apt".split() UpperCamelCase : List[Any] = tokenizer.tokenize(A_ ) self.assertListEqual(A_ , A_ ) UpperCamelCase : List[str] = tokens + [tokenizer.unk_token] UpperCamelCase : Any = [0, 1, 2, 4, 5, 1, 0, 3, 6] self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ ) , A_ )

from __future__ import annotations import inspect import unittest import numpy as np from transformers import DeiTConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, ) from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import DeiTImageProcessor class A__ : def __init__( self , A_ , A_=13 , A_=30 , A_=2 , A_=3 , A_=True , A_=True , A_=32 , A_=2 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=10 , A_=0.02 , A_=3 , A_=None , A_=2 , ): '''simple docstring''' UpperCamelCase : List[str] = parent UpperCamelCase : Tuple = batch_size UpperCamelCase : Union[str, Any] = image_size UpperCamelCase : Optional[int] = patch_size UpperCamelCase : List[str] = num_channels UpperCamelCase : Any = is_training UpperCamelCase : Dict = use_labels UpperCamelCase : List[str] = hidden_size UpperCamelCase : Dict = num_hidden_layers UpperCamelCase : Union[str, Any] = num_attention_heads UpperCamelCase : str = intermediate_size UpperCamelCase : Optional[int] = hidden_act UpperCamelCase : List[Any] = hidden_dropout_prob UpperCamelCase : Dict = attention_probs_dropout_prob UpperCamelCase : List[Any] = type_sequence_label_size UpperCamelCase : List[str] = initializer_range UpperCamelCase : Union[str, Any] = scope UpperCamelCase : Union[str, Any] = encoder_stride # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens) UpperCamelCase : Optional[Any] = (image_size // patch_size) ** 2 UpperCamelCase : int = num_patches + 2 def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) UpperCamelCase : Tuple = None if self.use_labels: UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCamelCase : Union[str, Any] = self.get_config() return config, pixel_values, labels def __UpperCamelCase( self ): '''simple docstring''' return DeiTConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , ) def __UpperCamelCase( self , A_ , A_ , A_ ): '''simple docstring''' UpperCamelCase : Optional[Any] = TFDeiTModel(config=A_ ) UpperCamelCase : Tuple = model(A_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def __UpperCamelCase( self , A_ , A_ , A_ ): '''simple docstring''' UpperCamelCase : List[str] = TFDeiTForMaskedImageModeling(config=A_ ) UpperCamelCase : Optional[Any] = model(A_ ) self.parent.assertEqual( result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images UpperCamelCase : Dict = 1 UpperCamelCase : Optional[Any] = TFDeiTForMaskedImageModeling(A_ ) UpperCamelCase : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) UpperCamelCase : Any = model(A_ ) self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) ) def __UpperCamelCase( self , A_ , A_ , A_ ): '''simple docstring''' UpperCamelCase : Optional[Any] = self.type_sequence_label_size UpperCamelCase : List[Any] = TFDeiTForImageClassification(A_ ) UpperCamelCase : Optional[int] = model(A_ , labels=A_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) # test greyscale images UpperCamelCase : List[Any] = 1 UpperCamelCase : Optional[Any] = TFDeiTForImageClassification(A_ ) UpperCamelCase : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) UpperCamelCase : List[Any] = model(A_ , labels=A_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : Union[str, Any] = self.prepare_config_and_inputs() UpperCamelCase , UpperCamelCase , UpperCamelCase : int = config_and_inputs UpperCamelCase : Union[str, Any] = {"pixel_values": pixel_values} return config, inputs_dict @require_tf class A__ ( __snake_case , __snake_case , unittest.TestCase ): _UpperCAmelCase :str = ( ( TFDeiTModel, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, ) if is_tf_available() else () ) _UpperCAmelCase :Tuple = ( { 'feature-extraction': TFDeiTModel, 'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher), } if is_tf_available() else {} ) _UpperCAmelCase :Dict = False _UpperCAmelCase :List[str] = False _UpperCAmelCase :Optional[Any] = False _UpperCAmelCase :Optional[int] = False def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : Union[str, Any] = TFDeiTModelTester(self ) UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=A_ , has_text_modality=A_ , hidden_size=37 ) def __UpperCamelCase( self ): '''simple docstring''' self.config_tester.run_common_tests() @unittest.skip(reason="DeiT does not use inputs_embeds" ) def __UpperCamelCase( self ): '''simple docstring''' pass def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase , UpperCamelCase : str = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase : Optional[int] = model_class(A_ ) self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) ) UpperCamelCase : Union[str, Any] = model.get_output_embeddings() self.assertTrue(x is None or isinstance(A_ , tf.keras.layers.Dense ) ) def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase , UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase : str = model_class(A_ ) UpperCamelCase : List[str] = inspect.signature(model.call ) # signature.parameters is an OrderedDict => so arg_names order is deterministic UpperCamelCase : Optional[Any] = [*signature.parameters.keys()] UpperCamelCase : Any = ["pixel_values"] self.assertListEqual(arg_names[:1] , A_ ) def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*A_ ) def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*A_ ) def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*A_ ) def __UpperCamelCase( self , A_ , A_ , A_=False ): '''simple docstring''' UpperCamelCase : List[str] = super()._prepare_for_class(A_ , A_ , return_labels=A_ ) if return_labels: if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters: del inputs_dict["labels"] return inputs_dict @slow def __UpperCamelCase( self ): '''simple docstring''' for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCamelCase : str = TFDeiTModel.from_pretrained(A_ ) self.assertIsNotNone(A_ ) def A_ ( ) -> str: UpperCamelCase : Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ) return image @require_tf @require_vision class A__ ( unittest.TestCase ): @cached_property def __UpperCamelCase( self ): '''simple docstring''' return ( DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" ) if is_vision_available() else None ) @slow def __UpperCamelCase( self ): '''simple docstring''' UpperCamelCase : str = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ) UpperCamelCase : List[Any] = self.default_image_processor UpperCamelCase : Union[str, Any] = prepare_img() UpperCamelCase : Union[str, Any] = image_processor(images=A_ , return_tensors="tf" ) # forward pass UpperCamelCase : str = model(**A_ ) # verify the logits UpperCamelCase : Dict = tf.TensorShape((1, 1000) ) self.assertEqual(outputs.logits.shape , A_ ) UpperCamelCase : Tuple = tf.constant([-1.02_66, 0.19_12, -1.28_61] ) self.assertTrue(np.allclose(outputs.logits[0, :3] , A_ , atol=1e-4 ) )

"""simple docstring""" import argparse import json import os from collections import OrderedDict import numpy as np import tensorflow as tf import torch def __A ( a_ :Optional[Any]) -> Union[str, Any]: __a : Dict = os.path.join(args.tf_model_dir , '''parameters.json''') __a : Optional[int] = json.loads(open(lowerCamelCase_).read()) if not params: raise ValueError( F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""") if not args.output.endswith('''.pt'''): __a : str = args.output + """.pt""" __a : int = OrderedDict() with tf.device('''/CPU:0'''): __a : Any = tf.train.load_checkpoint(args.tf_model_dir) __a : Any = reader.get_variable_to_shape_map() for key_name in shapes.keys(): __a : int = reader.get_tensor(lowerCamelCase_).astype(np.floataa) if key_name.endswith('''/adam_m''') or key_name.endswith('''/adam_v'''): continue if key_name.startswith('''pasts/'''): if key_name.startswith('''pasts/mlp'''): __a : Optional[Any] = int(key_name[9]) elif key_name.startswith('''pasts/out'''): __a : Dict = 8 __a : Optional[int] = """model.sqout.%d.weight""" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time __a : List[Any] = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix __a : Any = torch.tensor(lowerCamelCase_) elif key_name.startswith('''model/moe'''): __a : Optional[Any] = int(key_name[9:].split('''/''')[0]) if key_name.endswith('''/switch_gating/kernel'''): __a : Union[str, Any] = """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player __a : Tuple = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix __a : str = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/softmlp/kernel'''): __a : List[str] = """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player __a : int = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix __a : Optional[Any] = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/wo/kernel''') or key_name.endswith('''/wi/kernel'''): __a : int = key_name[-9:-7] for i in range(16): __a : Optional[Any] = """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer) __a : Dict = ( vnp[i].transpose([1, 0]).copy() ) # In Mesh-Tensorflow, it is one array, so it is divided __a : Optional[int] = torch.tensor(lowerCamelCase_) elif key_name.startswith('''model/mlp'''): __a : Optional[int] = int(key_name[9:].split('''/''')[0]) if key_name.endswith('''/p1/kernel'''): __a : List[Any] = """model.blocks.%d.feed_forward.mlp.wi.weight""" % player __a : Optional[int] = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix __a : Tuple = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/p1/bias'''): __a : Union[str, Any] = """model.blocks.%d.feed_forward.mlp.wi.bias""" % player __a : Tuple = vnp.copy() # same because it is one dimensional __a : Union[str, Any] = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/p2/kernel'''): __a : Any = """model.blocks.%d.feed_forward.mlp.wo.weight""" % player __a : Tuple = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix __a : Tuple = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/p2/bias'''): __a : Tuple = """model.blocks.%d.feed_forward.mlp.wo.bias""" % player __a : Optional[Any] = vnp.copy() # same because it is one dimensional __a : Any = torch.tensor(lowerCamelCase_) elif key_name.startswith('''model/ln'''): __a : Any = int(key_name[8:].split('''/''')[0]) if key_name.endswith('''/b'''): __a : int = """model.blocks.%d.feed_forward.norm.bias""" % player __a : Union[str, Any] = vnp.copy() # same because it is one dimensional __a : List[Any] = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/g'''): __a : List[str] = """model.blocks.%d.feed_forward.norm.weight""" % player __a : Any = vnp.copy() # same because it is one dimensional __a : int = torch.tensor(lowerCamelCase_) elif key_name.startswith('''model/att'''): __a : Optional[int] = int(key_name[9:].split('''/''')[0]) if key_name.endswith('''/qkv/kernel'''): __a : Optional[int] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum __a : Optional[int] = state[:, 0, :, :] __a : str = state[:, 1, :, :] __a : List[Any] = state[:, 2, :, :] __a : Optional[int] = ( state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]) .transpose([1, 0]) .copy() ) # Mesh-Tensorflow is a diagonal matrix __a : Any = ( state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]) .transpose([1, 0]) .copy() ) # Mesh-Tensorflow is a diagonal matrix __a : List[Any] = ( state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]) .transpose([1, 0]) .copy() ) # Mesh-Tensorflow is a diagonal matrix __a : Any = """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player __a : Any = torch.tensor(lowerCamelCase_) __a : Optional[Any] = """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player __a : List[Any] = torch.tensor(lowerCamelCase_) __a : Optional[Any] = """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player __a : List[str] = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/o/kernel'''): __a : Optional[int] = """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player __a : Tuple = ( vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]).transpose([1, 0]).copy() ) # Mesh-Tensorflow is a diagonal matrix __a : List[str] = torch.tensor(lowerCamelCase_) elif key_name.startswith('''model/an'''): __a : Optional[Any] = int(key_name[8:].split('''/''')[0]) if key_name.endswith('''/b'''): __a : Dict = """model.blocks.%d.self_attn.norm.bias""" % player __a : Any = vnp.copy() # same because it is one dimensional __a : Tuple = torch.tensor(lowerCamelCase_) elif key_name.endswith('''/g'''): __a : List[Any] = """model.blocks.%d.self_attn.norm.weight""" % player __a : int = vnp.copy() # same because it is one dimensional __a : str = torch.tensor(lowerCamelCase_) elif ( key_name.startswith('''model/wte''') or key_name.startswith('''model/wpe''') or key_name.startswith('''model/ete''') ): __a : Tuple = {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[ key_name[-3:] ] __a : int = """model.%s.weight""" % nlayer __a : List[Any] = vnp.copy() # same in embedded __a : str = torch.tensor(lowerCamelCase_) if key_name.startswith('''model/wte'''): __a : Dict = """lm_head.weight""" __a : List[Any] = vnp.copy() # same in embedded __a : Union[str, Any] = torch.tensor(lowerCamelCase_) elif key_name.startswith('''model/wob'''): __a : List[str] = """final_logits_bias""" __a : Any = vnp.copy() # same in embedded __a : Optional[int] = state.reshape((1, -1)) __a : List[Any] = torch.tensor(lowerCamelCase_) elif key_name == "model/dense/kernel": __a : Any = """model.last_project.weight""" __a : int = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix __a : int = torch.tensor(lowerCamelCase_) elif key_name == "model/dense_1/bias": __a : Any = """model.last_project.bias""" __a : List[str] = vnp.copy() # same because it is one dimensional __a : List[Any] = torch.tensor(lowerCamelCase_) torch.save(lowerCamelCase_ , args.output) if __name__ == "__main__": A = argparse.ArgumentParser( description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''') parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''') A = parser.parse_args() convert_tf_gptsan_to_pt(args)

'''simple docstring''' from __future__ import annotations import time from math import sqrt # 1 for manhattan, 0 for euclidean __UpperCAmelCase = 0 __UpperCAmelCase = [ [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], ] __UpperCAmelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right __UpperCAmelCase = tuple[int, int] class UpperCamelCase__ : """simple docstring""" def __init__( self : Optional[Any] , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : Node | None , ): '''simple docstring''' SCREAMING_SNAKE_CASE : List[str] = pos_x SCREAMING_SNAKE_CASE : Any = pos_y SCREAMING_SNAKE_CASE : Optional[int] = (pos_y, pos_x) SCREAMING_SNAKE_CASE : Tuple = goal_x SCREAMING_SNAKE_CASE : List[str] = goal_y SCREAMING_SNAKE_CASE : Optional[Any] = g_cost SCREAMING_SNAKE_CASE : Tuple = parent SCREAMING_SNAKE_CASE : int = self.calculate_heuristic() SCREAMING_SNAKE_CASE : Tuple = self.g_cost + self.h_cost def lowerCamelCase_ ( self : Optional[Any] ): '''simple docstring''' SCREAMING_SNAKE_CASE : Tuple = self.pos_x - self.goal_x SCREAMING_SNAKE_CASE : List[str] = self.pos_y - self.goal_y if HEURISTIC == 1: return abs(lowerCamelCase_ ) + abs(lowerCamelCase_ ) else: return sqrt(dy**2 + dx**2 ) def __lt__( self : Optional[Any] , lowerCamelCase_ : Node ): '''simple docstring''' return self.f_cost < other.f_cost class UpperCamelCase__ : """simple docstring""" def __init__( self : int , lowerCamelCase_ : TPosition , lowerCamelCase_ : TPosition ): '''simple docstring''' SCREAMING_SNAKE_CASE : Tuple = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , lowerCamelCase_ ) SCREAMING_SNAKE_CASE : str = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_99_99 , lowerCamelCase_ ) SCREAMING_SNAKE_CASE : Optional[Any] = [self.start] SCREAMING_SNAKE_CASE : list[Node] = [] SCREAMING_SNAKE_CASE : str = False def lowerCamelCase_ ( self : Any ): '''simple docstring''' while self.open_nodes: # Open Nodes are sorted using __lt__ self.open_nodes.sort() SCREAMING_SNAKE_CASE : Optional[Any] = self.open_nodes.pop(0 ) if current_node.pos == self.target.pos: return self.retrace_path(lowerCamelCase_ ) self.closed_nodes.append(lowerCamelCase_ ) SCREAMING_SNAKE_CASE : List[str] = self.get_successors(lowerCamelCase_ ) for child_node in successors: if child_node in self.closed_nodes: continue if child_node not in self.open_nodes: self.open_nodes.append(lowerCamelCase_ ) else: # retrieve the best current path SCREAMING_SNAKE_CASE : int = self.open_nodes.pop(self.open_nodes.index(lowerCamelCase_ ) ) if child_node.g_cost < better_node.g_cost: self.open_nodes.append(lowerCamelCase_ ) else: self.open_nodes.append(lowerCamelCase_ ) return [self.start.pos] def lowerCamelCase_ ( self : int , lowerCamelCase_ : Node ): '''simple docstring''' SCREAMING_SNAKE_CASE : Dict = [] for action in delta: SCREAMING_SNAKE_CASE : Dict = parent.pos_x + action[1] SCREAMING_SNAKE_CASE : List[str] = parent.pos_y + action[0] if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCamelCase_ ) - 1): continue if grid[pos_y][pos_x] != 0: continue successors.append( Node( lowerCamelCase_ , lowerCamelCase_ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , lowerCamelCase_ , ) ) return successors def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : Node | None ): '''simple docstring''' SCREAMING_SNAKE_CASE : int = node SCREAMING_SNAKE_CASE : List[str] = [] while current_node is not None: path.append((current_node.pos_y, current_node.pos_x) ) SCREAMING_SNAKE_CASE : Optional[Any] = current_node.parent path.reverse() return path class UpperCamelCase__ : """simple docstring""" def __init__( self : int , lowerCamelCase_ : TPosition , lowerCamelCase_ : TPosition ): '''simple docstring''' SCREAMING_SNAKE_CASE : str = AStar(lowerCamelCase_ , lowerCamelCase_ ) SCREAMING_SNAKE_CASE : Optional[Any] = AStar(lowerCamelCase_ , lowerCamelCase_ ) SCREAMING_SNAKE_CASE : Any = False def lowerCamelCase_ ( self : Tuple ): '''simple docstring''' while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes: self.fwd_astar.open_nodes.sort() self.bwd_astar.open_nodes.sort() SCREAMING_SNAKE_CASE : List[str] = self.fwd_astar.open_nodes.pop(0 ) SCREAMING_SNAKE_CASE : Optional[Any] = self.bwd_astar.open_nodes.pop(0 ) if current_bwd_node.pos == current_fwd_node.pos: return self.retrace_bidirectional_path( lowerCamelCase_ , lowerCamelCase_ ) self.fwd_astar.closed_nodes.append(lowerCamelCase_ ) self.bwd_astar.closed_nodes.append(lowerCamelCase_ ) SCREAMING_SNAKE_CASE : List[Any] = current_bwd_node SCREAMING_SNAKE_CASE : Any = current_fwd_node SCREAMING_SNAKE_CASE : Dict = { self.fwd_astar: self.fwd_astar.get_successors(lowerCamelCase_ ), self.bwd_astar: self.bwd_astar.get_successors(lowerCamelCase_ ), } for astar in [self.fwd_astar, self.bwd_astar]: for child_node in successors[astar]: if child_node in astar.closed_nodes: continue if child_node not in astar.open_nodes: astar.open_nodes.append(lowerCamelCase_ ) else: # retrieve the best current path SCREAMING_SNAKE_CASE : int = astar.open_nodes.pop( astar.open_nodes.index(lowerCamelCase_ ) ) if child_node.g_cost < better_node.g_cost: astar.open_nodes.append(lowerCamelCase_ ) else: astar.open_nodes.append(lowerCamelCase_ ) return [self.fwd_astar.start.pos] def lowerCamelCase_ ( self : str , lowerCamelCase_ : Node , lowerCamelCase_ : Node ): '''simple docstring''' SCREAMING_SNAKE_CASE : Optional[Any] = self.fwd_astar.retrace_path(lowerCamelCase_ ) SCREAMING_SNAKE_CASE : Any = self.bwd_astar.retrace_path(lowerCamelCase_ ) bwd_path.pop() bwd_path.reverse() SCREAMING_SNAKE_CASE : str = fwd_path + bwd_path return path if __name__ == "__main__": # all coordinates are given in format [y,x] __UpperCAmelCase = (0, 0) __UpperCAmelCase = (len(grid) - 1, len(grid[0]) - 1) for elem in grid: print(elem) __UpperCAmelCase = time.time() __UpperCAmelCase = AStar(init, goal) __UpperCAmelCase = a_star.search() __UpperCAmelCase = time.time() - start_time print(f'''AStar execution time = {end_time:f} seconds''') __UpperCAmelCase = time.time() __UpperCAmelCase = BidirectionalAStar(init, goal) __UpperCAmelCase = time.time() - bd_start_time print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')

'''simple docstring''' import argparse import os import re # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_dummies.py a__ : Optional[Any] = 'src/diffusers' # Matches is_xxx_available() a__ : List[Any] = re.compile(R'is\_([a-z_]*)_available\(\)') # Matches from xxx import bla a__ : int = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n') a__ : Any = '\n{0} = None\n' a__ : List[str] = '\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n' a__ : Dict = '\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n' def _lowercase ( __A ): '''simple docstring''' __UpperCamelCase = _re_backend.findall(__A ) if len(__A ) == 0: return None return "_and_".join(__A ) def _lowercase ( ): '''simple docstring''' with open(os.path.join(__A ,"""__init__.py""" ) ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f: __UpperCamelCase = f.readlines() # Get to the point we do the actual imports for type checking __UpperCamelCase = 0 __UpperCamelCase = {} # Go through the end of the file while line_index < len(__A ): # If the line contains is_backend_available, we grab all objects associated with the `else` block __UpperCamelCase = find_backend(lines[line_index] ) if backend is not None: while not lines[line_index].startswith("""else:""" ): line_index += 1 line_index += 1 __UpperCamelCase = [] # Until we unindent, add backend objects to the list while line_index < len(__A ) and len(lines[line_index] ) > 1: __UpperCamelCase = lines[line_index] __UpperCamelCase = _re_single_line_import.search(__A ) if single_line_import_search is not None: objects.extend(single_line_import_search.groups()[0].split(""", """ ) ) elif line.startswith(""" """ * 8 ): objects.append(line[8:-2] ) line_index += 1 if len(__A ) > 0: __UpperCamelCase = objects else: line_index += 1 return backend_specific_objects def _lowercase ( __A ,__A ): '''simple docstring''' if name.isupper(): return DUMMY_CONSTANT.format(__A ) elif name.islower(): return DUMMY_FUNCTION.format(__A ,__A ) else: return DUMMY_CLASS.format(__A ,__A ) def _lowercase ( __A=None ): '''simple docstring''' if backend_specific_objects is None: __UpperCamelCase = read_init() # For special correspondence backend to module name as used in the function requires_modulename __UpperCamelCase = {} for backend, objects in backend_specific_objects.items(): __UpperCamelCase = """[""" + """, """.join(f"\"{b}\"" for b in backend.split("""_and_""" ) ) + """]""" __UpperCamelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.\n""" dummy_file += "from ..utils import DummyObject, requires_backends\n\n" dummy_file += "\n".join([create_dummy_object(__A ,__A ) for o in objects] ) __UpperCamelCase = dummy_file return dummy_files def _lowercase ( __A=False ): '''simple docstring''' __UpperCamelCase = create_dummy_files() # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py __UpperCamelCase = {"""torch""": """pt"""} # Locate actual dummy modules and read their content. __UpperCamelCase = os.path.join(__A ,"""utils""" ) __UpperCamelCase = { backend: os.path.join(__A ,f"dummy_{short_names.get(__A ,__A )}_objects.py" ) for backend in dummy_files.keys() } __UpperCamelCase = {} for backend, file_path in dummy_file_paths.items(): if os.path.isfile(__A ): with open(__A ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f: __UpperCamelCase = f.read() else: __UpperCamelCase = """""" for backend in dummy_files.keys(): if dummy_files[backend] != actual_dummies[backend]: if overwrite: print( f"Updating diffusers.utils.dummy_{short_names.get(__A ,__A )}_objects.py as the main " """__init__ has new objects.""" ) with open(dummy_file_paths[backend] ,"""w""" ,encoding="""utf-8""" ,newline="""\n""" ) as f: f.write(dummy_files[backend] ) else: raise ValueError( """The main __init__ has objects that are not present in """ f"diffusers.utils.dummy_{short_names.get(__A ,__A )}_objects.py. Run `make fix-copies` " """to fix this.""" ) if __name__ == "__main__": a__ : int = argparse.ArgumentParser() parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.') a__ : Union[str, Any] = parser.parse_args() check_dummies(args.fix_and_overwrite)

'''simple docstring''' # Author: OMKAR PATHAK, Nwachukwu Chidiebere # Use a Python dictionary to construct the graph. from __future__ import annotations from pprint import pformat from typing import Generic, TypeVar a__ : int = TypeVar('T') class UpperCAmelCase__ ( Generic[T]): def __init__( self , lowercase = True ) -> None: __UpperCamelCase = {} # dictionary of lists __UpperCamelCase = directed def __lowerCamelCase ( self , lowercase , lowercase ) -> GraphAdjacencyList[T]: if not self.directed: # For undirected graphs # if both source vertex and destination vertex are both present in the # adjacency list, add destination vertex to source vertex list of adjacent # vertices and add source vertex to destination vertex list of adjacent # vertices. if source_vertex in self.adj_list and destination_vertex in self.adj_list: self.adj_list[source_vertex].append(lowercase ) self.adj_list[destination_vertex].append(lowercase ) # if only source vertex is present in adjacency list, add destination vertex # to source vertex list of adjacent vertices, then create a new vertex with # destination vertex as key and assign a list containing the source vertex # as it's first adjacent vertex. elif source_vertex in self.adj_list: self.adj_list[source_vertex].append(lowercase ) __UpperCamelCase = [source_vertex] # if only destination vertex is present in adjacency list, add source vertex # to destination vertex list of adjacent vertices, then create a new vertex # with source vertex as key and assign a list containing the source vertex # as it's first adjacent vertex. elif destination_vertex in self.adj_list: self.adj_list[destination_vertex].append(lowercase ) __UpperCamelCase = [destination_vertex] # if both source vertex and destination vertex are not present in adjacency # list, create a new vertex with source vertex as key and assign a list # containing the destination vertex as it's first adjacent vertex also # create a new vertex with destination vertex as key and assign a list # containing the source vertex as it's first adjacent vertex. else: __UpperCamelCase = [destination_vertex] __UpperCamelCase = [source_vertex] else: # For directed graphs # if both source vertex and destination vertex are present in adjacency # list, add destination vertex to source vertex list of adjacent vertices. if source_vertex in self.adj_list and destination_vertex in self.adj_list: self.adj_list[source_vertex].append(lowercase ) # if only source vertex is present in adjacency list, add destination # vertex to source vertex list of adjacent vertices and create a new vertex # with destination vertex as key, which has no adjacent vertex elif source_vertex in self.adj_list: self.adj_list[source_vertex].append(lowercase ) __UpperCamelCase = [] # if only destination vertex is present in adjacency list, create a new # vertex with source vertex as key and assign a list containing destination # vertex as first adjacent vertex elif destination_vertex in self.adj_list: __UpperCamelCase = [destination_vertex] # if both source vertex and destination vertex are not present in adjacency # list, create a new vertex with source vertex as key and a list containing # destination vertex as it's first adjacent vertex. Then create a new vertex # with destination vertex as key, which has no adjacent vertex else: __UpperCamelCase = [destination_vertex] __UpperCamelCase = [] return self def __repr__( self ) -> str: return pformat(self.adj_list )

import warnings from ...utils import logging from .image_processing_poolformer import PoolFormerImageProcessor UpperCAmelCase__ = logging.get_logger(__name__) class lowercase_ ( lowercase ): '''simple docstring''' def __init__( self : int , *__UpperCAmelCase : Any , **__UpperCAmelCase : Optional[int] ) ->None: """simple docstring""" warnings.warn( '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.''' ''' Please use PoolFormerImageProcessor instead.''' , __UpperCAmelCase , ) super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )

def _a ( a :int ) -> list: # bit count represents no. of bits in the gray code if bit_count < 0: raise ValueError('''The given input must be positive''' ) # get the generated string sequence a = gray_code_sequence_string(a ) # # convert them to integers for i in range(len(a ) ): a = int(sequence[i] , 2 ) return sequence def _a ( a :int ) -> list: # The approach is a recursive one # Base case achieved when either n = 0 or n=1 if bit_count == 0: return ["0"] if bit_count == 1: return ["0", "1"] a = 1 << bit_count # defines the length of the sequence # 1<< n is equivalent to 2^n # recursive answer will generate answer for n-1 bits a = gray_code_sequence_string(bit_count - 1 ) a = [] # append 0 to first half of the smaller sequence generated for i in range(seq_len // 2 ): a = '''0''' + smaller_sequence[i] sequence.append(a ) # append 1 to second half ... start from the end of the list for i in reversed(range(seq_len // 2 ) ): a = '''1''' + smaller_sequence[i] sequence.append(a ) return sequence if __name__ == "__main__": import doctest doctest.testmod()

from math import ceil, sqrt def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ = 1_0_0_0_0_0_0 ) -> int: __lowerCamelCase : Tuple = 0 for outer_width in range(3 , (limit // 4) + 2 ): if outer_width**2 > limit: __lowerCamelCase : List[str] = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 ) else: __lowerCamelCase : str = 1 if (outer_width - hole_width_lower_bound) % 2: hole_width_lower_bound += 1 answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1 return answer if __name__ == "__main__": print(F"""{solution() = }""")

import os import unittest from huggingface_hub.utils import are_progress_bars_disabled import transformers.models.bart.tokenization_bart from transformers import logging from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context from transformers.utils.logging import disable_progress_bar, enable_progress_bar class A_ ( unittest.TestCase ): def lowerCAmelCase ( self : Optional[int]): __lowerCamelCase : List[Any] = logging.get_logger() # the current default level is logging.WARNING __lowerCamelCase : Dict = logging.get_verbosity() logging.set_verbosity_error() self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity()) logging.set_verbosity_warning() self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity()) logging.set_verbosity_info() self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity()) logging.set_verbosity_debug() self.assertEqual(logger.getEffectiveLevel() ,logging.get_verbosity()) # restore to the original level logging.set_verbosity(SCREAMING_SNAKE_CASE__) def lowerCAmelCase ( self : Optional[Any]): __lowerCamelCase : Optional[int] = logging.get_verbosity() __lowerCamelCase : str = logging.get_logger('transformers.models.bart.tokenization_bart') __lowerCamelCase : Tuple = 'Testing 1, 2, 3' # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`) if level_origin <= logging.WARNING: with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl: logger.warning(SCREAMING_SNAKE_CASE__) self.assertEqual(cl.out ,msg + '\n') # this is setting the level for all of `transformers.*` loggers logging.set_verbosity_error() # should not be able to log warnings with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl: logger.warning(SCREAMING_SNAKE_CASE__) self.assertEqual(cl.out ,'') # should be able to log warnings again logging.set_verbosity_warning() with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl: logger.warning(SCREAMING_SNAKE_CASE__) self.assertEqual(cl.out ,msg + '\n') # restore to the original level logging.set_verbosity(SCREAMING_SNAKE_CASE__) @mockenv(TRANSFORMERS_VERBOSITY='error') def lowerCAmelCase ( self : Tuple): # reset for the env var to take effect, next time some logger call is made transformers.utils.logging._reset_library_root_logger() # this action activates the env var __lowerCamelCase : int = logging.get_logger('transformers.models.bart.tokenization_bart') __lowerCamelCase : int = os.getenv('TRANSFORMERS_VERBOSITY' ,SCREAMING_SNAKE_CASE__) __lowerCamelCase : Optional[Any] = logging.log_levels[env_level_str] __lowerCamelCase : Tuple = logging.get_verbosity() self.assertEqual( SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,F"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}" ,) # restore to the original level __lowerCamelCase : List[str] = '' transformers.utils.logging._reset_library_root_logger() @mockenv(TRANSFORMERS_VERBOSITY='super-error') def lowerCAmelCase ( self : List[Any]): # reset for the env var to take effect, next time some logger call is made transformers.utils.logging._reset_library_root_logger() __lowerCamelCase : List[str] = logging.logging.getLogger() with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl: # this action activates the env var logging.get_logger('transformers.models.bart.tokenization_bart') self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error' ,cl.out) # no need to restore as nothing was changed def lowerCAmelCase ( self : Any): # testing `logger.warning_advice()` transformers.utils.logging._reset_library_root_logger() __lowerCamelCase : Tuple = logging.get_logger('transformers.models.bart.tokenization_bart') __lowerCamelCase : Optional[int] = 'Testing 1, 2, 3' with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'): # nothing should be logged as env var disables this method with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl: logger.warning_advice(SCREAMING_SNAKE_CASE__) self.assertEqual(cl.out ,'') with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''): # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl: logger.warning_advice(SCREAMING_SNAKE_CASE__) self.assertEqual(cl.out ,msg + '\n') def SCREAMING_SNAKE_CASE__ ( ) -> Any: disable_progress_bar() assert are_progress_bars_disabled() enable_progress_bar() assert not are_progress_bars_disabled()

"""simple docstring""" import importlib import os import fsspec import pytest from fsspec import register_implementation from fsspec.registry import _registry as _fsspec_registry from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem from .utils import require_lza, require_zstandard def _lowerCAmelCase ( lowercase_ ): assert "mock" in _fsspec_registry assert "bz2" in _fsspec_registry def _lowerCAmelCase ( ): assert "mock" not in _fsspec_registry assert "bz2" in _fsspec_registry def _lowerCAmelCase ( ): UpperCAmelCase = 'mock-s3-bucket' UpperCAmelCase = F"""s3://{mock_bucket}""" UpperCAmelCase = extract_path_from_uri(lowercase_ ) assert dataset_path.startswith('s3://' ) is False UpperCAmelCase = './local/path' UpperCAmelCase = extract_path_from_uri(lowercase_ ) assert dataset_path == new_dataset_path def _lowerCAmelCase ( lowercase_ ): UpperCAmelCase = is_remote_filesystem(lowercase_ ) assert is_remote is True UpperCAmelCase = fsspec.filesystem('file' ) UpperCAmelCase = is_remote_filesystem(lowercase_ ) assert is_remote is False @pytest.mark.parametrize('compression_fs_class' , lowercase_ ) def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): UpperCAmelCase = {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file} UpperCAmelCase = input_paths[compression_fs_class.protocol] if input_path is None: UpperCAmelCase = F"""for '{compression_fs_class.protocol}' compression protocol, """ if compression_fs_class.protocol == "lz4": reason += require_lza.kwargs["reason"] elif compression_fs_class.protocol == "zstd": reason += require_zstandard.kwargs["reason"] pytest.skip(lowercase_ ) UpperCAmelCase = fsspec.filesystem(compression_fs_class.protocol , fo=lowercase_ ) assert isinstance(lowercase_ , lowercase_ ) UpperCAmelCase = os.path.basename(lowercase_ ) UpperCAmelCase = expected_filename[: expected_filename.rindex('.' )] assert fs.glob('*' ) == [expected_filename] with fs.open(lowercase_ , 'r' , encoding='utf-8' ) as f, open(lowercase_ , encoding='utf-8' ) as expected_file: assert f.read() == expected_file.read() @pytest.mark.parametrize('protocol' , ['zip', 'gzip'] ) def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ): UpperCAmelCase = {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path} UpperCAmelCase = compressed_file_paths[protocol] UpperCAmelCase = 'dataset.jsonl' UpperCAmelCase = F"""{protocol}://{member_file_path}::{compressed_file_path}""" UpperCAmelCase , *UpperCAmelCase = fsspec.get_fs_token_paths(lowercase_ ) assert fs.isfile(lowercase_ ) assert not fs.isfile('non_existing_' + member_file_path ) @pytest.mark.integration def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ): UpperCAmelCase = hf_api.dataset_info(lowercase_ , token=lowercase_ ) UpperCAmelCase = HfFileSystem(repo_info=lowercase_ , token=lowercase_ ) assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"] assert hffs.isdir('data' ) assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' ) with open(lowercase_ ) as f: assert hffs.open('data/text_data.txt' , 'r' ).read() == f.read() def _lowerCAmelCase ( ): UpperCAmelCase = 'bz2' # Import module import datasets.filesystems # Overwrite protocol and reload register_implementation(lowercase_ , lowercase_ , clobber=lowercase_ ) with pytest.warns(lowercase_ ) as warning_info: importlib.reload(datasets.filesystems ) assert len(lowercase_ ) == 1 assert ( str(warning_info[0].message ) == F"""A filesystem protocol was already set for {protocol} and will be overwritten.""" )

"""simple docstring""" import os import time import numpy as np import onnxruntime as ort snake_case_ = """1""" snake_case_ = """0""" snake_case_ = """1""" snake_case_ = ort.SessionOptions() snake_case_ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL print("""Create inference session...""") snake_case_ = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""] snake_case_ = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider) snake_case_ = ort.RunOptions() snake_case_ = 128 snake_case_ = 1 snake_case_ = np.ones((batch, sequence), dtype=np.intaa) snake_case_ = np.ones((batch, sequence), dtype=np.intaa) snake_case_ = np.ones((batch, sequence), dtype=np.intaa) print("""Warm up phase...""") sess.run( None, { sess.get_inputs()[0].name: input_ids, sess.get_inputs()[1].name: attention_mask, sess.get_inputs()[2].name: token_type_ids, }, run_options=run_opt, ) print("""Start inference...""") snake_case_ = time.time() snake_case_ = 2000 snake_case_ = {} for iter in range(max_iters): snake_case_ = sess.run( None, { sess.get_inputs()[0].name: input_ids, sess.get_inputs()[1].name: attention_mask, sess.get_inputs()[2].name: token_type_ids, }, run_options=run_opt, ) print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1000 / max_iters))

from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import ( BaseOutput, OptionalDependencyNotAvailable, is_flax_available, is_k_diffusion_available, is_k_diffusion_version, is_onnx_available, is_torch_available, is_transformers_available, is_transformers_version, ) @dataclass class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ): '''simple docstring''' _lowerCamelCase = 42 _lowerCamelCase = 42 try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipeline_cycle_diffusion import CycleDiffusionPipeline from .pipeline_stable_diffusion import StableDiffusionPipeline from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline from .pipeline_stable_unclip import StableUnCLIPPipeline from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline from .safety_checker import StableDiffusionSafetyChecker from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline else: from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline try: if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import ( StableDiffusionDepthaImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionPixaPixZeroPipeline, ) else: from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline try: if not ( is_torch_available() and is_transformers_available() and is_k_diffusion_available() and is_k_diffusion_version(">=", "0.0.12") ): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline try: if not (is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_onnx_objects import * # noqa F403 else: from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline if is_transformers_available() and is_flax_available(): import flax @flax.struct.dataclass class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ): '''simple docstring''' _lowerCamelCase = 42 _lowerCamelCase = 42 from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline from .safety_checker_flax import FlaxStableDiffusionSafetyChecker

"""simple docstring""" def _A ( _a : int ): """simple docstring""" A = abs(_a ) A = 0 while n > 0: res += n % 1_0 n //= 1_0 return res def _A ( _a : int ): """simple docstring""" A = abs(_a ) return n if n < 1_0 else n % 1_0 + sum_of_digits(n // 1_0 ) def _A ( _a : int ): """simple docstring""" return sum(int(_a ) for c in str(abs(_a ) ) ) def _A ( ): """simple docstring""" from collections.abc import Callable from timeit import timeit def benchmark_a_function(_a : Callable , _a : int ) -> None: A = f'{func.__name__}({value})' A = timeit(f'__main__.{call}' , setup="""import __main__""" ) print(f'{call:56} = {func(_a )} -- {timing:.4f} seconds' ) for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6): for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact): benchmark_a_function(_a , _a ) print() if __name__ == "__main__": import doctest doctest.testmod() benchmark()

import random class __A: @staticmethod def SCREAMING_SNAKE_CASE_ ( _snake_case ) -> tuple[list[int], list[int]]: '''simple docstring''' __a = [ord(_snake_case ) for i in text] __a = [] __a = [] for i in plain: __a = random.randint(1 , 300 ) __a = (i + k) * k cipher.append(_snake_case ) key.append(_snake_case ) return cipher, key @staticmethod def SCREAMING_SNAKE_CASE_ ( _snake_case , _snake_case ) -> str: '''simple docstring''' __a = [] for i in range(len(_snake_case ) ): __a = int((cipher[i] - (key[i]) ** 2) / key[i] ) plain.append(chr(_snake_case ) ) return "".join(_snake_case ) if __name__ == "__main__": A , A : Any = Onepad().encrypt('Hello') print(c, k) print(Onepad().decrypt(c, k))

'''simple docstring''' import unittest from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers @require_sentencepiece @slow # see https://github.com/huggingface/transformers/issues/11457 class __magic_name__ ( _UpperCamelCase , unittest.TestCase ): lowerCAmelCase : Optional[int] = BarthezTokenizer lowerCAmelCase : int = BarthezTokenizerFast lowerCAmelCase : Dict = True lowerCAmelCase : str = True def __lowercase ( self : List[Any] ): super().setUp() _a : List[Any] = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' ) tokenizer.save_pretrained(self.tmpdirname ) tokenizer.save_pretrained(self.tmpdirname ,legacy_format=_UpperCAmelCase ) _a : Union[str, Any] = tokenizer def __lowercase ( self : Tuple ): _a : Optional[Any] = '<pad>' _a : List[Any] = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase ) ,_UpperCAmelCase ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase ) ,_UpperCAmelCase ) def __lowercase ( self : str ): _a : Any = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] ,'<s>' ) self.assertEqual(vocab_keys[1] ,'<pad>' ) self.assertEqual(vocab_keys[-1] ,'<mask>' ) self.assertEqual(len(_UpperCAmelCase ) ,101122 ) def __lowercase ( self : Dict ): self.assertEqual(self.get_tokenizer().vocab_size ,101122 ) @require_torch def __lowercase ( self : Dict ): _a : Any = ['A long paragraph for summarization.', 'Another paragraph for summarization.'] _a : Dict = [0, 57, 3018, 70307, 91, 2] _a : Dict = self.tokenizer( _UpperCAmelCase ,max_length=len(_UpperCAmelCase ) ,padding=_UpperCAmelCase ,truncation=_UpperCAmelCase ,return_tensors='pt' ) self.assertIsInstance(_UpperCAmelCase ,_UpperCAmelCase ) self.assertEqual((2, 6) ,batch.input_ids.shape ) self.assertEqual((2, 6) ,batch.attention_mask.shape ) _a : Tuple = batch.input_ids.tolist()[0] self.assertListEqual(_UpperCAmelCase ,_UpperCAmelCase ) def __lowercase ( self : Optional[Any] ): if not self.test_rust_tokenizer: return _a : str = self.get_tokenizer() _a : List[str] = self.get_rust_tokenizer() _a : Dict = 'I was born in 92000, and this is falsé.' _a : List[Any] = tokenizer.tokenize(_UpperCAmelCase ) _a : Tuple = rust_tokenizer.tokenize(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase ,_UpperCAmelCase ) _a : Optional[Any] = tokenizer.encode(_UpperCAmelCase ,add_special_tokens=_UpperCAmelCase ) _a : Optional[int] = rust_tokenizer.encode(_UpperCAmelCase ,add_special_tokens=_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase ,_UpperCAmelCase ) _a : Union[str, Any] = self.get_rust_tokenizer() _a : Any = tokenizer.encode(_UpperCAmelCase ) _a : Optional[int] = rust_tokenizer.encode(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase ,_UpperCAmelCase ) @slow def __lowercase ( self : Optional[int] ): # fmt: off _a : Optional[int] = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 # fmt: on # moussaKam/mbarthez is a french model. So we also use french texts. _a : Optional[Any] = [ 'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, ' 'utilisé principalement dans le domaine du traitement automatique des langues (TAL).', 'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus ' 'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches ' 'telles que la traduction et la synthèse de texte.', ] self.tokenizer_integration_test_util( expected_encoding=_UpperCAmelCase ,model_name='moussaKam/mbarthez' ,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' ,sequences=_UpperCAmelCase ,)

'''simple docstring''' class lowerCAmelCase_: '''simple docstring''' def __init__( self ,__UpperCAmelCase ) -> None: lowerCAmelCase__ : Optional[int] = size lowerCAmelCase__ : Any = [0] * size lowerCAmelCase__ : Any = [0] * size @staticmethod def UpperCAmelCase_ ( __UpperCAmelCase ) -> int: return index | (index + 1) @staticmethod def UpperCAmelCase_ ( __UpperCAmelCase ) -> int: return (index & (index + 1)) - 1 def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> None: lowerCAmelCase__ : List[Any] = value while index < self.size: lowerCAmelCase__ : Dict = self.get_prev(__UpperCAmelCase ) + 1 if current_left_border == index: lowerCAmelCase__ : Optional[Any] = value else: lowerCAmelCase__ : Optional[int] = max(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) lowerCAmelCase__ : Optional[Any] = self.get_next(__UpperCAmelCase ) def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> int: right -= 1 # Because of right is exclusive lowerCAmelCase__ : Tuple = 0 while left <= right: lowerCAmelCase__ : Tuple = self.get_prev(__UpperCAmelCase ) if left <= current_left: lowerCAmelCase__ : Optional[Any] = max(__UpperCAmelCase ,self.tree[right] ) lowerCAmelCase__ : Optional[int] = current_left else: lowerCAmelCase__ : Optional[Any] = max(__UpperCAmelCase ,self.arr[right] ) right -= 1 return result if __name__ == "__main__": import doctest doctest.testmod()

'''simple docstring''' def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 1000 ): """simple docstring""" lowerCAmelCase__ : Union[str, Any] = -1 lowerCAmelCase__ : Optional[Any] = 0 for a in range(1 , n // 3 ): # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c lowerCAmelCase__ : Optional[Any] = (n * n - 2 * a * n) // (2 * n - 2 * a) lowerCAmelCase__ : Tuple = n - a - b if c * c == (a * a + b * b): lowerCAmelCase__ : int = a * b * c if candidate >= product: lowerCAmelCase__ : Any = candidate return product if __name__ == "__main__": print(F"""{solution() = }""")

'''simple docstring''' from __future__ import annotations def UpperCAmelCase_ ( __lowercase : list[float] , __lowercase : list[float] ) -> float: '''simple docstring''' _UpperCAmelCase = sorted(numsa + numsa ) _UpperCAmelCase , _UpperCAmelCase = divmod(len(__lowercase ) , 2 ) if mod == 1: return all_numbers[div] else: return (all_numbers[div] + all_numbers[div - 1]) / 2 if __name__ == "__main__": import doctest doctest.testmod() __SCREAMING_SNAKE_CASE :Tuple = [float(x) for x in input('''Enter the elements of first array: ''').split()] __SCREAMING_SNAKE_CASE :Any = [float(x) for x in input('''Enter the elements of second array: ''').split()] print(F"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")

import torch from diffusers import DDPMParallelScheduler from .test_schedulers import SchedulerCommonTest class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ): '''simple docstring''' lowercase_ = (DDPMParallelScheduler,) def SCREAMING_SNAKE_CASE_ (self : Any , **UpperCAmelCase_ : Any) ->Any: '''simple docstring''' lowerCamelCase__: Any ={ "num_train_timesteps": 1_000, "beta_start": 0.0001, "beta_end": 0.02, "beta_schedule": "linear", "variance_type": "fixed_small", "clip_sample": True, } config.update(**UpperCAmelCase_) return config def SCREAMING_SNAKE_CASE_ (self : int) ->Dict: '''simple docstring''' for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]: '''simple docstring''' for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]): self.check_over_configs(beta_start=UpperCAmelCase_ , beta_end=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Any: '''simple docstring''' for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : int) ->Optional[int]: '''simple docstring''' for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Optional[Any]: '''simple docstring''' for clip_sample in [True, False]: self.check_over_configs(clip_sample=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Any) ->Tuple: '''simple docstring''' self.check_over_configs(thresholding=UpperCAmelCase_) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=UpperCAmelCase_ , prediction_type=UpperCAmelCase_ , sample_max_value=UpperCAmelCase_ , ) def SCREAMING_SNAKE_CASE_ (self : Any) ->Optional[int]: '''simple docstring''' for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : int) ->int: '''simple docstring''' for t in [0, 500, 999]: self.check_over_forward(time_step=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->str: '''simple docstring''' lowerCamelCase__: Dict =self.scheduler_classes[0] lowerCamelCase__: Tuple =self.get_scheduler_config() lowerCamelCase__: Any =scheduler_class(**UpperCAmelCase_) assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5 def SCREAMING_SNAKE_CASE_ (self : Any) ->str: '''simple docstring''' lowerCamelCase__: int =self.scheduler_classes[0] lowerCamelCase__: Tuple =self.get_scheduler_config() lowerCamelCase__: Tuple =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: str =len(UpperCAmelCase_) lowerCamelCase__: Optional[int] =self.dummy_model() lowerCamelCase__: int =self.dummy_sample_deter lowerCamelCase__: Union[str, Any] =self.dummy_sample_deter + 0.1 lowerCamelCase__: Optional[Any] =self.dummy_sample_deter - 0.1 lowerCamelCase__: Optional[Any] =samplea.shape[0] lowerCamelCase__: List[Any] =torch.stack([samplea, samplea, samplea] , dim=0) lowerCamelCase__: Union[str, Any] =torch.arange(UpperCAmelCase_)[0:3, None].repeat(1 , UpperCAmelCase_) lowerCamelCase__: Optional[int] =model(samples.flatten(0 , 1) , timesteps.flatten(0 , 1)) lowerCamelCase__: Tuple =scheduler.batch_step_no_noise(UpperCAmelCase_ , timesteps.flatten(0 , 1) , samples.flatten(0 , 1)) lowerCamelCase__: List[str] =torch.sum(torch.abs(UpperCAmelCase_)) lowerCamelCase__: Any =torch.mean(torch.abs(UpperCAmelCase_)) assert abs(result_sum.item() - 1153.1833) < 1E-2 assert abs(result_mean.item() - 0.5005) < 1E-3 def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Union[str, Any]: '''simple docstring''' lowerCamelCase__: Any =self.scheduler_classes[0] lowerCamelCase__: Optional[Any] =self.get_scheduler_config() lowerCamelCase__: Optional[int] =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: Union[str, Any] =len(UpperCAmelCase_) lowerCamelCase__: Union[str, Any] =self.dummy_model() lowerCamelCase__: List[Any] =self.dummy_sample_deter lowerCamelCase__: int =torch.manual_seed(0) for t in reversed(range(UpperCAmelCase_)): # 1. predict noise residual lowerCamelCase__: Tuple =model(UpperCAmelCase_ , UpperCAmelCase_) # 2. predict previous mean of sample x_t-1 lowerCamelCase__: Optional[Any] =scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_).prev_sample lowerCamelCase__: Any =pred_prev_sample lowerCamelCase__: Any =torch.sum(torch.abs(UpperCAmelCase_)) lowerCamelCase__: List[str] =torch.mean(torch.abs(UpperCAmelCase_)) assert abs(result_sum.item() - 258.9606) < 1E-2 assert abs(result_mean.item() - 0.3372) < 1E-3 def SCREAMING_SNAKE_CASE_ (self : int) ->Any: '''simple docstring''' lowerCamelCase__: Tuple =self.scheduler_classes[0] lowerCamelCase__: Any =self.get_scheduler_config(prediction_type="v_prediction") lowerCamelCase__: Any =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: str =len(UpperCAmelCase_) lowerCamelCase__: str =self.dummy_model() lowerCamelCase__: str =self.dummy_sample_deter lowerCamelCase__: Dict =torch.manual_seed(0) for t in reversed(range(UpperCAmelCase_)): # 1. predict noise residual lowerCamelCase__: Union[str, Any] =model(UpperCAmelCase_ , UpperCAmelCase_) # 2. predict previous mean of sample x_t-1 lowerCamelCase__: Dict =scheduler.step(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , generator=UpperCAmelCase_).prev_sample lowerCamelCase__: List[str] =pred_prev_sample lowerCamelCase__: List[Any] =torch.sum(torch.abs(UpperCAmelCase_)) lowerCamelCase__: Tuple =torch.mean(torch.abs(UpperCAmelCase_)) assert abs(result_sum.item() - 202.0296) < 1E-2 assert abs(result_mean.item() - 0.2631) < 1E-3 def SCREAMING_SNAKE_CASE_ (self : Tuple) ->Optional[int]: '''simple docstring''' lowerCamelCase__: str =self.scheduler_classes[0] lowerCamelCase__: Union[str, Any] =self.get_scheduler_config() lowerCamelCase__: Any =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: List[Any] =[100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=UpperCAmelCase_) lowerCamelCase__: Union[str, Any] =scheduler.timesteps for i, timestep in enumerate(UpperCAmelCase_): if i == len(UpperCAmelCase_) - 1: lowerCamelCase__: Dict =-1 else: lowerCamelCase__: Union[str, Any] =timesteps[i + 1] lowerCamelCase__: Tuple =scheduler.previous_timestep(UpperCAmelCase_) lowerCamelCase__: str =prev_t.item() self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Union[str, Any]: '''simple docstring''' lowerCamelCase__: Tuple =self.scheduler_classes[0] lowerCamelCase__: List[Any] =self.get_scheduler_config() lowerCamelCase__: Dict =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: Optional[Any] =[100, 87, 50, 51, 0] with self.assertRaises(UpperCAmelCase_ , msg="`custom_timesteps` must be in descending order."): scheduler.set_timesteps(timesteps=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : List[Any]) ->List[Any]: '''simple docstring''' lowerCamelCase__: Dict =self.scheduler_classes[0] lowerCamelCase__: Any =self.get_scheduler_config() lowerCamelCase__: int =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: Optional[int] =[100, 87, 50, 1, 0] lowerCamelCase__: int =len(UpperCAmelCase_) with self.assertRaises(UpperCAmelCase_ , msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."): scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_ , timesteps=UpperCAmelCase_) def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Any: '''simple docstring''' lowerCamelCase__: Tuple =self.scheduler_classes[0] lowerCamelCase__: Optional[Any] =self.get_scheduler_config() lowerCamelCase__: Optional[Any] =scheduler_class(**UpperCAmelCase_) lowerCamelCase__: Dict =[scheduler.config.num_train_timesteps] with self.assertRaises( UpperCAmelCase_ , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ): scheduler.set_timesteps(timesteps=UpperCAmelCase_)

import pytest from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs @pytest.mark.parametrize( '''kwargs, expected''' , [ ({'''num_shards''': 0, '''max_num_jobs''': 1}, []), ({'''num_shards''': 10, '''max_num_jobs''': 1}, [range(10 )]), ({'''num_shards''': 10, '''max_num_jobs''': 10}, [range(UpperCAmelCase_ , i + 1 ) for i in range(10 )]), ({'''num_shards''': 1, '''max_num_jobs''': 10}, [range(1 )]), ({'''num_shards''': 10, '''max_num_jobs''': 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]), ({'''num_shards''': 3, '''max_num_jobs''': 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]), ] , ) def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ): """simple docstring""" a :List[Any] = _distribute_shards(**UpperCAmelCase_ ) assert out == expected @pytest.mark.parametrize( '''gen_kwargs, max_num_jobs, expected''' , [ ({'''foo''': 0}, 10, [{'''foo''': 0}]), ({'''shards''': [0, 1, 2, 3]}, 1, [{'''shards''': [0, 1, 2, 3]}]), ({'''shards''': [0, 1, 2, 3]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}, {'''shards''': [2]}, {'''shards''': [3]}]), ({'''shards''': [0, 1]}, 4, [{'''shards''': [0]}, {'''shards''': [1]}]), ({'''shards''': [0, 1, 2, 3]}, 2, [{'''shards''': [0, 1]}, {'''shards''': [2, 3]}]), ] , ) def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[Any] ): """simple docstring""" a :Optional[Any] = _split_gen_kwargs(UpperCAmelCase_ , UpperCAmelCase_ ) assert out == expected @pytest.mark.parametrize( '''gen_kwargs, expected''' , [ ({'''foo''': 0}, 1), ({'''shards''': [0]}, 1), ({'''shards''': [0, 1, 2, 3]}, 4), ({'''shards''': [0, 1, 2, 3], '''foo''': 0}, 4), ({'''shards''': [0, 1, 2, 3], '''other''': (0, 1)}, 4), ({'''shards''': [0, 1, 2, 3], '''shards2''': [0, 1]}, RuntimeError), ] , ) def __lowerCamelCase ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict ): """simple docstring""" if expected is RuntimeError: with pytest.raises(UpperCAmelCase_ ): _number_of_shards_in_gen_kwargs(UpperCAmelCase_ ) else: a :int = _number_of_shards_in_gen_kwargs(UpperCAmelCase_ ) assert out == expected

import json import os import unittest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class _snake_case ( _snake_case , unittest.TestCase ): SCREAMING_SNAKE_CASE__ = MgpstrTokenizer SCREAMING_SNAKE_CASE__ = False SCREAMING_SNAKE_CASE__ = {} SCREAMING_SNAKE_CASE__ = False def SCREAMING_SNAKE_CASE__ ( self ): super().setUp() # fmt: off a :int = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z'''] # fmt: on a :List[str] = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) ) a :Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] ) with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp: fp.write(json.dumps(_lowerCamelCase ) + '''\n''' ) def SCREAMING_SNAKE_CASE__ ( self , **_lowerCamelCase ): return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase ) def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ): a :str = '''tester''' a :Union[str, Any] = '''tester''' return input_text, output_text @unittest.skip('''MGP-STR always lower cases letters.''' ) def SCREAMING_SNAKE_CASE__ ( self ): pass def SCREAMING_SNAKE_CASE__ ( self ): a :List[Any] = self.get_tokenizers(do_lower_case=_lowerCamelCase ) for tokenizer in tokenizers: with self.subTest(F'''{tokenizer.__class__.__name__}''' ): a :Any = '''[SPECIAL_TOKEN]''' tokenizer.add_special_tokens({'''cls_token''': special_token} ) a :str = tokenizer.encode([special_token] , add_special_tokens=_lowerCamelCase ) self.assertEqual(len(_lowerCamelCase ) , 1 ) a :Tuple = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase ) self.assertTrue(special_token not in decoded ) def SCREAMING_SNAKE_CASE__ ( self ): a :Optional[Any] = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(F'''{tokenizer.__class__.__name__}''' ): a , a :Tuple = self.get_input_output_texts(_lowerCamelCase ) a :Tuple = tokenizer.tokenize(_lowerCamelCase ) a :int = tokenizer.convert_tokens_to_ids(_lowerCamelCase ) a :Optional[int] = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase ) self.assertListEqual(_lowerCamelCase , _lowerCamelCase ) a :Any = tokenizer.convert_ids_to_tokens(_lowerCamelCase ) self.assertNotEqual(len(_lowerCamelCase ) , 0 ) a :str = tokenizer.decode(_lowerCamelCase ) self.assertIsInstance(_lowerCamelCase , _lowerCamelCase ) self.assertEqual(text_a.replace(''' ''' , '''''' ) , _lowerCamelCase ) @unittest.skip('''MGP-STR tokenizer only handles one sequence.''' ) def SCREAMING_SNAKE_CASE__ ( self ): pass @unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' ) def SCREAMING_SNAKE_CASE__ ( self ): pass

def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int: if not isinstance(_UpperCAmelCase , _UpperCAmelCase ): raise TypeError('only integers accepted as input' ) else: lowerCamelCase__ : List[Any] = str(abs(_UpperCAmelCase ) ) lowerCamelCase__ : Optional[Any] = [list(_UpperCAmelCase ) for char in range(len(_UpperCAmelCase ) )] for index in range(len(_UpperCAmelCase ) ): num_transpositions[index].pop(_UpperCAmelCase ) return max( int(''.join(list(_UpperCAmelCase ) ) ) for transposition in num_transpositions ) if __name__ == "__main__": __import__("""doctest""").testmod()

import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_realm import RealmTokenizer _UpperCAmelCase : Optional[int] = logging.get_logger(__name__) _UpperCAmelCase : Any = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} _UpperCAmelCase : int = { """vocab_file""": { """google/realm-cc-news-pretrained-embedder""": ( """https://huggingface.co./google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt""" ), """google/realm-cc-news-pretrained-encoder""": ( """https://huggingface.co./google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt""" ), """google/realm-cc-news-pretrained-scorer""": ( """https://huggingface.co./google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt""" ), """google/realm-cc-news-pretrained-openqa""": ( """https://huggingface.co./google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt""" ), """google/realm-orqa-nq-openqa""": """https://huggingface.co./google/realm-orqa-nq-openqa/resolve/main/vocab.txt""", """google/realm-orqa-nq-reader""": """https://huggingface.co./google/realm-orqa-nq-reader/resolve/main/vocab.txt""", """google/realm-orqa-wq-openqa""": """https://huggingface.co./google/realm-orqa-wq-openqa/resolve/main/vocab.txt""", """google/realm-orqa-wq-reader""": """https://huggingface.co./google/realm-orqa-wq-reader/resolve/main/vocab.txt""", }, """tokenizer_file""": { """google/realm-cc-news-pretrained-embedder""": ( """https://huggingface.co./google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont""" ), """google/realm-cc-news-pretrained-encoder""": ( """https://huggingface.co./google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json""" ), """google/realm-cc-news-pretrained-scorer""": ( """https://huggingface.co./google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json""" ), """google/realm-cc-news-pretrained-openqa""": ( """https://huggingface.co./google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json""" ), """google/realm-orqa-nq-openqa""": ( """https://huggingface.co./google/realm-orqa-nq-openqa/resolve/main/tokenizer.json""" ), """google/realm-orqa-nq-reader""": ( """https://huggingface.co./google/realm-orqa-nq-reader/resolve/main/tokenizer.json""" ), """google/realm-orqa-wq-openqa""": ( """https://huggingface.co./google/realm-orqa-wq-openqa/resolve/main/tokenizer.json""" ), """google/realm-orqa-wq-reader""": ( """https://huggingface.co./google/realm-orqa-wq-reader/resolve/main/tokenizer.json""" ), }, } _UpperCAmelCase : int = { """google/realm-cc-news-pretrained-embedder""": 5_12, """google/realm-cc-news-pretrained-encoder""": 5_12, """google/realm-cc-news-pretrained-scorer""": 5_12, """google/realm-cc-news-pretrained-openqa""": 5_12, """google/realm-orqa-nq-openqa""": 5_12, """google/realm-orqa-nq-reader""": 5_12, """google/realm-orqa-wq-openqa""": 5_12, """google/realm-orqa-wq-reader""": 5_12, } _UpperCAmelCase : Any = { """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True}, """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True}, """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True}, """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True}, """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True}, """google/realm-orqa-nq-reader""": {"""do_lower_case""": True}, """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True}, """google/realm-orqa-wq-reader""": {"""do_lower_case""": True}, } class lowerCAmelCase ( __UpperCamelCase ): UpperCAmelCase__ = VOCAB_FILES_NAMES UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP UpperCAmelCase__ = PRETRAINED_INIT_CONFIGURATION UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCAmelCase__ = RealmTokenizer def __init__( self : Optional[int] , UpperCAmelCase : Tuple=None , UpperCAmelCase : Any=None , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Optional[Any]="[UNK]" , UpperCAmelCase : Any="[SEP]" , UpperCAmelCase : Tuple="[PAD]" , UpperCAmelCase : List[Any]="[CLS]" , UpperCAmelCase : Union[str, Any]="[MASK]" , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Any=None , **UpperCAmelCase : Optional[int] , ) -> str: super().__init__( UpperCAmelCase , tokenizer_file=UpperCAmelCase , do_lower_case=UpperCAmelCase , unk_token=UpperCAmelCase , sep_token=UpperCAmelCase , pad_token=UpperCAmelCase , cls_token=UpperCAmelCase , mask_token=UpperCAmelCase , tokenize_chinese_chars=UpperCAmelCase , strip_accents=UpperCAmelCase , **UpperCAmelCase , ) lowerCamelCase__ : List[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get('lowercase' , UpperCAmelCase ) != do_lower_case or normalizer_state.get('strip_accents' , UpperCAmelCase ) != strip_accents or normalizer_state.get('handle_chinese_chars' , UpperCAmelCase ) != tokenize_chinese_chars ): lowerCamelCase__ : Optional[int] = getattr(UpperCAmelCase , normalizer_state.pop('type' ) ) lowerCamelCase__ : Optional[Any] = do_lower_case lowerCamelCase__ : str = strip_accents lowerCamelCase__ : Optional[Any] = tokenize_chinese_chars lowerCamelCase__ : int = normalizer_class(**UpperCAmelCase ) lowerCamelCase__ : str = do_lower_case def A_ ( self : Optional[int] , UpperCAmelCase : int , **UpperCAmelCase : int ) -> List[Any]: lowerCamelCase__ : List[Any] = PaddingStrategy.MAX_LENGTH lowerCamelCase__ : Optional[int] = text lowerCamelCase__ : Dict = kwargs.pop('text_pair' , UpperCAmelCase ) lowerCamelCase__ : List[Any] = kwargs.pop('return_tensors' , UpperCAmelCase ) lowerCamelCase__ : List[Any] = { 'input_ids': [], 'attention_mask': [], 'token_type_ids': [], } for idx, candidate_text in enumerate(UpperCAmelCase ): if batch_text_pair is not None: lowerCamelCase__ : Tuple = batch_text_pair[idx] else: lowerCamelCase__ : Dict = None lowerCamelCase__ : Optional[int] = super().__call__(UpperCAmelCase , UpperCAmelCase , return_tensors=UpperCAmelCase , **UpperCAmelCase ) lowerCamelCase__ : Any = encoded_candidates.get('input_ids' ) lowerCamelCase__ : Union[str, Any] = encoded_candidates.get('attention_mask' ) lowerCamelCase__ : Tuple = encoded_candidates.get('token_type_ids' ) if encoded_input_ids is not None: output_data["input_ids"].append(UpperCAmelCase ) if encoded_attention_mask is not None: output_data["attention_mask"].append(UpperCAmelCase ) if encoded_token_type_ids is not None: output_data["token_type_ids"].append(UpperCAmelCase ) lowerCamelCase__ : int = {key: item for key, item in output_data.items() if len(UpperCAmelCase ) != 0} return BatchEncoding(UpperCAmelCase , tensor_type=UpperCAmelCase ) def A_ ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Optional[int]=None ) -> List[str]: lowerCamelCase__ : Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def A_ ( self : Tuple , UpperCAmelCase : List[int] , UpperCAmelCase : Optional[List[int]] = None ) -> List[int]: lowerCamelCase__ : List[Any] = [self.sep_token_id] lowerCamelCase__ : Optional[int] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def A_ ( self : Optional[Any] , UpperCAmelCase : str , UpperCAmelCase : Optional[str] = None ) -> Tuple[str]: lowerCamelCase__ : int = self._tokenizer.model.save(UpperCAmelCase , name=UpperCAmelCase ) return tuple(UpperCAmelCase )

"""simple docstring""" import logging from pathlib import Path import numpy as np import pytorch_lightning as pl import torch from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from pytorch_lightning.utilities import rank_zero_only from utils_rag import save_json def lowerCAmelCase__ ( _UpperCamelCase : Any ) -> List[Any]: """simple docstring""" snake_case = filter(lambda _UpperCamelCase : p.requires_grad , model.parameters() ) snake_case = sum([np.prod(p.size() ) for p in model_parameters] ) return params SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__) def lowerCAmelCase__ ( _UpperCamelCase : Tuple , _UpperCamelCase : List[Any] ) -> Dict: """simple docstring""" if metric == "rouge2": snake_case = '{val_avg_rouge2:.4f}-{step_count}' elif metric == "bleu": snake_case = '{val_avg_bleu:.4f}-{step_count}' elif metric == "em": snake_case = '{val_avg_em:.4f}-{step_count}' elif metric == "loss": snake_case = '{val_avg_loss:.4f}-{step_count}' else: raise NotImplementedError( f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this""" ' function.' ) snake_case = ModelCheckpoint( dirpath=_UpperCamelCase , filename=_UpperCamelCase , monitor=f"""val_{metric}""" , mode='max' , save_top_k=1 , every_n_epochs=1 , ) return checkpoint_callback def lowerCAmelCase__ ( _UpperCamelCase : Optional[int] , _UpperCamelCase : List[str] ) -> int: """simple docstring""" return EarlyStopping( monitor=f"""val_{metric}""" , mode='min' if 'loss' in metric else 'max' , patience=_UpperCamelCase , verbose=_UpperCamelCase , ) class lowerCAmelCase_ ( pl.Callback ): """simple docstring""" def snake_case ( self , lowerCAmelCase , lowerCAmelCase ): """simple docstring""" snake_case = {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )} pl_module.logger.log_metrics(lowerCAmelCase ) @rank_zero_only def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=True ): """simple docstring""" logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""" ) snake_case = trainer.callback_metrics trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']} ) # Log results snake_case = Path(pl_module.hparams.output_dir ) if type_path == "test": snake_case = od / 'test_results.txt' snake_case = od / 'test_generations.txt' else: # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json # If people want this it will be easy enough to add back. snake_case = od / F"""{type_path}_results/{trainer.global_step:05d}.txt""" snake_case = od / F"""{type_path}_generations/{trainer.global_step:05d}.txt""" results_file.parent.mkdir(exist_ok=lowerCAmelCase ) generations_file.parent.mkdir(exist_ok=lowerCAmelCase ) with open(lowerCAmelCase , 'a+' ) as writer: for key in sorted(lowerCAmelCase ): if key in ["log", "progress_bar", "preds"]: continue snake_case = metrics[key] if isinstance(lowerCAmelCase , torch.Tensor ): snake_case = val.item() snake_case = F"""{key}: {val:.6f}\n""" writer.write(lowerCAmelCase ) if not save_generations: return if "preds" in metrics: snake_case = '\n'.join(metrics['preds'] ) generations_file.open('w+' ).write(lowerCAmelCase ) @rank_zero_only def snake_case ( self , lowerCAmelCase , lowerCAmelCase ): """simple docstring""" try: snake_case = pl_module.model.model.num_parameters() except AttributeError: snake_case = pl_module.model.num_parameters() snake_case = count_trainable_parameters(lowerCAmelCase ) # mp stands for million parameters trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6} ) @rank_zero_only def snake_case ( self , lowerCAmelCase , lowerCAmelCase ): """simple docstring""" save_json(pl_module.metrics , pl_module.metrics_save_path ) return self._write_logs(lowerCAmelCase , lowerCAmelCase , 'test' ) @rank_zero_only def snake_case ( self , lowerCAmelCase , lowerCAmelCase ): """simple docstring""" save_json(pl_module.metrics , pl_module.metrics_save_path ) # Uncommenting this will save val generations # return self._write_logs(trainer, pl_module, "valid")

"""simple docstring""" import pickle import unittest import torch from accelerate import Accelerator from accelerate.state import AcceleratorState from accelerate.test_utils import require_cpu @require_cpu class lowerCAmelCase_ ( unittest.TestCase ): """simple docstring""" def snake_case ( self ): """simple docstring""" snake_case = torch.nn.Linear(10 , 10 ) snake_case = torch.optim.SGD(model.parameters() , 0.1 ) snake_case = Accelerator() snake_case = accelerator.prepare(lowerCAmelCase ) try: pickle.loads(pickle.dumps(lowerCAmelCase ) ) except Exception as e: self.fail(F"""Accelerated optimizer pickling failed with {e}""" ) AcceleratorState._reset_state()

'''simple docstring''' import pyarrow.parquet as pq import pytest from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config from datasets.features.image import Image from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ): assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize("""keep_in_memory""" , [False, True] ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : Dict = tmp_path / """cache""" UpperCAmelCase__ : Any = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase__ : Any = ParquetDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ ).read() _check_parquet_dataset(UpperCamelCase__ , UpperCamelCase__ ) @pytest.mark.parametrize( """features""" , [ None, {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}, {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""}, {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""}, {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""}, ] , ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : Dict = tmp_path / """cache""" UpperCAmelCase__ : Dict = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} UpperCAmelCase__ : Any = features.copy() if features else default_expected_features UpperCAmelCase__ : Tuple = ( Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase__ : Optional[int] = ParquetDatasetReader(UpperCamelCase__ , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read() _check_parquet_dataset(UpperCamelCase__ , UpperCamelCase__ ) @pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : List[Any] = tmp_path / """cache""" UpperCAmelCase__ : Optional[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} UpperCAmelCase__ : str = ParquetDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ , split=UpperCamelCase__ ).read() _check_parquet_dataset(UpperCamelCase__ , UpperCamelCase__ ) assert dataset.split == split if split else "train" @pytest.mark.parametrize("""path_type""" , [str, list] ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): if issubclass(UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : Optional[int] = parquet_path elif issubclass(UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : Dict = [parquet_path] UpperCAmelCase__ : str = tmp_path / """cache""" UpperCAmelCase__ : int = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} UpperCAmelCase__ : Union[str, Any] = ParquetDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read() _check_parquet_dataset(UpperCamelCase__ , UpperCamelCase__ ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=("train",) ): assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) for split in splits: UpperCAmelCase__ : Tuple = dataset_dict[split] assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @pytest.mark.parametrize("""keep_in_memory""" , [False, True] ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : List[Any] = tmp_path / """cache""" UpperCAmelCase__ : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): UpperCAmelCase__ : Any = ParquetDatasetReader( {"""train""": parquet_path} , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ ).read() _check_parquet_datasetdict(UpperCamelCase__ , UpperCamelCase__ ) @pytest.mark.parametrize( """features""" , [ None, {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}, {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""}, {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""}, {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""}, ] , ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : List[str] = tmp_path / """cache""" UpperCAmelCase__ : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} UpperCAmelCase__ : Tuple = features.copy() if features else default_expected_features UpperCAmelCase__ : int = ( Features({feature: Value(UpperCamelCase__ ) for feature, dtype in features.items()} ) if features is not None else None ) UpperCAmelCase__ : List[str] = ParquetDatasetReader({"""train""": parquet_path} , features=UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read() _check_parquet_datasetdict(UpperCamelCase__ , UpperCamelCase__ ) @pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): if split: UpperCAmelCase__ : Any = {split: parquet_path} else: UpperCAmelCase__ : Optional[int] = """train""" UpperCAmelCase__ : List[Any] = {"""train""": parquet_path, """test""": parquet_path} UpperCAmelCase__ : int = tmp_path / """cache""" UpperCAmelCase__ : Dict = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} UpperCAmelCase__ : List[Any] = ParquetDatasetReader(UpperCamelCase__ , cache_dir=UpperCamelCase__ ).read() _check_parquet_datasetdict(UpperCamelCase__ , UpperCamelCase__ , splits=list(path.keys() ) ) assert all(dataset[split].split == split for split in path.keys() ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : Any = ParquetDatasetWriter(UpperCamelCase__ , tmp_path / """foo.parquet""" ) assert writer.write() > 0 UpperCAmelCase__ : Union[str, Any] = pq.ParquetFile(tmp_path / """foo.parquet""" ) UpperCAmelCase__ : Any = pf.read() assert dataset.data.table == output_table def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ): UpperCAmelCase__ : Dict = str(shared_datadir / """test_image_rgb.jpg""" ) UpperCAmelCase__ : int = {"""image""": [image_path]} UpperCAmelCase__ : Any = Features({"""image""": Image()} ) UpperCAmelCase__ : Union[str, Any] = Dataset.from_dict(UpperCamelCase__ , features=UpperCamelCase__ ) UpperCAmelCase__ : Dict = ParquetDatasetWriter(UpperCamelCase__ , tmp_path / """foo.parquet""" ) assert writer.write() > 0 UpperCAmelCase__ : Union[str, Any] = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) ) assert dataset.features == reloaded_dataset.features UpperCAmelCase__ : Optional[Any] = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=UpperCamelCase__ ).read() assert dataset.features == reloaded_iterable_dataset.features @pytest.mark.parametrize( """feature, expected""" , [ (Features({"""foo""": Value("""int32""" )} ), None), (Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS), (Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS), ] , ) def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ): assert get_writer_batch_size(UpperCamelCase__ ) == expected

'''simple docstring''' import unittest from transformers.testing_utils import CaptureStdout from transformers.tools.python_interpreter import evaluate def _UpperCamelCase ( UpperCamelCase__ ): return x + 2 class _snake_case ( unittest.TestCase ): def snake_case__ ( self): UpperCAmelCase__ : List[str] = """x = 3""" UpperCAmelCase__ : Dict = {} UpperCAmelCase__ : List[str] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) assert result == 3 self.assertDictEqual(_lowerCamelCase , {"""x""": 3}) UpperCAmelCase__ : Optional[int] = """x = y""" UpperCAmelCase__ : Optional[Any] = {"""y""": 5} UpperCAmelCase__ : Dict = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(_lowerCamelCase , {"""x""": 5, """y""": 5}) def snake_case__ ( self): UpperCAmelCase__ : Any = """y = add_two(x)""" UpperCAmelCase__ : Optional[Any] = {"""x""": 3} UpperCAmelCase__ : Tuple = evaluate(_lowerCamelCase , {"""add_two""": add_two} , state=_lowerCamelCase) assert result == 5 self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """y""": 5}) # Won't work without the tool with CaptureStdout() as out: UpperCAmelCase__ : List[str] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) assert result is None assert "tried to execute add_two" in out.out def snake_case__ ( self): UpperCAmelCase__ : Union[str, Any] = """x = 3""" UpperCAmelCase__ : Dict = {} UpperCAmelCase__ : Optional[int] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) assert result == 3 self.assertDictEqual(_lowerCamelCase , {"""x""": 3}) def snake_case__ ( self): UpperCAmelCase__ : Union[str, Any] = """test_dict = {'x': x, 'y': add_two(x)}""" UpperCAmelCase__ : Any = {"""x""": 3} UpperCAmelCase__ : List[str] = evaluate(_lowerCamelCase , {"""add_two""": add_two} , state=_lowerCamelCase) self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """y""": 5}) self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}}) def snake_case__ ( self): UpperCAmelCase__ : List[Any] = """x = 3\ny = 5""" UpperCAmelCase__ : Union[str, Any] = {} UpperCAmelCase__ : List[Any] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """y""": 5}) def snake_case__ ( self): UpperCAmelCase__ : Dict = """text = f'This is x: {x}.'""" UpperCAmelCase__ : str = {"""x""": 3} UpperCAmelCase__ : Optional[Any] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) # evaluate returns the value of the last assignment. assert result == "This is x: 3." self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """text""": """This is x: 3."""}) def snake_case__ ( self): UpperCAmelCase__ : Union[str, Any] = """if x <= 3:\n y = 2\nelse:\n y = 5""" UpperCAmelCase__ : Optional[Any] = {"""x""": 3} UpperCAmelCase__ : Optional[int] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) # evaluate returns the value of the last assignment. assert result == 2 self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """y""": 2}) UpperCAmelCase__ : Optional[int] = {"""x""": 8} UpperCAmelCase__ : int = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(_lowerCamelCase , {"""x""": 8, """y""": 5}) def snake_case__ ( self): UpperCAmelCase__ : Union[str, Any] = """test_list = [x, add_two(x)]""" UpperCAmelCase__ : int = {"""x""": 3} UpperCAmelCase__ : Tuple = evaluate(_lowerCamelCase , {"""add_two""": add_two} , state=_lowerCamelCase) self.assertListEqual(_lowerCamelCase , [3, 5]) self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """test_list""": [3, 5]}) def snake_case__ ( self): UpperCAmelCase__ : Tuple = """y = x""" UpperCAmelCase__ : Optional[Any] = {"""x""": 3} UpperCAmelCase__ : Optional[int] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase) assert result == 3 self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """y""": 3}) def snake_case__ ( self): UpperCAmelCase__ : List[str] = """test_list = [x, add_two(x)]\ntest_list[1]""" UpperCAmelCase__ : Union[str, Any] = {"""x""": 3} UpperCAmelCase__ : int = evaluate(_lowerCamelCase , {"""add_two""": add_two} , state=_lowerCamelCase) assert result == 5 self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """test_list""": [3, 5]}) UpperCAmelCase__ : List[str] = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']""" UpperCAmelCase__ : Any = {"""x""": 3} UpperCAmelCase__ : Dict = evaluate(_lowerCamelCase , {"""add_two""": add_two} , state=_lowerCamelCase) assert result == 5 self.assertDictEqual(_lowerCamelCase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}}) def snake_case__ ( self): UpperCAmelCase__ : Optional[int] = """x = 0\nfor i in range(3):\n x = i""" UpperCAmelCase__ : str = {} UpperCAmelCase__ : Tuple = evaluate(_lowerCamelCase , {"""range""": range} , state=_lowerCamelCase) assert result == 2 self.assertDictEqual(_lowerCamelCase , {"""x""": 2, """i""": 2})

"""simple docstring""" import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import GLPNImageProcessor class _lowerCAmelCase ( unittest.TestCase ): """simple docstring""" def __init__( self , __UpperCAmelCase , __UpperCAmelCase=7 , __UpperCAmelCase=3 , __UpperCAmelCase=1_8 , __UpperCAmelCase=3_0 , __UpperCAmelCase=4_0_0 , __UpperCAmelCase=True , __UpperCAmelCase=3_2 , __UpperCAmelCase=True , ): '''simple docstring''' lowerCAmelCase__ :List[Any] = parent lowerCAmelCase__ :Any = batch_size lowerCAmelCase__ :Any = num_channels lowerCAmelCase__ :str = image_size lowerCAmelCase__ :Optional[Any] = min_resolution lowerCAmelCase__ :Tuple = max_resolution lowerCAmelCase__ :str = do_resize lowerCAmelCase__ :Optional[Any] = size_divisor lowerCAmelCase__ :List[Any] = do_rescale def snake_case ( self ): '''simple docstring''' return { "do_resize": self.do_resize, "size_divisor": self.size_divisor, "do_rescale": self.do_rescale, } @require_torch @require_vision class _lowerCAmelCase ( a , unittest.TestCase ): """simple docstring""" __magic_name__ :Tuple = GLPNImageProcessor if is_vision_available() else None def snake_case ( self ): '''simple docstring''' lowerCAmelCase__ :int = GLPNImageProcessingTester(self ) @property def snake_case ( self ): '''simple docstring''' return self.image_processor_tester.prepare_image_processor_dict() def snake_case ( self ): '''simple docstring''' lowerCAmelCase__ :int = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__UpperCAmelCase , 'do_resize' ) ) self.assertTrue(hasattr(__UpperCAmelCase , 'size_divisor' ) ) self.assertTrue(hasattr(__UpperCAmelCase , 'resample' ) ) self.assertTrue(hasattr(__UpperCAmelCase , 'do_rescale' ) ) def snake_case ( self ): '''simple docstring''' pass def snake_case ( self ): '''simple docstring''' lowerCAmelCase__ :Any = self.image_processing_class(**self.image_processor_dict ) # create random PIL images lowerCAmelCase__ :int = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase ) for image in image_inputs: self.assertIsInstance(__UpperCAmelCase , Image.Image ) # Test not batched input (GLPNImageProcessor doesn't support batching) lowerCAmelCase__ :List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 ) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 ) def snake_case ( self ): '''simple docstring''' lowerCAmelCase__ :int = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors lowerCAmelCase__ :Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase ) for image in image_inputs: self.assertIsInstance(__UpperCAmelCase , np.ndarray ) # Test not batched input (GLPNImageProcessor doesn't support batching) lowerCAmelCase__ :List[str] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 ) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 ) def snake_case ( self ): '''simple docstring''' lowerCAmelCase__ :List[Any] = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors lowerCAmelCase__ :Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase ) for image in image_inputs: self.assertIsInstance(__UpperCAmelCase , torch.Tensor ) # Test not batched input (GLPNImageProcessor doesn't support batching) lowerCAmelCase__ :List[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 ) self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )

"""simple docstring""" import inspect import os import re from transformers.configuration_utils import PretrainedConfig from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_config_docstrings.py __A = """src/transformers""" # This is to make sure the transformers module imported is the one in the repo. __A = direct_transformers_import(PATH_TO_TRANSFORMERS) __A = transformers.models.auto.configuration_auto.CONFIG_MAPPING __A = { # used to compute the property `self.chunk_length` """EncodecConfig""": ["""overlap"""], # used as `self.bert_model = BertModel(config, ...)` """DPRConfig""": True, # not used in modeling files, but it's an important information """FSMTConfig""": ["""langs"""], # used internally in the configuration class file """GPTNeoConfig""": ["""attention_types"""], # used internally in the configuration class file """EsmConfig""": ["""is_folding_model"""], # used during training (despite we don't have training script for these models yet) """Mask2FormerConfig""": ["""ignore_value"""], # `ignore_value` used during training (despite we don't have training script for these models yet) # `norm` used in conversion script (despite not using in the modeling file) """OneFormerConfig""": ["""ignore_value""", """norm"""], # used during preprocessing and collation, see `collating_graphormer.py` """GraphormerConfig""": ["""spatial_pos_max"""], # used internally in the configuration class file """T5Config""": ["""feed_forward_proj"""], # used internally in the configuration class file # `tokenizer_class` get default value `T5Tokenizer` intentionally """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""], """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""], # used internally in the configuration class file """LongT5Config""": ["""feed_forward_proj"""], # used internally in the configuration class file """SwitchTransformersConfig""": ["""feed_forward_proj"""], # having default values other than `1e-5` - we can't fix them without breaking """BioGptConfig""": ["""layer_norm_eps"""], # having default values other than `1e-5` - we can't fix them without breaking """GLPNConfig""": ["""layer_norm_eps"""], # having default values other than `1e-5` - we can't fix them without breaking """SegformerConfig""": ["""layer_norm_eps"""], # having default values other than `1e-5` - we can't fix them without breaking """CvtConfig""": ["""layer_norm_eps"""], # having default values other than `1e-5` - we can't fix them without breaking """PerceiverConfig""": ["""layer_norm_eps"""], # used internally to calculate the feature size """InformerConfig""": ["""num_static_real_features""", """num_time_features"""], # used internally to calculate the feature size """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""], # used internally to calculate the feature size """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""], # used internally to calculate `mlp_dim` """SamVisionConfig""": ["""mlp_ratio"""], # For (head) training, but so far not implemented """ClapAudioConfig""": ["""num_classes"""], # Not used, but providing useful information to users """SpeechT5HifiGanConfig""": ["""sampling_rate"""], } # TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure SPECIAL_CASES_TO_ALLOW.update( { """CLIPSegConfig""": True, """DeformableDetrConfig""": True, """DetaConfig""": True, """DinatConfig""": True, """DonutSwinConfig""": True, """EfficientFormerConfig""": True, """FSMTConfig""": True, """JukeboxConfig""": True, """LayoutLMv2Config""": True, """MaskFormerSwinConfig""": True, """MT5Config""": True, """NatConfig""": True, """OneFormerConfig""": True, """PerceiverConfig""": True, """RagConfig""": True, """SpeechT5Config""": True, """SwinConfig""": True, """Swin2SRConfig""": True, """Swinv2Config""": True, """SwitchTransformersConfig""": True, """TableTransformerConfig""": True, """TapasConfig""": True, """TransfoXLConfig""": True, """UniSpeechConfig""": True, """UniSpeechSatConfig""": True, """WavLMConfig""": True, """WhisperConfig""": True, # TODO: @Arthur (for `alignment_head` and `alignment_layer`) """JukeboxPriorConfig""": True, # TODO: @Younes (for `is_decoder`) """Pix2StructTextConfig""": True, } ) def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int: """simple docstring""" lowerCAmelCase__ :List[str] = False for attribute in attributes: for modeling_source in source_strings: # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)` if ( F"config.{attribute}" in modeling_source or F"getattr(config, \"{attribute}\"" in modeling_source or F"getattr(self.config, \"{attribute}\"" in modeling_source ): lowerCAmelCase__ :List[str] = True # Deal with multi-line cases elif ( re.search( rF"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\"" , _SCREAMING_SNAKE_CASE , ) is not None ): lowerCAmelCase__ :int = True # `SequenceSummary` is called with `SequenceSummary(config)` elif attribute in [ "summary_type", "summary_use_proj", "summary_activation", "summary_last_dropout", "summary_proj_to_labels", "summary_first_dropout", ]: if "SequenceSummary" in modeling_source: lowerCAmelCase__ :Any = True if attribute_used: break if attribute_used: break # common and important attributes, even if they do not always appear in the modeling files lowerCAmelCase__ :Union[str, Any] = [ 'bos_index', 'eos_index', 'pad_index', 'unk_index', 'mask_index', 'image_size', 'use_cache', 'out_features', 'out_indices', ] lowerCAmelCase__ :Union[str, Any] = ['encoder_no_repeat_ngram_size'] # Special cases to be allowed lowerCAmelCase__ :Any = True if not attribute_used: lowerCAmelCase__ :List[Any] = False for attribute in attributes: # Allow if the default value in the configuration class is different from the one in `PretrainedConfig` if attribute in ["is_encoder_decoder"] and default_value is True: lowerCAmelCase__ :List[str] = True elif attribute in ["tie_word_embeddings"] and default_value is False: lowerCAmelCase__ :Tuple = True # Allow cases without checking the default value in the configuration class elif attribute in attributes_to_allow + attributes_used_in_generation: lowerCAmelCase__ :Optional[Any] = True elif attribute.endswith('_token_id' ): lowerCAmelCase__ :List[Any] = True # configuration class specific cases if not case_allowed: lowerCAmelCase__ :List[str] = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] ) lowerCAmelCase__ :List[Any] = allowed_cases is True or attribute in allowed_cases return attribute_used or case_allowed def __A (_SCREAMING_SNAKE_CASE ) ->Union[str, Any]: """simple docstring""" lowerCAmelCase__ :List[Any] = dict(inspect.signature(config_class.__init__ ).parameters ) lowerCAmelCase__ :List[Any] = [x for x in list(signature.keys() ) if x not in ['self', 'kwargs']] lowerCAmelCase__ :List[Any] = [signature[param].default for param in parameter_names] # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long # as one variant is used, the test should pass lowerCAmelCase__ :Optional[Any] = {} if len(config_class.attribute_map ) > 0: lowerCAmelCase__ :Optional[int] = {v: k for k, v in config_class.attribute_map.items()} # Get the path to modeling source files lowerCAmelCase__ :str = inspect.getsourcefile(_SCREAMING_SNAKE_CASE ) lowerCAmelCase__ :List[Any] = os.path.dirname(_SCREAMING_SNAKE_CASE ) # Let's check against all frameworks: as long as one framework uses an attribute, we are good. lowerCAmelCase__ :Dict = [os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for fn in os.listdir(_SCREAMING_SNAKE_CASE ) if fn.startswith('modeling_' )] # Get the source code strings lowerCAmelCase__ :Tuple = [] for path in modeling_paths: if os.path.isfile(_SCREAMING_SNAKE_CASE ): with open(_SCREAMING_SNAKE_CASE ) as fp: modeling_sources.append(fp.read() ) lowerCAmelCase__ :Any = [] for config_param, default_value in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): # `attributes` here is all the variant names for `config_param` lowerCAmelCase__ :Optional[int] = [config_param] # some configuration classes have non-empty `attribute_map`, and both names could be used in the # corresponding modeling files. As long as one of them appears, it is fine. if config_param in reversed_attribute_map: attributes.append(reversed_attribute_map[config_param] ) if not check_attribute_being_used(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): unused_attributes.append(attributes[0] ) return sorted(_SCREAMING_SNAKE_CASE ) def __A () ->List[Any]: """simple docstring""" lowerCAmelCase__ :Optional[int] = {} for _config_class in list(CONFIG_MAPPING.values() ): # Skip deprecated models if "models.deprecated" in _config_class.__module__: continue # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.) lowerCAmelCase__ :List[str] = [ cls for name, cls in inspect.getmembers( inspect.getmodule(_config_class ) , lambda _SCREAMING_SNAKE_CASE : inspect.isclass(_SCREAMING_SNAKE_CASE ) and issubclass(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and inspect.getmodule(_SCREAMING_SNAKE_CASE ) == inspect.getmodule(_config_class ) , ) ] for config_class in config_classes_in_module: lowerCAmelCase__ :Union[str, Any] = check_config_attributes_being_used(_SCREAMING_SNAKE_CASE ) if len(_SCREAMING_SNAKE_CASE ) > 0: lowerCAmelCase__ :int = unused_attributes if len(_SCREAMING_SNAKE_CASE ) > 0: lowerCAmelCase__ :Any = 'The following configuration classes contain unused attributes in the corresponding modeling files:\n' for name, attributes in configs_with_unused_attributes.items(): error += F"{name}: {attributes}\n" raise ValueError(_SCREAMING_SNAKE_CASE ) if __name__ == "__main__": check_config_attributes()

'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) lowercase__ : str = { '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''], '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''], '''processing_wav2vec2''': ['''Wav2Vec2Processor'''], '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ : Tuple = [ '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''', '''Wav2Vec2ForAudioFrameClassification''', '''Wav2Vec2ForCTC''', '''Wav2Vec2ForMaskedLM''', '''Wav2Vec2ForPreTraining''', '''Wav2Vec2ForSequenceClassification''', '''Wav2Vec2ForXVector''', '''Wav2Vec2Model''', '''Wav2Vec2PreTrainedModel''', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ : Optional[int] = [ '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFWav2Vec2ForCTC''', '''TFWav2Vec2Model''', '''TFWav2Vec2PreTrainedModel''', '''TFWav2Vec2ForSequenceClassification''', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ : Any = [ '''FlaxWav2Vec2ForCTC''', '''FlaxWav2Vec2ForPreTraining''', '''FlaxWav2Vec2Model''', '''FlaxWav2Vec2PreTrainedModel''', ] if TYPE_CHECKING: from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig from .feature_extraction_wavaveca import WavaVecaFeatureExtractor from .processing_wavaveca import WavaVecaProcessor from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_wavaveca import ( WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaForAudioFrameClassification, WavaVecaForCTC, WavaVecaForMaskedLM, WavaVecaForPreTraining, WavaVecaForSequenceClassification, WavaVecaForXVector, WavaVecaModel, WavaVecaPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_wavaveca import ( TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, TFWavaVecaForCTC, TFWavaVecaForSequenceClassification, TFWavaVecaModel, TFWavaVecaPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_wavaveca import ( FlaxWavaVecaForCTC, FlaxWavaVecaForPreTraining, FlaxWavaVecaModel, FlaxWavaVecaPreTrainedModel, ) else: import sys lowercase__ : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

'''simple docstring''' import itertools from dataclasses import dataclass from typing import Optional import pandas as pd import pyarrow as pa import datasets from datasets.table import table_cast @dataclass class SCREAMING_SNAKE_CASE (datasets.BuilderConfig ): lowerCAmelCase = None class SCREAMING_SNAKE_CASE (datasets.ArrowBasedBuilder ): lowerCAmelCase = PandasConfig def SCREAMING_SNAKE_CASE ( self): '''simple docstring''' return datasets.DatasetInfo(features=self.config.features) def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase): '''simple docstring''' if not self.config.data_files: raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}') __A : Dict = dl_manager.download_and_extract(self.config.data_files) if isinstance(_UpperCAmelCase , (str, list, tuple)): __A : Union[str, Any] = data_files if isinstance(_UpperCAmelCase , _UpperCAmelCase): __A : Optional[int] = [files] # Use `dl_manager.iter_files` to skip hidden files in an extracted archive __A : Optional[Any] = [dl_manager.iter_files(_UpperCAmelCase) for file in files] return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files})] __A : Tuple = [] for split_name, files in data_files.items(): if isinstance(_UpperCAmelCase , _UpperCAmelCase): __A : Dict = [files] # Use `dl_manager.iter_files` to skip hidden files in an extracted archive __A : Optional[Any] = [dl_manager.iter_files(_UpperCAmelCase) for file in files] splits.append(datasets.SplitGenerator(name=_UpperCAmelCase , gen_kwargs={'files': files})) return splits def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase): '''simple docstring''' if self.config.features is not None: # more expensive cast to support nested features with keys in a different order # allows str <-> int/float or str to Audio for example __A : List[str] = table_cast(_UpperCAmelCase , self.config.features.arrow_schema) return pa_table def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase): '''simple docstring''' for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)): with open(_UpperCAmelCase , 'rb') as f: __A : Optional[int] = pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase)) yield i, self._cast_table(_UpperCAmelCase)

from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available __snake_case = { """configuration_audio_spectrogram_transformer""": [ """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ASTConfig""", ] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __snake_case = [ """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""", """ASTForAudioClassification""", """ASTModel""", """ASTPreTrainedModel""", ] try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __snake_case = ["""ASTFeatureExtractor"""] if TYPE_CHECKING: from .configuration_audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ASTConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ASTForAudioClassification, ASTModel, ASTPreTrainedModel, ) try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor else: import sys __snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

from __future__ import annotations from statistics import mean def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> list[int]: '''simple docstring''' SCREAMING_SNAKE_CASE__ = [0] * no_of_processes SCREAMING_SNAKE_CASE__ = [0] * no_of_processes # Initialize remaining_time to waiting_time. for i in range(UpperCamelCase_ ): SCREAMING_SNAKE_CASE__ = burst_time[i] SCREAMING_SNAKE_CASE__ = [] SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = 0 # When processes are not completed, # A process whose arrival time has passed \ # and has remaining execution time is put into the ready_process. # The shortest process in the ready_process, target_process is executed. while completed != no_of_processes: SCREAMING_SNAKE_CASE__ = [] SCREAMING_SNAKE_CASE__ = -1 for i in range(UpperCamelCase_ ): if (arrival_time[i] <= total_time) and (remaining_time[i] > 0): ready_process.append(UpperCamelCase_ ) if len(UpperCamelCase_ ) > 0: SCREAMING_SNAKE_CASE__ = ready_process[0] for i in ready_process: if remaining_time[i] < remaining_time[target_process]: SCREAMING_SNAKE_CASE__ = i total_time += burst_time[target_process] completed += 1 SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = ( total_time - arrival_time[target_process] - burst_time[target_process] ) else: total_time += 1 return waiting_time def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> list[int]: '''simple docstring''' SCREAMING_SNAKE_CASE__ = [0] * no_of_processes for i in range(UpperCamelCase_ ): SCREAMING_SNAKE_CASE__ = burst_time[i] + waiting_time[i] return turn_around_time if __name__ == "__main__": print("""[TEST CASE 01]""") __snake_case = 4 __snake_case = [2, 5, 3, 7] __snake_case = [0, 0, 0, 0] __snake_case = calculate_waitingtime(arrival_time, burst_time, no_of_processes) __snake_case = calculate_turnaroundtime( burst_time, no_of_processes, waiting_time ) # Printing the Result print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""") for i, process_id in enumerate(list(range(1, 5))): print( F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t""" F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}""" ) print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""") print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")

"""simple docstring""" import numpy as np from numpy import ndarray from scipy.optimize import Bounds, LinearConstraint, minimize def lowercase__ ( snake_case_ :ndarray ): return np.dot(snake_case_ , snake_case_ ) class _UpperCAmelCase : def __init__( self : Union[str, Any] , *, _lowercase : float = np.inf , _lowercase : str = "linear" , _lowercase : float = 0.0 , ): __UpperCAmelCase = regularization __UpperCAmelCase = gamma if kernel == "linear": __UpperCAmelCase = self.__linear elif kernel == "rbf": if self.gamma == 0: raise ValueError('''rbf kernel requires gamma''' ) if not isinstance(self.gamma , (float, int) ): raise ValueError('''gamma must be float or int''' ) if not self.gamma > 0: raise ValueError('''gamma must be > 0''' ) __UpperCAmelCase = self.__rbf # in the future, there could be a default value like in sklearn # sklear: def_gamma = 1/(n_features * X.var()) (wiki) # previously it was 1/(n_features) else: __UpperCAmelCase = F'''Unknown kernel: {kernel}''' raise ValueError(_lowercase ) def a ( self : Dict , _lowercase : ndarray , _lowercase : ndarray ): return np.dot(_lowercase , _lowercase ) def a ( self : Any , _lowercase : ndarray , _lowercase : ndarray ): return np.exp(-(self.gamma * norm_squared(vectora - vectora )) ) def a ( self : Union[str, Any] , _lowercase : list[ndarray] , _lowercase : ndarray ): __UpperCAmelCase = observations __UpperCAmelCase = classes # using Wolfe's Dual to calculate w. # Primal problem: minimize 1/2*norm_squared(w) # constraint: yn(w . xn + b) >= 1 # # With l a vector # Dual problem: maximize sum_n(ln) - # 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm)) # constraint: self.C >= ln >= 0 # and sum_n(ln*yn) = 0 # Then we get w using w = sum_n(ln*yn*xn) # At the end we can get b ~= mean(yn - w . xn) # # Since we use kernels, we only need l_star to calculate b # and to classify observations ((__UpperCAmelCase) , ) = np.shape(_lowercase ) def to_minimize(_lowercase : ndarray ) -> float: __UpperCAmelCase = 0 ((__UpperCAmelCase) , ) = np.shape(_lowercase ) for i in range(_lowercase ): for j in range(_lowercase ): s += ( candidate[i] * candidate[j] * classes[i] * classes[j] * self.kernel(observations[i] , observations[j] ) ) return 1 / 2 * s - sum(_lowercase ) __UpperCAmelCase = LinearConstraint(_lowercase , 0 , 0 ) __UpperCAmelCase = Bounds(0 , self.regularization ) __UpperCAmelCase = minimize( _lowercase , np.ones(_lowercase ) , bounds=_lowercase , constraints=[ly_contraint] ).x __UpperCAmelCase = l_star # calculating mean offset of separation plane to points __UpperCAmelCase = 0 for i in range(_lowercase ): for j in range(_lowercase ): s += classes[i] - classes[i] * self.optimum[i] * self.kernel( observations[i] , observations[j] ) __UpperCAmelCase = s / n def a ( self : List[Any] , _lowercase : ndarray ): __UpperCAmelCase = sum( self.optimum[n] * self.classes[n] * self.kernel(self.observations[n] , _lowercase ) for n in range(len(self.classes ) ) ) return 1 if s + self.offset >= 0 else -1 if __name__ == "__main__": import doctest doctest.testmod()

"""simple docstring""" import unittest import torch from torch import nn from accelerate.test_utils import require_cuda from accelerate.utils.memory import find_executable_batch_size, release_memory def lowercase__ ( ): raise RuntimeError('''CUDA out of memory.''' ) class _UpperCAmelCase ( nn.Module ): def __init__( self : Optional[Any] ): super().__init__() __UpperCAmelCase = nn.Linear(3 , 4 ) __UpperCAmelCase = nn.BatchNormad(4 ) __UpperCAmelCase = nn.Linear(4 , 5 ) def a ( self : Optional[int] , _lowercase : Optional[Any] ): return self.lineara(self.batchnorm(self.lineara(_lowercase ) ) ) class _UpperCAmelCase ( unittest.TestCase ): def a ( self : List[str] ): __UpperCAmelCase = [] @find_executable_batch_size(starting_batch_size=1_28 ) def mock_training_loop_function(_lowercase : Optional[int] ): nonlocal batch_sizes batch_sizes.append(_lowercase ) if batch_size != 8: raise_fake_out_of_memory() mock_training_loop_function() self.assertListEqual(_lowercase , [1_28, 64, 32, 16, 8] ) def a ( self : Optional[int] ): __UpperCAmelCase = [] @find_executable_batch_size(starting_batch_size=1_28 ) def mock_training_loop_function(_lowercase : str , _lowercase : List[str] ): nonlocal batch_sizes batch_sizes.append(_lowercase ) if batch_size != 8: raise_fake_out_of_memory() return batch_size, arga __UpperCAmelCase , __UpperCAmelCase = mock_training_loop_function('''hello''' ) self.assertListEqual(_lowercase , [1_28, 64, 32, 16, 8] ) self.assertListEqual([bs, arga] , [8, '''hello'''] ) def a ( self : Tuple ): @find_executable_batch_size(starting_batch_size=0 ) def mock_training_loop_function(_lowercase : Optional[int] ): pass with self.assertRaises(_lowercase ) as cm: mock_training_loop_function() self.assertIn('''No executable batch size found, reached zero.''' , cm.exception.args[0] ) def a ( self : List[Any] ): @find_executable_batch_size(starting_batch_size=16 ) def mock_training_loop_function(_lowercase : List[Any] ): if batch_size > 0: raise_fake_out_of_memory() pass with self.assertRaises(_lowercase ) as cm: mock_training_loop_function() self.assertIn('''No executable batch size found, reached zero.''' , cm.exception.args[0] ) def a ( self : Union[str, Any] ): @find_executable_batch_size(starting_batch_size=1_28 ) def mock_training_loop_function(_lowercase : Optional[Any] , _lowercase : List[str] , _lowercase : str ): if batch_size != 8: raise raise_fake_out_of_memory() with self.assertRaises(_lowercase ) as cm: mock_training_loop_function(1_28 , '''hello''' , '''world''' ) self.assertIn('''Batch size was passed into `f`''' , cm.exception.args[0] ) self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')''' , cm.exception.args[0] ) def a ( self : Dict ): @find_executable_batch_size(starting_batch_size=16 ) def mock_training_loop_function(_lowercase : int ): raise ValueError('''Oops, we had an error!''' ) with self.assertRaises(_lowercase ) as cm: mock_training_loop_function() self.assertIn('''Oops, we had an error!''' , cm.exception.args[0] ) @require_cuda def a ( self : str ): __UpperCAmelCase = torch.cuda.memory_allocated() __UpperCAmelCase = ModelForTest() model.cuda() self.assertGreater(torch.cuda.memory_allocated() , _lowercase ) __UpperCAmelCase = release_memory(_lowercase ) self.assertEqual(torch.cuda.memory_allocated() , _lowercase )

"""simple docstring""" SCREAMING_SNAKE_CASE = "Alexander Joslin" import operator as op from .stack import Stack def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> int: A__ = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub} A__ = Stack() A__ = Stack() for i in equation: if i.isdigit(): # RULE 1 operand_stack.push(int(lowercase_ ) ) elif i in operators: # RULE 2 operator_stack.push(lowercase_ ) elif i == ")": # RULE 4 A__ = operator_stack.peek() operator_stack.pop() A__ = operand_stack.peek() operand_stack.pop() A__ = operand_stack.peek() operand_stack.pop() A__ = operators[opr](lowercase_ , lowercase_ ) operand_stack.push(lowercase_ ) # RULE 5 return operand_stack.peek() if __name__ == "__main__": SCREAMING_SNAKE_CASE = "(5 + ((4 * 2) * (2 + 3)))" # answer = 45 print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')

"""simple docstring""" SCREAMING_SNAKE_CASE = [ 999, 800, 799, 600, 599, 500, 400, 399, 377, 355, 333, 311, 288, 266, 244, 222, 200, 199, 177, 155, 133, 111, 88, 66, 44, 22, 0, ] SCREAMING_SNAKE_CASE = [ 999, 976, 952, 928, 905, 882, 858, 857, 810, 762, 715, 714, 572, 429, 428, 286, 285, 238, 190, 143, 142, 118, 95, 71, 47, 24, 0, ] SCREAMING_SNAKE_CASE = [ 999, 988, 977, 966, 955, 944, 933, 922, 911, 900, 899, 879, 859, 840, 820, 800, 799, 766, 733, 700, 699, 650, 600, 599, 500, 499, 400, 399, 350, 300, 299, 266, 233, 200, 199, 179, 159, 140, 120, 100, 99, 88, 77, 66, 55, 44, 33, 22, 11, 0, ] SCREAMING_SNAKE_CASE = [ 999, 995, 992, 989, 985, 981, 978, 975, 971, 967, 964, 961, 957, 956, 951, 947, 942, 937, 933, 928, 923, 919, 914, 913, 908, 903, 897, 892, 887, 881, 876, 871, 870, 864, 858, 852, 846, 840, 834, 828, 827, 820, 813, 806, 799, 792, 785, 784, 777, 770, 763, 756, 749, 742, 741, 733, 724, 716, 707, 699, 698, 688, 677, 666, 656, 655, 645, 634, 623, 613, 612, 598, 584, 570, 569, 555, 541, 527, 526, 505, 484, 483, 462, 440, 439, 396, 395, 352, 351, 308, 307, 264, 263, 220, 219, 176, 132, 88, 44, 0, ] SCREAMING_SNAKE_CASE = [ 999, 997, 995, 992, 990, 988, 986, 984, 981, 979, 977, 975, 972, 970, 968, 966, 964, 961, 959, 957, 956, 954, 951, 949, 946, 944, 941, 939, 936, 934, 931, 929, 926, 924, 921, 919, 916, 914, 913, 910, 907, 905, 902, 899, 896, 893, 891, 888, 885, 882, 879, 877, 874, 871, 870, 867, 864, 861, 858, 855, 852, 849, 846, 843, 840, 837, 834, 831, 828, 827, 824, 821, 817, 814, 811, 808, 804, 801, 798, 795, 791, 788, 785, 784, 780, 777, 774, 770, 766, 763, 760, 756, 752, 749, 746, 742, 741, 737, 733, 730, 726, 722, 718, 714, 710, 707, 703, 699, 698, 694, 690, 685, 681, 677, 673, 669, 664, 660, 656, 655, 650, 646, 641, 636, 632, 627, 622, 618, 613, 612, 607, 602, 596, 591, 586, 580, 575, 570, 569, 563, 557, 551, 545, 539, 533, 527, 526, 519, 512, 505, 498, 491, 484, 483, 474, 466, 457, 449, 440, 439, 428, 418, 407, 396, 395, 381, 366, 352, 351, 330, 308, 307, 286, 264, 263, 242, 220, 219, 176, 175, 132, 131, 88, 44, 0, ] SCREAMING_SNAKE_CASE = [ 999, 991, 982, 974, 966, 958, 950, 941, 933, 925, 916, 908, 900, 899, 874, 850, 825, 800, 799, 700, 600, 500, 400, 300, 200, 100, 0, ] SCREAMING_SNAKE_CASE = [ 999, 992, 985, 978, 971, 964, 957, 949, 942, 935, 928, 921, 914, 907, 900, 899, 879, 859, 840, 820, 800, 799, 766, 733, 700, 699, 650, 600, 599, 500, 499, 400, 399, 300, 299, 200, 199, 100, 99, 0, ] SCREAMING_SNAKE_CASE = [ 999, 996, 992, 989, 985, 982, 979, 975, 972, 968, 965, 961, 958, 955, 951, 948, 944, 941, 938, 934, 931, 927, 924, 920, 917, 914, 910, 907, 903, 900, 899, 891, 884, 876, 869, 861, 853, 846, 838, 830, 823, 815, 808, 800, 799, 788, 777, 766, 755, 744, 733, 722, 711, 700, 699, 688, 677, 666, 655, 644, 633, 622, 611, 600, 599, 585, 571, 557, 542, 528, 514, 500, 499, 485, 471, 457, 442, 428, 414, 400, 399, 379, 359, 340, 320, 300, 299, 279, 259, 240, 220, 200, 199, 166, 133, 100, 99, 66, 33, 0, ]

"""simple docstring""" import requests _snake_case = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey=' def lowerCAmelCase__ ( UpperCamelCase__ ): '''simple docstring''' # fetching a list of articles in json format _a : Dict = requests.get(_NEWS_API + bbc_news_api_key ).json() # each article in the list is a dict for i, article in enumerate(bbc_news_page["""articles"""] , 1 ): print(F"""{i}.) {article['title']}""" ) if __name__ == "__main__": fetch_bbc_news(bbc_news_api_key='<Your BBC News API key goes here>')

"""simple docstring""" from __future__ import annotations def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ): '''simple docstring''' print(F"""Vertex\tShortest Distance from vertex {src}""" ) for i, d in enumerate(UpperCamelCase__ ): print(F"""{i}\t\t{d}""" ) def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): '''simple docstring''' for j in range(UpperCamelCase__ ): _a , _a , _a : List[str] = (graph[j][k] for k in ["""src""", """dst""", """weight"""]) if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]: return True return False def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ): '''simple docstring''' _a : Dict = [float("""inf""" )] * vertex_count _a : Any = 0.0 for _ in range(vertex_count - 1 ): for j in range(UpperCamelCase__ ): _a , _a , _a : List[Any] = (graph[j][k] for k in ["""src""", """dst""", """weight"""]) if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]: _a : Any = distance[u] + w _a : Union[str, Any] = check_negative_cycle(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) if negative_cycle_exists: raise Exception("""Negative cycle found""" ) return distance if __name__ == "__main__": import doctest doctest.testmod() _snake_case = int(input('Enter number of vertices: ').strip()) _snake_case = int(input('Enter number of edges: ').strip()) _snake_case = [{} for _ in range(E)] for i in range(E): print('Edge ', i + 1) _snake_case , _snake_case , _snake_case = ( int(x) for x in input('Enter source, destination, weight: ').strip().split(' ') ) _snake_case = {'src': src, 'dst': dest, 'weight': weight} _snake_case = int(input('\nEnter shortest path source:').strip()) _snake_case = bellman_ford(graph, V, E, source) print_distance(shortest_distance, 0)

"""simple docstring""" from __future__ import annotations from statistics import mean def _snake_case ( _snake_case : list[int] , _snake_case : list[int] , _snake_case : int ) -> list[int]: '''simple docstring''' _A = [0] * no_of_processes _A = [0] * no_of_processes # Initialize remaining_time to waiting_time. for i in range(_snake_case ): _A = burst_time[i] _A = [] _A = 0 _A = 0 # When processes are not completed, # A process whose arrival time has passed \ # and has remaining execution time is put into the ready_process. # The shortest process in the ready_process, target_process is executed. while completed != no_of_processes: _A = [] _A = -1 for i in range(_snake_case ): if (arrival_time[i] <= total_time) and (remaining_time[i] > 0): ready_process.append(_snake_case ) if len(_snake_case ) > 0: _A = ready_process[0] for i in ready_process: if remaining_time[i] < remaining_time[target_process]: _A = i total_time += burst_time[target_process] completed += 1 _A = 0 _A = ( total_time - arrival_time[target_process] - burst_time[target_process] ) else: total_time += 1 return waiting_time def _snake_case ( _snake_case : list[int] , _snake_case : int , _snake_case : list[int] ) -> list[int]: '''simple docstring''' _A = [0] * no_of_processes for i in range(_snake_case ): _A = burst_time[i] + waiting_time[i] return turn_around_time if __name__ == "__main__": print('''[TEST CASE 01]''') a = 4 a = [2, 5, 3, 7] a = [0, 0, 0, 0] a = calculate_waitingtime(arrival_time, burst_time, no_of_processes) a = calculate_turnaroundtime( burst_time, no_of_processes, waiting_time ) # Printing the Result print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''') for i, process_id in enumerate(list(range(1, 5))): print( F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t''' F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}''' ) print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''') print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')

"""simple docstring""" import argparse import os import torch from transformers import ( XLNetConfig, XLNetForQuestionAnswering, XLNetForSequenceClassification, XLNetLMHeadModel, load_tf_weights_in_xlnet, ) from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging a = { '''cola''': 2, '''mnli''': 3, '''mrpc''': 2, '''sst-2''': 2, '''sts-b''': 1, '''qqp''': 2, '''qnli''': 2, '''rte''': 2, '''wnli''': 2, } logging.set_verbosity_info() def _snake_case ( _snake_case : str , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Tuple=None ) -> List[str]: '''simple docstring''' _A = XLNetConfig.from_json_file(_snake_case ) _A = finetuning_task.lower() if finetuning_task is not None else '' if finetuning_task in GLUE_TASKS_NUM_LABELS: print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}''' ) _A = finetuning_task _A = GLUE_TASKS_NUM_LABELS[finetuning_task] _A = XLNetForSequenceClassification(_snake_case ) elif "squad" in finetuning_task: _A = finetuning_task _A = XLNetForQuestionAnswering(_snake_case ) else: _A = XLNetLMHeadModel(_snake_case ) # Load weights from tf checkpoint load_tf_weights_in_xlnet(_snake_case , _snake_case , _snake_case ) # Save pytorch-model _A = os.path.join(_snake_case , _snake_case ) _A = os.path.join(_snake_case , _snake_case ) print(F'''Save PyTorch model to {os.path.abspath(_snake_case )}''' ) torch.save(model.state_dict() , _snake_case ) print(F'''Save configuration file to {os.path.abspath(_snake_case )}''' ) with open(_snake_case , 'w' , encoding='utf-8' ) as f: f.write(config.to_json_string() ) if __name__ == "__main__": a = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.''' ) parser.add_argument( '''--xlnet_config_file''', default=None, type=str, required=True, help=( '''The config json file corresponding to the pre-trained XLNet model. \n''' '''This specifies the model architecture.''' ), ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the folder to store the PyTorch model or dataset/vocab.''', ) parser.add_argument( '''--finetuning_task''', default=None, type=str, help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''', ) a = parser.parse_args() print(args) convert_xlnet_checkpoint_to_pytorch( args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task )

def lowercase_ (A : int = 2_0_0_0_0_0_0 ): snake_case__ : Dict = [0 for i in range(n + 1 )] snake_case__ : List[str] = 1 snake_case__ : List[str] = 1 for i in range(2 , int(n**0.5 ) + 1 ): if primality_list[i] == 0: for j in range(i * i , n + 1 , A ): snake_case__ : List[str] = 1 snake_case__ : List[Any] = 0 for i in range(A ): if primality_list[i] == 0: sum_of_primes += i return sum_of_primes if __name__ == "__main__": print(F"""{solution() = }""")

import argparse import fairseq import torch from torch import nn from transformers import ( MBartaaTokenizer, MBartConfig, MBartForCausalLM, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel, WavaVecaConfig, WavaVecaFeatureExtractor, WavaVecaModel, logging, ) logging.set_verbosity_info() a_ :Tuple = logging.get_logger(__name__) a_ :List[Any] = { "post_extract_proj": "feature_projection.projection", "encoder.pos_conv.0": "encoder.pos_conv_embed.conv", "self_attn.k_proj": "encoder.layers.*.attention.k_proj", "self_attn.v_proj": "encoder.layers.*.attention.v_proj", "self_attn.q_proj": "encoder.layers.*.attention.q_proj", "self_attn.out_proj": "encoder.layers.*.attention.out_proj", "self_attn_layer_norm": "encoder.layers.*.layer_norm", "fc1": "encoder.layers.*.feed_forward.intermediate_dense", "fc2": "encoder.layers.*.feed_forward.output_dense", "final_layer_norm": "encoder.layers.*.final_layer_norm", "encoder.layer_norm": "encoder.layer_norm", "w2v_model.layer_norm": "feature_projection.layer_norm", "quantizer.weight_proj": "quantizer.weight_proj", "quantizer.vars": "quantizer.codevectors", "project_q": "project_q", "final_proj": "project_hid", "w2v_encoder.proj": "lm_head", "mask_emb": "masked_spec_embed", } a_ :Optional[int] = [ "lm_head", "quantizer.weight_proj", "quantizer.codevectors", "project_q", "project_hid", ] def lowercase_ (A : Union[str, Any] , A : str , A : Dict , A : Optional[Any] , A : Optional[Any] ): for attribute in key.split('.' ): snake_case__ : Any = getattr(A , A ) if weight_type is not None: snake_case__ : Optional[Any] = getattr(A , A ).shape else: snake_case__ : Optional[int] = hf_pointer.shape assert hf_shape == value.shape, ( F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be''' F''' {value.shape} for {full_name}''' ) if weight_type == "weight": snake_case__ : Tuple = value elif weight_type == "weight_g": snake_case__ : Tuple = value elif weight_type == "weight_v": snake_case__ : List[Any] = value elif weight_type == "bias": snake_case__ : List[Any] = value else: snake_case__ : Optional[Any] = value logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' ) def lowercase_ (A : str , A : Any ): snake_case__ : Union[str, Any] = [] snake_case__ : Union[str, Any] = fairseq_model.state_dict() snake_case__ : Union[str, Any] = hf_model.feature_extractor snake_case__ : Any = hf_model.adapter for name, value in fairseq_dict.items(): snake_case__ : Any = False if "conv_layers" in name: load_conv_layer( A , A , A , A , hf_model.config.feat_extract_norm == 'group' , ) snake_case__ : List[Any] = True elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.'] ): load_adapter(A , A , A , A ) snake_case__ : Optional[Any] = True else: for key, mapped_key in MAPPING.items(): if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]: snake_case__ : Tuple = True if "*" in mapped_key: snake_case__ : List[Any] = name.split(A )[0].split('.' )[-2] snake_case__ : Optional[int] = mapped_key.replace('*' , A ) if "weight_g" in name: snake_case__ : Optional[int] = 'weight_g' elif "weight_v" in name: snake_case__ : Optional[Any] = 'weight_v' elif "bias" in name: snake_case__ : Union[str, Any] = 'bias' elif "weight" in name: snake_case__ : Optional[int] = 'weight' else: snake_case__ : Tuple = None set_recursively(A , A , A , A , A ) continue if not is_used: unused_weights.append(A ) logger.warning(F'''Unused weights: {unused_weights}''' ) def lowercase_ (A : Union[str, Any] , A : Any , A : str , A : str , A : int ): snake_case__ : str = full_name.split('conv_layers.' )[-1] snake_case__ : Optional[int] = name.split('.' ) snake_case__ : Tuple = int(items[0] ) snake_case__ : Any = int(items[1] ) if type_id == 0: if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' ) snake_case__ : Union[str, Any] = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' ) snake_case__ : Union[str, Any] = value logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' ) elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm): if "bias" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, ( F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was''' " found." ) snake_case__ : Optional[int] = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) elif "weight" in name: assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, ( F'''{full_name} has size {value.shape}, but''' F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.''' ) snake_case__ : Optional[Any] = value logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' ) else: unused_weights.append(A ) def lowercase_ (A : Optional[Any] , A : Any , A : Tuple , A : Any ): snake_case__ : List[str] = full_name.split('adaptor.' )[-1] snake_case__ : Tuple = name.split('.' ) if items[1].isdigit(): snake_case__ : Optional[int] = int(items[1] ) else: snake_case__ : Any = None if "adaptor" not in full_name: if "proj_ln" in full_name: # has to be layer norm if "bias" in name: assert ( value.shape == adapter.proj_layer_norm.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.''' snake_case__ : List[Any] = value logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj_layer_norm.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.''' snake_case__ : int = value else: # has to be projection layer if "bias" in name: assert ( value.shape == adapter.proj.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.''' snake_case__ : str = value logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' ) if "weight" in name: assert ( value.shape == adapter.proj.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.''' snake_case__ : Dict = value logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' ) elif isinstance(A , A ): if "bias" in name: assert ( value.shape == adapter.layers[layer_id].conv.bias.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.''' snake_case__ : List[str] = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) elif "weight" in name: assert ( value.shape == adapter.layers[layer_id].conv.weight.data.shape ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.''' snake_case__ : List[str] = value logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' ) else: unused_weights.append(A ) def lowercase_ (A : int ): snake_case__ , snake_case__ : Union[str, Any] = emb.weight.shape snake_case__ : int = nn.Linear(A , A , bias=A ) snake_case__ : Optional[Any] = emb.weight.data return lin_layer @torch.no_grad() def lowercase_ (A : Tuple , A : Tuple , A : Any , A : Optional[Any] , A : int , A : Optional[Any] , A : Union[str, Any] , A : Union[str, Any] , A : Optional[Any] , A : List[Any] , A : Union[str, Any] , ): snake_case__ : Optional[Any] = WavaVecaConfig.from_pretrained( A , add_adapter=A , adapter_stride=A , adapter_kernel_size=A , use_auth_token=A , output_hidden_size=A , ) snake_case__ : Dict = MBartConfig.from_pretrained(A ) # load model snake_case__ , snake_case__ , snake_case__ : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={ 'config_yaml': config_yaml_path, 'data': '/'.join(dict_path.split('/' )[:-1] ), 'w2v_path': checkpoint_path, 'load_pretrained_decoder_from': None, } , ) snake_case__ : List[Any] = model[0].eval() # load feature extractor snake_case__ : str = WavaVecaFeatureExtractor.from_pretrained(A , use_auth_token=A ) # set weights for wav2vec2 encoder snake_case__ : List[str] = WavaVecaModel(A ) recursively_load_weights_wavaveca(model.encoder , A ) # load decoder weights snake_case__ : Any = MBartForCausalLM(A ) snake_case__ , snake_case__ : int = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=A ) logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' ) logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' ) snake_case__ : Union[str, Any] = SpeechEncoderDecoderModel(encoder=A , decoder=A ) snake_case__ : str = False snake_case__ : int = MBartaaTokenizer(A ) tokenizer.save_pretrained(A ) snake_case__ : Any = hf_wavavec.config.to_dict() snake_case__ : Tuple = tokenizer.pad_token_id snake_case__ : Union[str, Any] = tokenizer.bos_token_id snake_case__ : Dict = tokenizer.eos_token_id snake_case__ : Optional[int] = 'mbart50' snake_case__ : Union[str, Any] = 'wav2vec2' snake_case__ : List[str] = tokenizer.eos_token_id snake_case__ : Union[str, Any] = 2_5_0_0_0_4 snake_case__ : int = tokenizer.eos_token_id snake_case__ : Union[str, Any] = SpeechEncoderDecoderConfig.from_dict(A ) hf_wavavec.save_pretrained(A ) feature_extractor.save_pretrained(A ) if __name__ == "__main__": a_ :str = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint") parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model") parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model") parser.add_argument( "--encoder_config_path", default="facebook/wav2vec2-xls-r-1b", type=str, help="Path to hf encoder wav2vec2 checkpoint config", ) parser.add_argument( "--decoder_config_path", default="facebook/mbart-large-50-one-to-many-mmt", type=str, help="Path to hf decoder checkpoint config", ) parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers") parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers") parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers") parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim") parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config") a_ :Union[str, Any] = parser.parse_args() convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.dict_path, args.config_yaml_path, encoder_config_path=args.encoder_config_path, decoder_config_path=args.decoder_config_path, add_adapter=args.add_adapter, adapter_kernel_size=args.adapter_kernel_size, adapter_stride=args.adapter_stride, decoder_start_token_id=args.start_token_id, encoder_output_dim=args.encoder_output_dim, )

'''simple docstring''' def a ( lowerCamelCase__ ): '''simple docstring''' A_ : List[Any] = [0] * len(lowerCamelCase__ ) A_ : int = [] A_ : int = [] A_ : Tuple = 0 for values in graph.values(): for i in values: indegree[i] += 1 for i in range(len(lowerCamelCase__ ) ): if indegree[i] == 0: queue.append(lowerCamelCase__ ) while queue: A_ : Dict = queue.pop(0 ) cnt += 1 topo.append(lowerCamelCase__ ) for x in graph[vertex]: indegree[x] -= 1 if indegree[x] == 0: queue.append(lowerCamelCase__ ) if cnt != len(lowerCamelCase__ ): print("""Cycle exists""" ) else: print(lowerCamelCase__ ) # Adjacency List of Graph lowerCamelCase :Tuple = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []} topological_sort(graph)

'''simple docstring''' import unittest import numpy as np import torch from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad class _lowerCAmelCase ( unittest.TestCase ): def _a (self ): A_ : Dict = 10 def _a (self ): A_ : str = [1, 2, 3, 4] A_ : List[str] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0] self.assertEqual(truncate_or_pad(lowercase , self.block_size , 0 ) , lowercase ) def _a (self ): A_ : str = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] A_ : Tuple = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(truncate_or_pad(lowercase , self.block_size , 0 ) , lowercase ) def _a (self ): A_ : List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] A_ : str = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(truncate_or_pad(lowercase , self.block_size , 0 ) , lowercase ) def _a (self ): A_ : Any = """It was the year of Our Lord one thousand seven hundred and seventy-five.\n\nSpiritual revelations were conceded to England at that favoured period, as at this.""" A_, A_ : Optional[int] = process_story(lowercase ) self.assertEqual(lowercase , [] ) def _a (self ): A_ : Union[str, Any] = """""" A_, A_ : Union[str, Any] = process_story(lowercase ) self.assertEqual(lowercase , [] ) self.assertEqual(lowercase , [] ) def _a (self ): A_ : List[Any] = ( """It was the year of Our Lord one thousand seven hundred and """ """seventy-five\n\nSpiritual revelations were conceded to England """ """at that favoured period, as at this.\n@highlight\n\nIt was the best of times""" ) A_, A_ : int = process_story(lowercase ) A_ : List[Any] = [ """It was the year of Our Lord one thousand seven hundred and seventy-five.""", """Spiritual revelations were conceded to England at that favoured period, as at this.""", ] self.assertEqual(lowercase , lowercase ) A_ : Tuple = ["""It was the best of times."""] self.assertEqual(lowercase , lowercase ) def _a (self ): A_ : Dict = torch.tensor([1, 2, 3, 4] ) A_ : Union[str, Any] = torch.tensor([1, 1, 1, 1] ) np.testing.assert_array_equal(build_mask(lowercase , 0 ).numpy() , expected.numpy() ) def _a (self ): A_ : Union[str, Any] = torch.tensor([1, 2, 3, 4, 23, 23, 23] ) A_ : List[str] = torch.tensor([1, 1, 1, 1, 0, 0, 0] ) np.testing.assert_array_equal(build_mask(lowercase , 23 ).numpy() , expected.numpy() ) def _a (self ): A_ : Optional[Any] = torch.tensor([8, 2, 3, 4, 1, 1, 1] ) A_ : Tuple = torch.tensor([1, 1, 1, 1, 0, 0, 0] ) np.testing.assert_array_equal(build_mask(lowercase , 1 ).numpy() , expected.numpy() ) def _a (self ): A_ : List[str] = 101 A_ : str = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] ) A_ : int = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] ) A_ : Optional[Any] = compute_token_type_ids(lowercase , lowercase ) np.testing.assert_array_equal(lowercase , lowercase )

import argparse from collections import OrderedDict from pathlib import Path import requests import torch from PIL import Image from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor from transformers.utils import logging logging.set_verbosity_info() _UpperCAmelCase : Dict = logging.get_logger(__name__) def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> List[str]: lowerCamelCase__ : str = OrderedDict() for key, value in state_dict.items(): if key.startswith('module.encoder' ): lowerCamelCase__ : Optional[Any] = key.replace('module.encoder' , 'glpn.encoder' ) if key.startswith('module.decoder' ): lowerCamelCase__ : List[str] = key.replace('module.decoder' , 'decoder.stages' ) if "patch_embed" in key: # replace for example patch_embed1 by patch_embeddings.0 lowerCamelCase__ : Dict = key[key.find('patch_embed' ) + len('patch_embed' )] lowerCamelCase__ : Tuple = key.replace(F"""patch_embed{idx}""" , F"""patch_embeddings.{int(_UpperCAmelCase )-1}""" ) if "norm" in key: lowerCamelCase__ : str = key.replace('norm' , 'layer_norm' ) if "glpn.encoder.layer_norm" in key: # replace for example layer_norm1 by layer_norm.0 lowerCamelCase__ : Dict = key[key.find('glpn.encoder.layer_norm' ) + len('glpn.encoder.layer_norm' )] lowerCamelCase__ : str = key.replace(F"""layer_norm{idx}""" , F"""layer_norm.{int(_UpperCAmelCase )-1}""" ) if "layer_norm1" in key: lowerCamelCase__ : Optional[int] = key.replace('layer_norm1' , 'layer_norm_1' ) if "layer_norm2" in key: lowerCamelCase__ : Optional[int] = key.replace('layer_norm2' , 'layer_norm_2' ) if "block" in key: # replace for example block1 by block.0 lowerCamelCase__ : List[Any] = key[key.find('block' ) + len('block' )] lowerCamelCase__ : int = key.replace(F"""block{idx}""" , F"""block.{int(_UpperCAmelCase )-1}""" ) if "attn.q" in key: lowerCamelCase__ : Union[str, Any] = key.replace('attn.q' , 'attention.self.query' ) if "attn.proj" in key: lowerCamelCase__ : Union[str, Any] = key.replace('attn.proj' , 'attention.output.dense' ) if "attn" in key: lowerCamelCase__ : Dict = key.replace('attn' , 'attention.self' ) if "fc1" in key: lowerCamelCase__ : Dict = key.replace('fc1' , 'dense1' ) if "fc2" in key: lowerCamelCase__ : Any = key.replace('fc2' , 'dense2' ) if "linear_pred" in key: lowerCamelCase__ : Dict = key.replace('linear_pred' , 'classifier' ) if "linear_fuse" in key: lowerCamelCase__ : Tuple = key.replace('linear_fuse.conv' , 'linear_fuse' ) lowerCamelCase__ : List[str] = key.replace('linear_fuse.bn' , 'batch_norm' ) if "linear_c" in key: # replace for example linear_c4 by linear_c.3 lowerCamelCase__ : Optional[Any] = key[key.find('linear_c' ) + len('linear_c' )] lowerCamelCase__ : Dict = key.replace(F"""linear_c{idx}""" , F"""linear_c.{int(_UpperCAmelCase )-1}""" ) if "bot_conv" in key: lowerCamelCase__ : str = key.replace('bot_conv' , '0.convolution' ) if "skip_conv1" in key: lowerCamelCase__ : Union[str, Any] = key.replace('skip_conv1' , '1.convolution' ) if "skip_conv2" in key: lowerCamelCase__ : List[Any] = key.replace('skip_conv2' , '2.convolution' ) if "fusion1" in key: lowerCamelCase__ : Optional[int] = key.replace('fusion1' , '1.fusion' ) if "fusion2" in key: lowerCamelCase__ : Union[str, Any] = key.replace('fusion2' , '2.fusion' ) if "fusion3" in key: lowerCamelCase__ : List[Any] = key.replace('fusion3' , '3.fusion' ) if "fusion" in key and "conv" in key: lowerCamelCase__ : str = key.replace('conv' , 'convolutional_layer' ) if key.startswith('module.last_layer_depth' ): lowerCamelCase__ : Dict = key.replace('module.last_layer_depth' , 'head.head' ) lowerCamelCase__ : str = value return new_state_dict def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]: # for each of the encoder blocks: for i in range(config.num_encoder_blocks ): for j in range(config.depths[i] ): # read in weights + bias of keys and values (which is a single matrix in the original implementation) lowerCamelCase__ : Any = state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" ) lowerCamelCase__ : Optional[Any] = state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" ) # next, add keys and values (in that order) to the state dict lowerCamelCase__ : Optional[int] = kv_weight[ : config.hidden_sizes[i], : ] lowerCamelCase__ : Optional[int] = kv_bias[: config.hidden_sizes[i]] lowerCamelCase__ : Any = kv_weight[ config.hidden_sizes[i] :, : ] lowerCamelCase__ : Dict = kv_bias[config.hidden_sizes[i] :] def SCREAMING_SNAKE_CASE ( ) -> str: lowerCamelCase__ : List[str] = 'http://images.cocodataset.org/val2017/000000039769.jpg' lowerCamelCase__ : Tuple = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ) return image @torch.no_grad() def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=None ) -> Optional[int]: lowerCamelCase__ : str = GLPNConfig(hidden_sizes=[64, 128, 320, 512] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] ) # load image processor (only resize + rescale) lowerCamelCase__ : Union[str, Any] = GLPNImageProcessor() # prepare image lowerCamelCase__ : str = prepare_img() lowerCamelCase__ : Tuple = image_processor(images=_UpperCAmelCase , return_tensors='pt' ).pixel_values logger.info('Converting model...' ) # load original state dict lowerCamelCase__ : Any = torch.load(_UpperCAmelCase , map_location=torch.device('cpu' ) ) # rename keys lowerCamelCase__ : str = rename_keys(_UpperCAmelCase ) # key and value matrices need special treatment read_in_k_v(_UpperCAmelCase , _UpperCAmelCase ) # create HuggingFace model and load state dict lowerCamelCase__ : Dict = GLPNForDepthEstimation(_UpperCAmelCase ) model.load_state_dict(_UpperCAmelCase ) model.eval() # forward pass lowerCamelCase__ : List[str] = model(_UpperCAmelCase ) lowerCamelCase__ : Tuple = outputs.predicted_depth # verify output if model_name is not None: if "nyu" in model_name: lowerCamelCase__ : List[Any] = torch.tensor( [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]] ) elif "kitti" in model_name: lowerCamelCase__ : List[str] = torch.tensor( [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]] ) else: raise ValueError(F"""Unknown model name: {model_name}""" ) lowerCamelCase__ : Tuple = torch.Size([1, 480, 640] ) assert predicted_depth.shape == expected_shape assert torch.allclose(predicted_depth[0, :3, :3] , _UpperCAmelCase , atol=1e-4 ) print('Looks ok!' ) # finally, push to hub if required if push_to_hub: logger.info('Pushing model and image processor to the hub...' ) model.push_to_hub( repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization='nielsr' , commit_message='Add model' , use_temp_dir=_UpperCAmelCase , ) image_processor.push_to_hub( repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization='nielsr' , commit_message='Add image processor' , use_temp_dir=_UpperCAmelCase , ) if __name__ == "__main__": _UpperCAmelCase : Tuple = argparse.ArgumentParser() parser.add_argument( """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file).""", ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model.""" ) parser.add_argument( """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub.""" ) parser.add_argument( """--model_name""", default="""glpn-kitti""", type=str, help="""Name of the model in case you're pushing to the hub.""", ) _UpperCAmelCase : int = parser.parse_args() convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

'''simple docstring''' import argparse import json from dataclasses import dataclass, field from functools import partial from pathlib import Path from typing import List import timm import torch import torch.nn as nn from huggingface_hub import hf_hub_download from torch import Tensor from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification from transformers.utils import logging logging.set_verbosity_info() _lowerCAmelCase = logging.get_logger() @dataclass class A : '''simple docstring''' A = 42 A = field(default_factory=SCREAMING_SNAKE_CASE__ ) A = field(default_factory=SCREAMING_SNAKE_CASE__ ) def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]: __UpperCamelCase : str = len(list(m.modules() ) ) == 1 or isinstance(_UpperCAmelCase , nn.Convad ) or isinstance(_UpperCAmelCase , nn.BatchNormad ) if has_not_submodules: self.traced.append(_UpperCAmelCase ) def __call__(self , _UpperCAmelCase ) -> Optional[int]: for m in self.module.modules(): self.handles.append(m.register_forward_hook(self._forward_hook ) ) self.module(_UpperCAmelCase ) [x.remove() for x in self.handles] return self @property def a_ (self ) -> Tuple: # check the len of the state_dict keys to see if we have learnable params return list(filter(lambda _UpperCAmelCase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) ) @dataclass class A : '''simple docstring''' A = 42 A = 42 A = 0 A = field(default_factory=SCREAMING_SNAKE_CASE__ ) A = field(default_factory=SCREAMING_SNAKE_CASE__ ) def __call__(self , _UpperCAmelCase ) -> Any: __UpperCamelCase : List[str] = Tracker(self.dest )(_UpperCAmelCase ).parametrized __UpperCamelCase : List[Any] = Tracker(self.src )(_UpperCAmelCase ).parametrized __UpperCamelCase : Optional[int] = list(filter(lambda _UpperCAmelCase : type(_UpperCAmelCase ) not in self.src_skip , _UpperCAmelCase ) ) __UpperCamelCase : List[Any] = list(filter(lambda _UpperCAmelCase : type(_UpperCAmelCase ) not in self.dest_skip , _UpperCAmelCase ) ) if len(_UpperCAmelCase ) != len(_UpperCAmelCase ): raise Exception( f"Numbers of operations are different. Source module has {len(_UpperCAmelCase )} operations while" f" destination module has {len(_UpperCAmelCase )}." ) for dest_m, src_m in zip(_UpperCAmelCase , _UpperCAmelCase ): dest_m.load_state_dict(src_m.state_dict() ) if self.verbose == 1: print(f"Transfered from={src_m} to={dest_m}" ) def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = True ): print(F"Converting {name}..." ) with torch.no_grad(): __UpperCamelCase : int = timm.create_model(snake_case__ , pretrained=snake_case__ ).eval() __UpperCamelCase : Union[str, Any] = ResNetForImageClassification(snake_case__ ).eval() __UpperCamelCase : Tuple = ModuleTransfer(src=snake_case__ , dest=snake_case__ ) __UpperCamelCase : List[Any] = torch.randn((1, 3, 224, 224) ) module_transfer(snake_case__ ) assert torch.allclose(from_model(snake_case__ ) , our_model(snake_case__ ).logits ), "The model logits don't match the original one." __UpperCamelCase : Any = F"resnet{'-'.join(name.split('resnet' ) )}" print(snake_case__ ) if push_to_hub: our_model.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message="Add model" , use_temp_dir=snake_case__ , ) # we can use the convnext one __UpperCamelCase : Union[str, Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" ) image_processor.push_to_hub( repo_path_or_name=save_directory / checkpoint_name , commit_message="Add image processor" , use_temp_dir=snake_case__ , ) print(F"Pushed {checkpoint_name}" ) def __lowerCAmelCase ( snake_case__ , snake_case__ = None , snake_case__ = True ): __UpperCamelCase : str = "imagenet-1k-id2label.json" __UpperCamelCase : Any = 1_000 __UpperCamelCase : List[str] = (1, num_labels) __UpperCamelCase : List[str] = "huggingface/label-files" __UpperCamelCase : str = num_labels __UpperCamelCase : str = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type="dataset" ) , "r" ) ) __UpperCamelCase : List[str] = {int(snake_case__ ): v for k, v in idalabel.items()} __UpperCamelCase : Any = idalabel __UpperCamelCase : Optional[int] = {v: k for k, v in idalabel.items()} __UpperCamelCase : Tuple = partial(snake_case__ , num_labels=snake_case__ , idalabel=snake_case__ , labelaid=snake_case__ ) __UpperCamelCase : Dict = { "resnet18": ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ), "resnet26": ImageNetPreTrainedConfig( depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ), "resnet34": ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ), "resnet50": ImageNetPreTrainedConfig( depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ), "resnet101": ImageNetPreTrainedConfig( depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ), "resnet152": ImageNetPreTrainedConfig( depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ), } if model_name: convert_weight_and_push(snake_case__ , names_to_config[model_name] , snake_case__ , snake_case__ ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(snake_case__ , snake_case__ , snake_case__ , snake_case__ ) return config, expected_shape if __name__ == "__main__": _lowerCAmelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default=None, type=str, help=( '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,''' ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.''' ), ) parser.add_argument( '''--pytorch_dump_folder_path''', default=None, type=Path, required=True, help='''Path to the output PyTorch model directory.''', ) parser.add_argument( '''--push_to_hub''', default=True, type=bool, required=False, help='''If True, push model and image processor to the hub.''', ) _lowerCAmelCase = parser.parse_args() _lowerCAmelCase = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

import json from typing import Iterator, List, Union from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers from tokenizers.implementations.base_tokenizer import BaseTokenizer from tokenizers.models import Unigram from tokenizers.processors import TemplateProcessing class __snake_case ( lowerCamelCase__ ): def __init__( self , snake_case__ = "▁" , snake_case__ = True , snake_case__ = "<unk>" , snake_case__ = "</s>" , snake_case__ = "<pad>" , ) -> int: '''simple docstring''' UpperCAmelCase : Dict ={ '''pad''': {'''id''': 0, '''token''': pad_token}, '''eos''': {'''id''': 1, '''token''': eos_token}, '''unk''': {'''id''': 2, '''token''': unk_token}, } UpperCAmelCase : Optional[int] =[None] * len(self.special_tokens ) for token_dict in self.special_tokens.values(): UpperCAmelCase : Tuple =token_dict['''token'''] UpperCAmelCase : Any =Tokenizer(Unigram() ) UpperCAmelCase : Tuple =normalizers.Sequence( [ normalizers.Nmt(), normalizers.NFKC(), normalizers.Replace(Regex(''' {2,}''' ) , ''' ''' ), normalizers.Lowercase(), ] ) UpperCAmelCase : Dict =pre_tokenizers.Sequence( [ pre_tokenizers.Metaspace(replacement=snake_case__ , add_prefix_space=snake_case__ ), pre_tokenizers.Digits(individual_digits=snake_case__ ), pre_tokenizers.Punctuation(), ] ) UpperCAmelCase : int =decoders.Metaspace(replacement=snake_case__ , add_prefix_space=snake_case__ ) UpperCAmelCase : List[Any] =TemplateProcessing( single=f'''$A {self.special_tokens['eos']['token']}''' , special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])] , ) UpperCAmelCase : Dict ={ '''model''': '''SentencePieceUnigram''', '''replacement''': replacement, '''add_prefix_space''': add_prefix_space, } super().__init__(snake_case__ , snake_case__ ) def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = 8000 , snake_case__ = True , ) -> List[str]: '''simple docstring''' UpperCAmelCase : Dict =trainers.UnigramTrainer( vocab_size=snake_case__ , special_tokens=self.special_tokens_list , show_progress=snake_case__ , ) if isinstance(snake_case__ , snake_case__ ): UpperCAmelCase : Union[str, Any] =[files] self._tokenizer.train(snake_case__ , trainer=snake_case__ ) self.add_unk_id() def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = 8000 , snake_case__ = True , ) -> Any: '''simple docstring''' UpperCAmelCase : Tuple =trainers.UnigramTrainer( vocab_size=snake_case__ , special_tokens=self.special_tokens_list , show_progress=snake_case__ , ) self._tokenizer.train_from_iterator(snake_case__ , trainer=snake_case__ ) self.add_unk_id() def UpperCAmelCase__ ( self ) -> List[Any]: '''simple docstring''' UpperCAmelCase : str =json.loads(self._tokenizer.to_str() ) UpperCAmelCase : Optional[Any] =self.special_tokens['''unk''']['''id'''] UpperCAmelCase : Tuple =Tokenizer.from_str(json.dumps(snake_case__ ) )

from __future__ import annotations def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> list[list[int]]: '''simple docstring''' UpperCAmelCase : list[list[int]] =[] create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , [] , __lowerCAmelCase ) return result def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )-> None: '''simple docstring''' if level == 0: total_list.append(current_list[:] ) return for i in range(__lowerCAmelCase , total_number - level + 2 ): current_list.append(__lowerCAmelCase ) create_all_state(i + 1 , __lowerCAmelCase , level - 1 , __lowerCAmelCase , __lowerCAmelCase ) current_list.pop() def lowerCAmelCase_ ( __lowerCAmelCase )-> None: '''simple docstring''' for i in total_list: print(*__lowerCAmelCase ) if __name__ == "__main__": __snake_case = 4 __snake_case = 2 __snake_case = generate_all_combinations(n, k) print_all_state(total_list)

"""simple docstring""" import io import math from typing import Dict, Optional, Union import numpy as np from huggingface_hub import hf_hub_download from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image from ...image_utils import ( ChannelDimension, ImageInput, get_image_size, infer_channel_dimension_format, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_torch_available, is_vision_available, logging from ...utils.import_utils import requires_backends if is_vision_available(): import textwrap from PIL import Image, ImageDraw, ImageFont if is_torch_available(): import torch from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11 else: snake_case_ = False snake_case_ = logging.get_logger(__name__) snake_case_ = """ybelkada/fonts""" def _lowerCAmelCase ( ): if is_torch_available() and not is_torch_greater_or_equal_than_1_11: raise ImportError( F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """ 'Pix2StructImageProcessor. Please upgrade torch.' ) def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ): requires_backends(lowercase_ , ['torch'] ) _check_torch_version() UpperCAmelCase = image_tensor.unsqueeze(0 ) UpperCAmelCase = torch.nn.functional.unfold(lowercase_ , (patch_height, patch_width) , stride=(patch_height, patch_width) ) UpperCAmelCase = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , lowercase_ , lowercase_ , -1 ) UpperCAmelCase = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape( image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , ) return patches.unsqueeze(0 ) def _lowerCAmelCase ( lowercase_ , lowercase_ = 36 , lowercase_ = "black" , lowercase_ = "white" , lowercase_ = 5 , lowercase_ = 5 , lowercase_ = 5 , lowercase_ = 5 , lowercase_ = None , lowercase_ = None , ): requires_backends(lowercase_ , 'vision' ) # Add new lines so that each line is no more than 80 characters. UpperCAmelCase = textwrap.TextWrapper(width=80 ) UpperCAmelCase = wrapper.wrap(text=lowercase_ ) UpperCAmelCase = '\n'.join(lowercase_ ) if font_bytes is not None and font_path is None: UpperCAmelCase = io.BytesIO(lowercase_ ) elif font_path is not None: UpperCAmelCase = font_path else: UpperCAmelCase = hf_hub_download(lowercase_ , 'Arial.TTF' ) UpperCAmelCase = ImageFont.truetype(lowercase_ , encoding='UTF-8' , size=lowercase_ ) # Use a temporary canvas to determine the width and height in pixels when # rendering the text. UpperCAmelCase = ImageDraw.Draw(Image.new('RGB' , (1, 1) , lowercase_ ) ) UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = temp_draw.textbbox((0, 0) , lowercase_ , lowercase_ ) # Create the actual image with a bit of padding around the text. UpperCAmelCase = text_width + left_padding + right_padding UpperCAmelCase = text_height + top_padding + bottom_padding UpperCAmelCase = Image.new('RGB' , (image_width, image_height) , lowercase_ ) UpperCAmelCase = ImageDraw.Draw(lowercase_ ) draw.text(xy=(left_padding, top_padding) , text=lowercase_ , fill=lowercase_ , font=lowercase_ ) return image def _lowerCAmelCase ( lowercase_ , lowercase_ , **lowercase_ ): requires_backends(lowercase_ , 'vision' ) # Convert to PIL image if necessary UpperCAmelCase = to_pil_image(lowercase_ ) UpperCAmelCase = render_text(lowercase_ , **lowercase_ ) UpperCAmelCase = max(header_image.width , image.width ) UpperCAmelCase = int(image.height * (new_width / image.width) ) UpperCAmelCase = int(header_image.height * (new_width / header_image.width) ) UpperCAmelCase = Image.new('RGB' , (new_width, new_height + new_header_height) , 'white' ) new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) ) new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) ) # Convert back to the original framework if necessary UpperCAmelCase = to_numpy_array(lowercase_ ) if infer_channel_dimension_format(lowercase_ ) == ChannelDimension.LAST: UpperCAmelCase = to_channel_dimension_format(lowercase_ , ChannelDimension.LAST ) return new_image class A_ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __UpperCamelCase = ["""flattened_patches"""] def __init__( self :str , lowercase_ :bool = True , lowercase_ :bool = True , lowercase_ :Dict[str, int] = None , lowercase_ :int = 20_48 , lowercase_ :bool = False , **lowercase_ :Optional[Any] , ) -> None: super().__init__(**lowercase_ ) UpperCAmelCase = patch_size if patch_size is not None else {'height': 16, 'width': 16} UpperCAmelCase = do_normalize UpperCAmelCase = do_convert_rgb UpperCAmelCase = max_patches UpperCAmelCase = is_vqa def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :np.ndarray , lowercase_ :int , lowercase_ :dict , **lowercase_ :str ) -> np.ndarray: requires_backends(self.extract_flattened_patches , 'torch' ) _check_torch_version() # convert to torch UpperCAmelCase = to_channel_dimension_format(lowercase_ , ChannelDimension.FIRST ) UpperCAmelCase = torch.from_numpy(lowercase_ ) UpperCAmelCase , UpperCAmelCase = patch_size['height'], patch_size['width'] UpperCAmelCase , UpperCAmelCase = get_image_size(lowercase_ ) # maximize scale s.t. UpperCAmelCase = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) ) UpperCAmelCase = max(min(math.floor(scale * image_height / patch_height ) , lowercase_ ) , 1 ) UpperCAmelCase = max(min(math.floor(scale * image_width / patch_width ) , lowercase_ ) , 1 ) UpperCAmelCase = max(num_feasible_rows * patch_height , 1 ) UpperCAmelCase = max(num_feasible_cols * patch_width , 1 ) UpperCAmelCase = torch.nn.functional.interpolate( image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode='bilinear' , align_corners=lowercase_ , antialias=lowercase_ , ).squeeze(0 ) # [1, rows, columns, patch_height * patch_width * image_channels] UpperCAmelCase = torch_extract_patches(lowercase_ , lowercase_ , lowercase_ ) UpperCAmelCase = patches.shape UpperCAmelCase = patches_shape[1] UpperCAmelCase = patches_shape[2] UpperCAmelCase = patches_shape[3] # [rows * columns, patch_height * patch_width * image_channels] UpperCAmelCase = patches.reshape([rows * columns, depth] ) # [rows * columns, 1] UpperCAmelCase = torch.arange(lowercase_ ).reshape([rows, 1] ).repeat(1 , lowercase_ ).reshape([rows * columns, 1] ) UpperCAmelCase = torch.arange(lowercase_ ).reshape([1, columns] ).repeat(lowercase_ , 1 ).reshape([rows * columns, 1] ) # Offset by 1 so the ids do not contain zeros, which represent padding. row_ids += 1 col_ids += 1 # Prepare additional patch features. # [rows * columns, 1] UpperCAmelCase = row_ids.to(torch.floataa ) UpperCAmelCase = col_ids.to(torch.floataa ) # [rows * columns, 2 + patch_height * patch_width * image_channels] UpperCAmelCase = torch.cat([row_ids, col_ids, patches] , -1 ) # [max_patches, 2 + patch_height * patch_width * image_channels] UpperCAmelCase = torch.nn.functional.pad(lowercase_ , [0, 0, 0, max_patches - (rows * columns)] ).float() UpperCAmelCase = to_numpy_array(lowercase_ ) return result def UpperCAmelCase__ ( self :int , lowercase_ :np.ndarray , lowercase_ :Optional[Union[str, ChannelDimension]] = None , **lowercase_ :Any ) -> np.ndarray: if image.dtype == np.uinta: UpperCAmelCase = image.astype(np.floataa ) # take mean across the whole `image` UpperCAmelCase = np.mean(lowercase_ ) UpperCAmelCase = np.std(lowercase_ ) UpperCAmelCase = max(lowercase_ , 1.0 / math.sqrt(np.prod(image.shape ) ) ) return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , **lowercase_ ) def UpperCAmelCase__ ( self :Dict , lowercase_ :ImageInput , lowercase_ :Optional[str] = None , lowercase_ :bool = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[int] = None , lowercase_ :Optional[Dict[str, int]] = None , lowercase_ :Optional[Union[str, TensorType]] = None , lowercase_ :ChannelDimension = ChannelDimension.FIRST , **lowercase_ :List[str] , ) -> ImageInput: UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize UpperCAmelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb UpperCAmelCase = patch_size if patch_size is not None else self.patch_size UpperCAmelCase = max_patches if max_patches is not None else self.max_patches UpperCAmelCase = self.is_vqa if kwargs.get('data_format' , lowercase_ ) is not None: raise ValueError('data_format is not an accepted input as the outputs are ' ) UpperCAmelCase = make_list_of_images(lowercase_ ) if not valid_images(lowercase_ ): raise ValueError( 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, ' 'torch.Tensor, tf.Tensor or jax.ndarray.' ) # PIL RGBA images are converted to RGB if do_convert_rgb: UpperCAmelCase = [convert_to_rgb(lowercase_ ) for image in images] # All transformations expect numpy arrays. UpperCAmelCase = [to_numpy_array(lowercase_ ) for image in images] if is_vqa: if header_text is None: raise ValueError('A header text must be provided for VQA models.' ) UpperCAmelCase = kwargs.pop('font_bytes' , lowercase_ ) UpperCAmelCase = kwargs.pop('font_path' , lowercase_ ) if isinstance(lowercase_ , lowercase_ ): UpperCAmelCase = [header_text] * len(lowercase_ ) UpperCAmelCase = [ render_header(lowercase_ , header_text[i] , font_bytes=lowercase_ , font_path=lowercase_ ) for i, image in enumerate(lowercase_ ) ] if do_normalize: UpperCAmelCase = [self.normalize(image=lowercase_ ) for image in images] # convert to torch tensor and permute UpperCAmelCase = [ self.extract_flattened_patches(image=lowercase_ , max_patches=lowercase_ , patch_size=lowercase_ ) for image in images ] # create attention mask in numpy UpperCAmelCase = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images] UpperCAmelCase = BatchFeature( data={'flattened_patches': images, 'attention_mask': attention_masks} , tensor_type=lowercase_ ) return encoded_outputs

"""simple docstring""" import warnings from typing import List, Optional, Union from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class A_ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __UpperCamelCase = ["""image_processor""", """tokenizer"""] __UpperCamelCase = """LayoutLMv2ImageProcessor""" __UpperCamelCase = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""") def __init__( self :Any , lowercase_ :int=None , lowercase_ :Union[str, Any]=None , **lowercase_ :Optional[Any] ) -> Dict: if "feature_extractor" in kwargs: warnings.warn( 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`' ' instead.' , lowercase_ , ) UpperCAmelCase = kwargs.pop('feature_extractor' ) UpperCAmelCase = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError('You need to specify an `image_processor`.' ) if tokenizer is None: raise ValueError('You need to specify a `tokenizer`.' ) super().__init__(lowercase_ , lowercase_ ) def __call__( self :str , lowercase_ :Optional[int] , lowercase_ :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase_ :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowercase_ :Union[List[List[int]], List[List[List[int]]]] = None , lowercase_ :Optional[Union[List[int], List[List[int]]]] = None , lowercase_ :bool = True , lowercase_ :Union[bool, str, PaddingStrategy] = False , lowercase_ :Union[bool, str, TruncationStrategy] = None , lowercase_ :Optional[int] = None , lowercase_ :int = 0 , lowercase_ :Optional[int] = None , lowercase_ :Optional[bool] = None , lowercase_ :Optional[bool] = None , lowercase_ :bool = False , lowercase_ :bool = False , lowercase_ :bool = False , lowercase_ :bool = False , lowercase_ :bool = True , lowercase_ :Optional[Union[str, TensorType]] = None , **lowercase_ :Any , ) -> BatchEncoding: # verify input if self.image_processor.apply_ocr and (boxes is not None): raise ValueError( 'You cannot provide bounding boxes ' 'if you initialized the image processor with apply_ocr set to True.' ) if self.image_processor.apply_ocr and (word_labels is not None): raise ValueError( 'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.' ) if return_overflowing_tokens is True and return_offsets_mapping is False: raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.' ) # first, apply the image processor UpperCAmelCase = self.image_processor(images=lowercase_ , return_tensors=lowercase_ ) # second, apply the tokenizer if text is not None and self.image_processor.apply_ocr and text_pair is None: if isinstance(lowercase_ , lowercase_ ): UpperCAmelCase = [text] # add batch dimension (as the image processor always adds a batch dimension) UpperCAmelCase = features['words'] UpperCAmelCase = self.tokenizer( text=text if text is not None else features['words'] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features['boxes'] , word_labels=lowercase_ , add_special_tokens=lowercase_ , padding=lowercase_ , truncation=lowercase_ , max_length=lowercase_ , stride=lowercase_ , pad_to_multiple_of=lowercase_ , return_token_type_ids=lowercase_ , return_attention_mask=lowercase_ , return_overflowing_tokens=lowercase_ , return_special_tokens_mask=lowercase_ , return_offsets_mapping=lowercase_ , return_length=lowercase_ , verbose=lowercase_ , return_tensors=lowercase_ , **lowercase_ , ) # add pixel values UpperCAmelCase = features.pop('pixel_values' ) if return_overflowing_tokens is True: UpperCAmelCase = self.get_overflowing_images(lowercase_ , encoded_inputs['overflow_to_sample_mapping'] ) UpperCAmelCase = images return encoded_inputs def UpperCAmelCase__ ( self :Dict , lowercase_ :List[Any] , lowercase_ :Any ) -> Optional[Any]: # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image UpperCAmelCase = [] for sample_idx in overflow_to_sample_mapping: images_with_overflow.append(images[sample_idx] ) if len(lowercase_ ) != len(lowercase_ ): raise ValueError( 'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got' f""" {len(lowercase_ )} and {len(lowercase_ )}""" ) return images_with_overflow def UpperCAmelCase__ ( self :Any , *lowercase_ :int , **lowercase_ :Tuple ) -> Tuple: return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ ) def UpperCAmelCase__ ( self :Any , *lowercase_ :List[Any] , **lowercase_ :Optional[int] ) -> Optional[Any]: return self.tokenizer.decode(*lowercase_ , **lowercase_ ) @property def UpperCAmelCase__ ( self :int ) -> Optional[int]: return ["input_ids", "bbox", "attention_mask", "image"] @property def UpperCAmelCase__ ( self :int ) -> Dict: warnings.warn( '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase_ , ) return self.image_processor_class @property def UpperCAmelCase__ ( self :Union[str, Any] ) -> Optional[int]: warnings.warn( '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase_ , ) return self.image_processor

'''simple docstring''' import logging import os import threading import time try: import warnings except ImportError: __lowerCAmelCase = None try: import msvcrt except ImportError: __lowerCAmelCase = None try: import fcntl except ImportError: __lowerCAmelCase = None # Backward compatibility # ------------------------------------------------ try: TimeoutError except NameError: __lowerCAmelCase = OSError # Data # ------------------------------------------------ __lowerCAmelCase = [ """Timeout""", """BaseFileLock""", """WindowsFileLock""", """UnixFileLock""", """SoftFileLock""", """FileLock""", ] __lowerCAmelCase = """3.0.12""" __lowerCAmelCase = None def UpperCAmelCase_ (): """simple docstring""" global _logger _a : Dict = _logger or logging.getLogger(__name__ ) return _logger class UpperCAmelCase__ ( lowercase__ ): """simple docstring""" def __init__( self : Any ,_a : Optional[Any] ): '''simple docstring''' _a : Dict = lock_file return None def __str__( self : Any ): '''simple docstring''' _a : List[Any] = F"""The file lock '{self.lock_file}' could not be acquired.""" return temp class UpperCAmelCase__ : """simple docstring""" def __init__( self : Optional[int] ,_a : List[str] ): '''simple docstring''' _a : List[str] = lock return None def __enter__( self : Union[str, Any] ): '''simple docstring''' return self.lock def __exit__( self : Dict ,_a : List[Any] ,_a : Union[str, Any] ,_a : Optional[int] ): '''simple docstring''' self.lock.release() return None class UpperCAmelCase__ : """simple docstring""" def __init__( self : Union[str, Any] ,_a : Optional[Any] ,_a : int=-1 ,_a : str=None ): '''simple docstring''' _a : List[Any] = max_filename_length if max_filename_length is not None else 255 # Hash the filename if it's too long _a : Dict = self.hash_filename_if_too_long(_a ,_a ) # The path to the lock file. _a : List[Any] = lock_file # The file descriptor for the *_lock_file* as it is returned by the # os.open() function. # This file lock is only NOT None, if the object currently holds the # lock. _a : List[str] = None # The default timeout value. _a : List[str] = timeout # We use this lock primarily for the lock counter. _a : int = threading.Lock() # The lock counter is used for implementing the nested locking # mechanism. Whenever the lock is acquired, the counter is increased and # the lock is only released, when this value is 0 again. _a : str = 0 return None @property def __lowercase ( self : Optional[int] ): '''simple docstring''' return self._lock_file @property def __lowercase ( self : Union[str, Any] ): '''simple docstring''' return self._timeout @timeout.setter def __lowercase ( self : List[Any] ,_a : Any ): '''simple docstring''' _a : Union[str, Any] = float(_a ) return None def __lowercase ( self : Union[str, Any] ): '''simple docstring''' raise NotImplementedError() def __lowercase ( self : Union[str, Any] ): '''simple docstring''' raise NotImplementedError() @property def __lowercase ( self : Optional[Any] ): '''simple docstring''' return self._lock_file_fd is not None def __lowercase ( self : str ,_a : Optional[int]=None ,_a : Optional[Any]=0.05 ): '''simple docstring''' if timeout is None: _a : str = self.timeout # Increment the number right at the beginning. # We can still undo it, if something fails. with self._thread_lock: self._lock_counter += 1 _a : str = id(self ) _a : int = self._lock_file _a : Dict = time.time() try: while True: with self._thread_lock: if not self.is_locked: logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" ) self._acquire() if self.is_locked: logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" ) break elif timeout >= 0 and time.time() - start_time > timeout: logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" ) raise Timeout(self._lock_file ) else: logger().debug( F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" ) time.sleep(_a ) except: # noqa # Something did go wrong, so decrement the counter. with self._thread_lock: _a : Tuple = max(0 ,self._lock_counter - 1 ) raise return _Acquire_ReturnProxy(lock=self ) def __lowercase ( self : Optional[int] ,_a : Tuple=False ): '''simple docstring''' with self._thread_lock: if self.is_locked: self._lock_counter -= 1 if self._lock_counter == 0 or force: _a : int = id(self ) _a : Union[str, Any] = self._lock_file logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" ) self._release() _a : Optional[Any] = 0 logger().debug(F"""Lock {lock_id} released on {lock_filename}""" ) return None def __enter__( self : List[Any] ): '''simple docstring''' self.acquire() return self def __exit__( self : List[str] ,_a : str ,_a : Any ,_a : Any ): '''simple docstring''' self.release() return None def __del__( self : int ): '''simple docstring''' self.release(force=_a ) return None def __lowercase ( self : Optional[int] ,_a : str ,_a : int ): '''simple docstring''' _a : Any = os.path.basename(_a ) if len(_a ) > max_length and max_length > 0: _a : List[str] = os.path.dirname(_a ) _a : Optional[Any] = str(hash(_a ) ) _a : List[str] = filename[: max_length - len(_a ) - 8] + '...' + hashed_filename + '.lock' return os.path.join(_a ,_a ) else: return path class UpperCAmelCase__ ( lowercase__ ): """simple docstring""" def __init__( self : int ,_a : Optional[Any] ,_a : List[str]=-1 ,_a : int=None ): '''simple docstring''' from .file_utils import relative_to_absolute_path super().__init__(_a ,timeout=_a ,max_filename_length=_a ) _a : Any = '\\\\?\\' + relative_to_absolute_path(self.lock_file ) def __lowercase ( self : Optional[int] ): '''simple docstring''' _a : List[Any] = os.O_RDWR | os.O_CREAT | os.O_TRUNC try: _a : int = os.open(self._lock_file ,_a ) except OSError: pass else: try: msvcrt.locking(_a ,msvcrt.LK_NBLCK ,1 ) except OSError: os.close(_a ) else: _a : List[Any] = fd return None def __lowercase ( self : Optional[int] ): '''simple docstring''' _a : Optional[int] = self._lock_file_fd _a : Any = None msvcrt.locking(_a ,msvcrt.LK_UNLCK ,1 ) os.close(_a ) try: os.remove(self._lock_file ) # Probably another instance of the application # that acquired the file lock. except OSError: pass return None class UpperCAmelCase__ ( lowercase__ ): """simple docstring""" def __init__( self : int ,_a : Union[str, Any] ,_a : List[str]=-1 ,_a : Any=None ): '''simple docstring''' _a : Optional[int] = os.statvfs(os.path.dirname(_a ) ).f_namemax super().__init__(_a ,timeout=_a ,max_filename_length=_a ) def __lowercase ( self : List[Any] ): '''simple docstring''' _a : List[Any] = os.O_RDWR | os.O_CREAT | os.O_TRUNC _a : Optional[Any] = os.open(self._lock_file ,_a ) try: fcntl.flock(_a ,fcntl.LOCK_EX | fcntl.LOCK_NB ) except OSError: os.close(_a ) else: _a : Tuple = fd return None def __lowercase ( self : Dict ): '''simple docstring''' _a : Optional[int] = self._lock_file_fd _a : Union[str, Any] = None fcntl.flock(_a ,fcntl.LOCK_UN ) os.close(_a ) return None class UpperCAmelCase__ ( lowercase__ ): """simple docstring""" def __lowercase ( self : List[str] ): '''simple docstring''' _a : Dict = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC try: _a : List[Any] = os.open(self._lock_file ,_a ) except OSError: pass else: _a : List[str] = fd return None def __lowercase ( self : int ): '''simple docstring''' os.close(self._lock_file_fd ) _a : Tuple = None try: os.remove(self._lock_file ) # The file is already deleted and that's what we want. except OSError: pass return None __lowerCAmelCase = None if msvcrt: __lowerCAmelCase = WindowsFileLock elif fcntl: __lowerCAmelCase = UnixFileLock else: __lowerCAmelCase = SoftFileLock if warnings is not None: warnings.warn("""only soft file lock is available""")

'''simple docstring''' import json import os import unittest from transformers.models.blenderbot_small.tokenization_blenderbot_small import ( VOCAB_FILES_NAMES, BlenderbotSmallTokenizer, ) from ...test_tokenization_common import TokenizerTesterMixin class UpperCAmelCase__ ( lowercase__ , unittest.TestCase ): """simple docstring""" __UpperCAmelCase : Dict = BlenderbotSmallTokenizer __UpperCAmelCase : Tuple = False def __lowercase ( self : List[Any] ): '''simple docstring''' super().setUp() _a : List[str] = ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__'] _a : Tuple = dict(zip(_a ,range(len(_a ) ) ) ) _a : List[Any] = ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', ''] _a : List[Any] = {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'} _a : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['vocab_file'] ) _a : str = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['merges_file'] ) with open(self.vocab_file ,'w' ,encoding='utf-8' ) as fp: fp.write(json.dumps(_a ) + '\n' ) with open(self.merges_file ,'w' ,encoding='utf-8' ) as fp: fp.write('\n'.join(_a ) ) def __lowercase ( self : List[Any] ,**_a : int ): '''simple docstring''' kwargs.update(self.special_tokens_map ) return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname ,**_a ) def __lowercase ( self : Tuple ,_a : int ): '''simple docstring''' _a : Optional[Any] = 'adapt act apte' _a : Dict = 'adapt act apte' return input_text, output_text def __lowercase ( self : int ): '''simple docstring''' _a : Optional[int] = BlenderbotSmallTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map ) _a : Union[str, Any] = 'adapt act apte' _a : Dict = ['adapt', 'act', 'ap@@', 'te'] _a : Tuple = tokenizer.tokenize(_a ) self.assertListEqual(_a ,_a ) _a : List[str] = [tokenizer.bos_token] + tokens + [tokenizer.eos_token] _a : Dict = [0, 1, 2, 3, 4, 5] self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) ,_a ) def __lowercase ( self : Optional[int] ): '''simple docstring''' _a : str = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' ) assert tok('sam' ).input_ids == [1384] _a : Union[str, Any] = 'I am a small frog.' _a : int = tok([src_text] ,padding=_a ,truncation=_a )['input_ids'] _a : str = tok.batch_decode(_a ,skip_special_tokens=_a ,clean_up_tokenization_spaces=_a )[0] assert src_text != decoded # I wish it did! assert decoded == "i am a small frog ." def __lowercase ( self : Any ): '''simple docstring''' _a : Optional[int] = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M' ) _a : Union[str, Any] = 'I am a small frog .' _a : Optional[Any] = '.' _a : Optional[Any] = tok(_a )['input_ids'] _a : Union[str, Any] = tok(_a )['input_ids'] assert encoded[-1] == encoded_dot[0]

def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]: lowerCamelCase : Tuple = [0 for i in range(r + 1 )] # nc0 = 1 lowerCamelCase : Optional[int] = 1 for i in range(1 ,n + 1 ): # to compute current row from previous row. lowerCamelCase : List[str] = min(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) while j > 0: c[j] += c[j - 1] j -= 1 return c[r] print(binomial_coefficient(n=10, r=5))

"""simple docstring""" import sys from .dependency_versions_table import deps from .utils.versions import require_version, require_version_core # define which module versions we always want to check at run time # (usually the ones defined in `install_requires` in setup.py) # # order specific notes: # - tqdm must be checked before tokenizers __SCREAMING_SNAKE_CASE ="python tqdm regex requests packaging filelock numpy tokenizers".split() if sys.version_info < (3, 7): pkgs_to_check_at_runtime.append("dataclasses") if sys.version_info < (3, 8): pkgs_to_check_at_runtime.append("importlib_metadata") for pkg in pkgs_to_check_at_runtime: if pkg in deps: if pkg == "tokenizers": # must be loaded here, or else tqdm check may fail from .utils import is_tokenizers_available if not is_tokenizers_available(): continue # not required, check version only if installed require_version_core(deps[pkg]) else: raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py") def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any]=None ): require_version(deps[pkg] , __SCREAMING_SNAKE_CASE )

"""simple docstring""" from typing import Dict, List, Optional, Union import numpy as np from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy _lowerCAmelCase :str = logging.get_logger(__name__) class _UpperCAmelCase ( a ): '''simple docstring''' def __init__( self , A , A , A , **A ) -> Tuple: _UpperCAmelCase : Optional[Any] = feature_size _UpperCAmelCase : Optional[int] = sampling_rate _UpperCAmelCase : int = padding_value _UpperCAmelCase : Any = kwargs.pop('''padding_side''' , '''right''' ) _UpperCAmelCase : Tuple = kwargs.pop('''return_attention_mask''' , A ) super().__init__(**A ) def __lowerCAmelCase ( self , A , A = True , A = None , A = False , A = None , A = None , A = None , ) -> BatchFeature: # If we have a list of dicts, let's convert it in a dict of lists # We do this to allow using this method as a collate_fn function in PyTorch Dataloader if isinstance(A , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ): _UpperCAmelCase : int = { key: [example[key] for example in processed_features] for key in processed_features[0].keys() } # The model's main input name, usually `input_values`, has be passed for padding if self.model_input_names[0] not in processed_features: raise ValueError( '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`''' f' to this method that includes {self.model_input_names[0]}, but you provided' f' {list(processed_features.keys() )}' ) _UpperCAmelCase : Any = processed_features[self.model_input_names[0]] _UpperCAmelCase : str = ( return_attention_mask if return_attention_mask is not None else self.return_attention_mask ) if len(A ) == 0: if return_attention_mask: _UpperCAmelCase : Tuple = [] return processed_features # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays # and rebuild them afterwards if no return_tensors is specified # Note that we lose the specific device the tensor may be on for PyTorch _UpperCAmelCase : Dict = required_input[0] if isinstance(A , (list, tuple) ): # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element. _UpperCAmelCase : Dict = 0 while len(required_input[index] ) == 0: index += 1 if index < len(A ): _UpperCAmelCase : Optional[Any] = required_input[index][0] if return_tensors is None: if is_tf_tensor(A ): _UpperCAmelCase : int = '''tf''' elif is_torch_tensor(A ): _UpperCAmelCase : Optional[Any] = '''pt''' elif isinstance(A , (int, float, list, tuple, np.ndarray) ): _UpperCAmelCase : Dict = '''np''' else: raise ValueError( f'type of {first_element} unknown: {type(A )}. ' '''Should be one of a python, numpy, pytorch or tensorflow object.''' ) for key, value in processed_features.items(): if isinstance(value[0] , (int, float) ): _UpperCAmelCase : Dict = to_numpy(A ) else: _UpperCAmelCase : Union[str, Any] = [to_numpy(A ) for v in value] # Convert padding_strategy in PaddingStrategy _UpperCAmelCase : Optional[Any] = self._get_padding_strategies(padding=A , max_length=A ) _UpperCAmelCase : Tuple = processed_features[self.model_input_names[0]] _UpperCAmelCase : Union[str, Any] = len(A ) if not all(len(A ) == batch_size for v in processed_features.values() ): raise ValueError('''Some items in the output dictionary have a different batch size than others.''' ) _UpperCAmelCase : Tuple = [] for i in range(A ): _UpperCAmelCase : Dict = {k: v[i] for k, v in processed_features.items()} # truncation _UpperCAmelCase : Dict = self._truncate( A , max_length=A , pad_to_multiple_of=A , truncation=A , ) truncated_inputs.append(A ) if padding_strategy == PaddingStrategy.LONGEST: # make sure that `max_length` cannot be longer than the longest truncated length _UpperCAmelCase : Optional[Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs ) _UpperCAmelCase : Optional[Any] = PaddingStrategy.MAX_LENGTH _UpperCAmelCase : Any = {} for i in range(A ): # padding _UpperCAmelCase : List[Any] = self._pad( truncated_inputs[i] , max_length=A , padding_strategy=A , pad_to_multiple_of=A , return_attention_mask=A , ) for key, value in outputs.items(): if key not in batch_outputs: _UpperCAmelCase : Optional[int] = [] if value.dtype is np.dtype(np.floataa ): _UpperCAmelCase : Union[str, Any] = value.astype(np.floataa ) batch_outputs[key].append(A ) return BatchFeature(A , tensor_type=A ) def __lowerCAmelCase ( self , A , A = None , A = PaddingStrategy.DO_NOT_PAD , A = None , A = None , ) -> dict: _UpperCAmelCase : Optional[int] = processed_features[self.model_input_names[0]] if padding_strategy == PaddingStrategy.LONGEST: _UpperCAmelCase : int = len(A ) if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): _UpperCAmelCase : str = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of _UpperCAmelCase : str = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A ) < max_length if return_attention_mask and "attention_mask" not in processed_features: _UpperCAmelCase : Optional[int] = np.ones(len(A ) , dtype=np.intaa ) if needs_to_be_padded: _UpperCAmelCase : List[str] = max_length - len(A ) if self.padding_side == "right": if return_attention_mask: _UpperCAmelCase : Optional[int] = np.pad( processed_features['''attention_mask'''] , (0, difference) ) _UpperCAmelCase : Tuple = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference) _UpperCAmelCase : List[Any] = np.pad( A , A , '''constant''' , constant_values=self.padding_value ) elif self.padding_side == "left": if return_attention_mask: _UpperCAmelCase : Optional[Any] = np.pad( processed_features['''attention_mask'''] , (difference, 0) ) _UpperCAmelCase : Tuple = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0) _UpperCAmelCase : Optional[int] = np.pad( A , A , '''constant''' , constant_values=self.padding_value ) else: raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) ) return processed_features def __lowerCAmelCase ( self , A , A = None , A = None , A = None , ) -> Any: if not truncation: return processed_features elif truncation and max_length is None: raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' ) _UpperCAmelCase : List[str] = processed_features[self.model_input_names[0]] # find `max_length` that fits `pad_to_multiple_of` if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): _UpperCAmelCase : str = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of _UpperCAmelCase : Union[str, Any] = len(A ) > max_length if needs_to_be_truncated: _UpperCAmelCase : Optional[Any] = processed_features[self.model_input_names[0]][:max_length] if "attention_mask" in processed_features: _UpperCAmelCase : int = processed_features['''attention_mask'''][:max_length] return processed_features def __lowerCAmelCase ( self , A=False , A=None ) -> List[Any]: # Get padding strategy if padding is not False: if padding is True: _UpperCAmelCase : Dict = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch elif not isinstance(A , A ): _UpperCAmelCase : Tuple = PaddingStrategy(A ) elif isinstance(A , A ): _UpperCAmelCase : Optional[Any] = padding else: _UpperCAmelCase : Optional[int] = PaddingStrategy.DO_NOT_PAD # Set max length if needed if max_length is None: if padding_strategy == PaddingStrategy.MAX_LENGTH: raise ValueError( f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined' ) # Test if we have a padding value if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None): raise ValueError( '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use''' ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' ) return padding_strategy

"""simple docstring""" # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _lowerCAmelCase :Optional[Any] = { 'configuration_efficientnet': [ 'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'EfficientNetConfig', 'EfficientNetOnnxConfig', ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase :str = ['EfficientNetImageProcessor'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase :Any = [ 'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST', 'EfficientNetForImageClassification', 'EfficientNetModel', 'EfficientNetPreTrainedModel', ] if TYPE_CHECKING: from .configuration_efficientnet import ( EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientNetConfig, EfficientNetOnnxConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_efficientnet import EfficientNetImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_efficientnet import ( EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST, EfficientNetForImageClassification, EfficientNetModel, EfficientNetPreTrainedModel, ) else: import sys _lowerCAmelCase :Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure)

'''simple docstring''' import argparse import json import numpy import torch from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging logging.set_verbosity_info() def UpperCamelCase_( snake_case : Optional[int] , snake_case : Dict ): '''simple docstring''' snake_case_ = torch.load(snake_case , map_location="cpu" ) snake_case_ = chkpt["model"] # We have the base model one level deeper than the original XLM repository snake_case_ = {} for k, v in state_dict.items(): if "pred_layer" in k: snake_case_ = v else: snake_case_ = v snake_case_ = chkpt["params"] snake_case_ = {n: v for n, v in config.items() if not isinstance(snake_case , (torch.FloatTensor, numpy.ndarray) )} snake_case_ = chkpt["dico_word2id"] snake_case_ = {s + "</w>" if s.find("@@" ) == -1 and i > 1_3 else s.replace("@@" , "" ): i for s, i in vocab.items()} # Save pytorch-model snake_case_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME snake_case_ = pytorch_dump_folder_path + "/" + CONFIG_NAME snake_case_ = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"] print(f'Save PyTorch model to {pytorch_weights_dump_path}' ) torch.save(snake_case , snake_case ) print(f'Save configuration file to {pytorch_config_dump_path}' ) with open(snake_case , "w" , encoding="utf-8" ) as f: f.write(json.dumps(snake_case , indent=2 ) + "\n" ) print(f'Save vocab file to {pytorch_config_dump_path}' ) with open(snake_case , "w" , encoding="utf-8" ) as f: f.write(json.dumps(snake_case , indent=2 ) + "\n" ) if __name__ == "__main__": _SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) _SCREAMING_SNAKE_CASE : Optional[int] = parser.parse_args() convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)

def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ): """simple docstring""" while b: a , a :Optional[Any] = b, a % b return a def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ): """simple docstring""" return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_ , a % b ) def __lowerCamelCase ( ): """simple docstring""" print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' ) print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' ) print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' ) print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' ) print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' ) print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' ) print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' ) print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' ) print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' ) print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' ) if __name__ == "__main__": main()

from typing import Callable, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase__ = logging.get_logger(__name__) UpperCamelCase__ = { """microsoft/xprophetnet-large-wiki100-cased""": ( """https://huggingface.co./microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json""" ), } class a__ ( snake_case__ ): _a : str = """xlm-prophetnet""" _a : Dict = ["""past_key_values"""] _a : Any = { """num_attention_heads""": """num_encoder_attention_heads""", } def __init__( self , _A = 0.1 , _A = "gelu" , _A = 3_0_5_2_2 , _A = 1_0_2_4 , _A = 4_0_9_6 , _A = 1_2 , _A = 1_6 , _A = 4_0_9_6 , _A = 1_2 , _A = 1_6 , _A = 0.1 , _A = 0.1 , _A = 5_1_2 , _A = 0.02 , _A = True , _A = True , _A = 0 , _A = 2 , _A = 3_2 , _A = 1_2_8 , _A = False , _A = 0.0 , _A = True , _A = 0 , _A = 1 , _A = 2 , **_A , ): """simple docstring""" __lowerCAmelCase = vocab_size __lowerCAmelCase = hidden_size __lowerCAmelCase = encoder_ffn_dim __lowerCAmelCase = num_encoder_layers __lowerCAmelCase = num_encoder_attention_heads __lowerCAmelCase = decoder_ffn_dim __lowerCAmelCase = num_decoder_layers __lowerCAmelCase = num_decoder_attention_heads __lowerCAmelCase = max_position_embeddings __lowerCAmelCase = init_std # Normal(0, this parameter) __lowerCAmelCase = activation_function # parameters for xlmprophetnet __lowerCAmelCase = ngram __lowerCAmelCase = num_buckets __lowerCAmelCase = relative_max_distance __lowerCAmelCase = disable_ngram_loss __lowerCAmelCase = eps # 3 Types of Dropout __lowerCAmelCase = attention_dropout __lowerCAmelCase = activation_dropout __lowerCAmelCase = dropout __lowerCAmelCase = use_cache super().__init__( pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , is_encoder_decoder=_A , add_cross_attention=_A , decoder_start_token_id=_A , **_A , ) @property def __SCREAMING_SNAKE_CASE( self ): """simple docstring""" return self.num_encoder_layers + self.num_decoder_layers @num_hidden_layers.setter def __SCREAMING_SNAKE_CASE( self , _A ): """simple docstring""" raise NotImplementedError( "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and" " `num_decoder_layers`." )

import enum import shutil import sys UpperCamelCase__ , UpperCamelCase__ = shutil.get_terminal_size() UpperCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""} class a__ ( enum.Enum ): _a : Any = 0 _a : Dict = 1 def _a ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict="" ): sys.stdout.write(str(SCREAMING_SNAKE_CASE_ ) + end ) sys.stdout.flush() def _a ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str="" ): forceWrite(F"""\u001b[{color}m{content}\u001b[0m""" , SCREAMING_SNAKE_CASE_ ) def _a ( ): forceWrite("\r" ) def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str ): forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" ) def _a ( ): forceWrite(" " * TERMINAL_WIDTH ) reset_cursor() def _a ( ): reset_cursor() forceWrite("-" * TERMINAL_WIDTH )

import unittest import torch from diffusers import VQModel from diffusers.utils import floats_tensor, torch_device from diffusers.utils.testing_utils import enable_full_determinism from .test_modeling_common import ModelTesterMixin, UNetTesterMixin enable_full_determinism() class UpperCAmelCase__ ( A_ , A_ , unittest.TestCase ): """simple docstring""" UpperCAmelCase__ : Dict = VQModel UpperCAmelCase__ : str = "sample" @property def _a ( self , A_=(32, 32) ) -> str: __UpperCamelCase =4 __UpperCamelCase =3 __UpperCamelCase =floats_tensor((batch_size, num_channels) + sizes ).to(A_ ) return {"sample": image} @property def _a ( self ) -> str: return (3, 32, 32) @property def _a ( self ) -> Optional[int]: return (3, 32, 32) def _a ( self ) -> Optional[Any]: __UpperCamelCase ={ 'block_out_channels': [32, 64], 'in_channels': 3, 'out_channels': 3, 'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'], 'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'], 'latent_channels': 3, } __UpperCamelCase =self.dummy_input return init_dict, inputs_dict def _a ( self ) -> str: pass def _a ( self ) -> List[str]: pass def _a ( self ) -> int: __UpperCamelCase , __UpperCamelCase =VQModel.from_pretrained('fusing/vqgan-dummy' , output_loading_info=A_ ) self.assertIsNotNone(A_ ) self.assertEqual(len(loading_info['missing_keys'] ) , 0 ) model.to(A_ ) __UpperCamelCase =model(**self.dummy_input ) assert image is not None, "Make sure output is not None" def _a ( self ) -> Tuple: __UpperCamelCase =VQModel.from_pretrained('fusing/vqgan-dummy' ) model.to(A_ ).eval() torch.manual_seed(0 ) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0 ) __UpperCamelCase =torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size ) __UpperCamelCase =image.to(A_ ) with torch.no_grad(): __UpperCamelCase =model(A_ ).sample __UpperCamelCase =output[0, -1, -3:, -3:].flatten().cpu() # fmt: off __UpperCamelCase =torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] ) # fmt: on self.assertTrue(torch.allclose(A_ , A_ , atol=1E-3 ) )

import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class _snake_case : def __init__( self , a , a=13 , a=64 , a=2 , a=3 , a=True , a=True , a=32 , a=5 , a=4 , a=37 , a="gelu" , a=0.1 , a=0.1 , a=10 , a=0.02 , a=[1, 16, 4, 4] , a=None , ) -> Optional[Any]: SCREAMING_SNAKE_CASE = parent SCREAMING_SNAKE_CASE = batch_size SCREAMING_SNAKE_CASE = image_size SCREAMING_SNAKE_CASE = patch_size SCREAMING_SNAKE_CASE = num_channels SCREAMING_SNAKE_CASE = is_training SCREAMING_SNAKE_CASE = use_labels SCREAMING_SNAKE_CASE = hidden_size SCREAMING_SNAKE_CASE = num_hidden_layers SCREAMING_SNAKE_CASE = num_attention_heads SCREAMING_SNAKE_CASE = intermediate_size SCREAMING_SNAKE_CASE = hidden_act SCREAMING_SNAKE_CASE = hidden_dropout_prob SCREAMING_SNAKE_CASE = attention_probs_dropout_prob SCREAMING_SNAKE_CASE = type_sequence_label_size SCREAMING_SNAKE_CASE = initializer_range SCREAMING_SNAKE_CASE = scope SCREAMING_SNAKE_CASE = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size SCREAMING_SNAKE_CASE = (self.image_size // 32) ** 2 SCREAMING_SNAKE_CASE = num_patches + 1 def SCREAMING_SNAKE_CASE__ ( self) -> Dict: SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) SCREAMING_SNAKE_CASE = None if self.use_labels: SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size) SCREAMING_SNAKE_CASE = self.get_config() return config, pixel_values, labels def SCREAMING_SNAKE_CASE__ ( self) -> Tuple: SCREAMING_SNAKE_CASE = { 'global_padding': 'same', 'layer_type': 'bottleneck', 'depths': [3, 4, 9], 'out_features': ['stage1', 'stage2', 'stage3'], 'embedding_dynamic_padding': True, 'hidden_sizes': [4, 8, 16, 32], 'num_groups': 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=a , ) def SCREAMING_SNAKE_CASE__ ( self , a , a , a) -> int: SCREAMING_SNAKE_CASE = ViTHybridModel(config=a) model.to(a) model.eval() SCREAMING_SNAKE_CASE = model(a) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)) def SCREAMING_SNAKE_CASE__ ( self , a , a , a) -> List[Any]: SCREAMING_SNAKE_CASE = self.type_sequence_label_size SCREAMING_SNAKE_CASE = ViTHybridForImageClassification(a) model.to(a) model.eval() SCREAMING_SNAKE_CASE = model(a , labels=a) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size)) def SCREAMING_SNAKE_CASE__ ( self) -> Any: SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = config_and_inputs SCREAMING_SNAKE_CASE = {'pixel_values': pixel_values} return config, inputs_dict @require_torch class _snake_case ( A__ , A__ , unittest.TestCase ): _lowercase : Optional[int] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () _lowercase : str = ( {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification} if is_torch_available() else {} ) _lowercase : int = False _lowercase : Any = False _lowercase : Dict = False def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]: SCREAMING_SNAKE_CASE = ViTHybridModelTester(self) SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37) def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]: self.config_tester.run_common_tests() @unittest.skip(reason='ViT does not use inputs_embeds') def SCREAMING_SNAKE_CASE__ ( self) -> int: pass def SCREAMING_SNAKE_CASE__ ( self) -> int: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE = model_class(a) self.assertIsInstance(model.get_input_embeddings() , (nn.Module)) SCREAMING_SNAKE_CASE = model.get_output_embeddings() self.assertTrue(x is None or isinstance(a , nn.Linear)) def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE = model_class(a) SCREAMING_SNAKE_CASE = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE = [*signature.parameters.keys()] SCREAMING_SNAKE_CASE = ['pixel_values'] self.assertListEqual(arg_names[:1] , a) def SCREAMING_SNAKE_CASE__ ( self) -> str: SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*a) def SCREAMING_SNAKE_CASE__ ( self) -> int: SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*a) def SCREAMING_SNAKE_CASE__ ( self) -> Tuple: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() SCREAMING_SNAKE_CASE = _config_zero_init(a) for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE = model_class(config=a) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": SCREAMING_SNAKE_CASE = [f'''{name}.{key}''' for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @slow def SCREAMING_SNAKE_CASE__ ( self) -> str: for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE = ViTHybridModel.from_pretrained(a) self.assertIsNotNone(a) def lowerCamelCase__ (): SCREAMING_SNAKE_CASE = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png') return image @require_torch @require_vision class _snake_case ( unittest.TestCase ): @cached_property def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]: return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def SCREAMING_SNAKE_CASE__ ( self) -> Tuple: SCREAMING_SNAKE_CASE = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to( a) SCREAMING_SNAKE_CASE = self.default_image_processor SCREAMING_SNAKE_CASE = prepare_img() SCREAMING_SNAKE_CASE = image_processor(images=a , return_tensors='pt').to(a) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE = model(**a) # verify the logits SCREAMING_SNAKE_CASE = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape , a) SCREAMING_SNAKE_CASE = torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(a) self.assertTrue(torch.allclose(outputs.logits[0, :3] , a , atol=1E-4)) @slow @require_accelerate def SCREAMING_SNAKE_CASE__ ( self) -> List[str]: SCREAMING_SNAKE_CASE = ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384') SCREAMING_SNAKE_CASE = ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384' , device_map='auto') SCREAMING_SNAKE_CASE = prepare_img() SCREAMING_SNAKE_CASE = image_processor(images=a , return_tensors='pt') SCREAMING_SNAKE_CASE = model(**a) SCREAMING_SNAKE_CASE = outputs.logits # model predicts one of the 1000 ImageNet classes SCREAMING_SNAKE_CASE = logits.argmax(-1).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , 'tabby, tabby cat')

'''simple docstring''' # Algorithm for the pigeonhole sorting def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> Optional[Any]: '''simple docstring''' snake_case : int = min(SCREAMING_SNAKE_CASE__ ) # min() finds the minimum value snake_case : int = max(SCREAMING_SNAKE_CASE__ ) # max() finds the maximum value snake_case : List[Any] = max_val - min_val + 1 # size is difference of max and min values plus one # list of pigeonholes of size equal to the variable size snake_case : List[str] = [0] * size # Populate the pigeonholes. for x in a: assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ), "integers only please" holes[x - min_val] += 1 # Putting the elements back into the array in an order. snake_case : Dict = 0 for count in range(SCREAMING_SNAKE_CASE__ ): while holes[count] > 0: holes[count] -= 1 snake_case : int = count + min_val i += 1 def _UpperCamelCase ( ) -> Union[str, Any]: '''simple docstring''' snake_case : Tuple = [8, 3, 2, 7, 4, 6, 8] pigeonhole_sort(SCREAMING_SNAKE_CASE__ ) print('''Sorted order is:''' , ''' '''.join(SCREAMING_SNAKE_CASE__ ) ) if __name__ == "__main__": main()

'''simple docstring''' import inspect import unittest from transformers import RegNetConfig, is_flax_available from transformers.testing_utils import require_flax, slow from transformers.utils import cached_property, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor if is_flax_available(): import jax import jax.numpy as jnp from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class snake_case__ ( unittest.TestCase ): """simple docstring""" def __init__( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : List[str]=3 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Dict=10 , UpperCamelCase__ : Any=[10, 20, 30, 40] , UpperCamelCase__ : Any=[1, 1, 2, 1] , UpperCamelCase__ : List[str]=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : str="relu" , UpperCamelCase__ : Union[str, Any]=3 , UpperCamelCase__ : Tuple=None , ) -> List[str]: """simple docstring""" snake_case : List[str] = parent snake_case : Tuple = batch_size snake_case : int = image_size snake_case : Any = num_channels snake_case : Optional[int] = embeddings_size snake_case : Optional[int] = hidden_sizes snake_case : str = depths snake_case : Tuple = is_training snake_case : List[str] = use_labels snake_case : List[str] = hidden_act snake_case : Tuple = num_labels snake_case : Tuple = scope snake_case : List[str] = len(UpperCamelCase__ ) def lowerCAmelCase ( self : Dict ) -> int: """simple docstring""" snake_case : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) snake_case : Any = self.get_config() return config, pixel_values def lowerCAmelCase ( self : List[str] ) -> str: """simple docstring""" return RegNetConfig( num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , ) def lowerCAmelCase ( self : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int ) -> Tuple: """simple docstring""" snake_case : List[str] = FlaxRegNetModel(config=UpperCamelCase__ ) snake_case : str = model(UpperCamelCase__ ) # Output shape (b, c, h, w) self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , ) def lowerCAmelCase ( self : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Dict ) -> Dict: """simple docstring""" snake_case : int = self.num_labels snake_case : List[str] = FlaxRegNetForImageClassification(config=UpperCamelCase__ ) snake_case : Any = model(UpperCamelCase__ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def lowerCAmelCase ( self : str ) -> Dict: """simple docstring""" snake_case : str = self.prepare_config_and_inputs() snake_case ,snake_case : Tuple = config_and_inputs snake_case : int = {'''pixel_values''': pixel_values} return config, inputs_dict @require_flax class snake_case__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ): """simple docstring""" lowerCamelCase = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else () lowerCamelCase = False lowerCamelCase = False lowerCamelCase = False def lowerCAmelCase ( self : List[str] ) -> None: """simple docstring""" snake_case : List[str] = FlaxRegNetModelTester(self ) snake_case : Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ ) def lowerCAmelCase ( self : Any ) -> str: """simple docstring""" self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def lowerCAmelCase ( self : Tuple ) -> Any: """simple docstring""" return def lowerCAmelCase ( self : Optional[int] ) -> str: """simple docstring""" snake_case : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*UpperCamelCase__ ) def lowerCAmelCase ( self : str ) -> Dict: """simple docstring""" snake_case : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ ) @unittest.skip(reason='''RegNet does not use inputs_embeds''' ) def lowerCAmelCase ( self : List[str] ) -> int: """simple docstring""" pass @unittest.skip(reason='''RegNet does not support input and output embeddings''' ) def lowerCAmelCase ( self : Union[str, Any] ) -> Any: """simple docstring""" pass def lowerCAmelCase ( self : Optional[int] ) -> str: """simple docstring""" snake_case ,snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: snake_case : Union[str, Any] = model_class(UpperCamelCase__ ) snake_case : Union[str, Any] = inspect.signature(model.__call__ ) # signature.parameters is an OrderedDict => so arg_names order is deterministic snake_case : int = [*signature.parameters.keys()] snake_case : Optional[Any] = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , UpperCamelCase__ ) def lowerCAmelCase ( self : List[Any] ) -> List[str]: """simple docstring""" def check_hidden_states_output(UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple ): snake_case : Union[str, Any] = model_class(UpperCamelCase__ ) snake_case : Any = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) ) snake_case : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states snake_case : Optional[int] = self.model_tester.num_stages self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 ) snake_case ,snake_case : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: snake_case : Tuple = True check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] snake_case : List[str] = True check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]: """simple docstring""" snake_case ,snake_case : str = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): snake_case : Any = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) snake_case : Optional[Any] = model_class(UpperCamelCase__ ) @jax.jit def model_jitted(UpperCamelCase__ : Dict , **UpperCamelCase__ : Tuple ): return model(pixel_values=UpperCamelCase__ , **UpperCamelCase__ ) with self.subTest('''JIT Enabled''' ): snake_case : Optional[int] = model_jitted(**UpperCamelCase__ ).to_tuple() with self.subTest('''JIT Disabled''' ): with jax.disable_jit(): snake_case : Tuple = model_jitted(**UpperCamelCase__ ).to_tuple() self.assertEqual(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) ) for jitted_output, output in zip(UpperCamelCase__ , UpperCamelCase__ ): self.assertEqual(jitted_output.shape , output.shape ) def _UpperCamelCase ( ) -> Union[str, Any]: '''simple docstring''' snake_case : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_flax class snake_case__ ( unittest.TestCase ): """simple docstring""" @cached_property def lowerCAmelCase ( self : Optional[Any] ) -> Tuple: """simple docstring""" return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None @slow def lowerCAmelCase ( self : int ) -> int: """simple docstring""" snake_case : str = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' ) snake_case : Any = self.default_image_processor snake_case : Any = prepare_img() snake_case : Union[str, Any] = image_processor(images=UpperCamelCase__ , return_tensors='''np''' ) snake_case : List[str] = model(**UpperCamelCase__ ) # verify the logits snake_case : Optional[int] = (1, 1000) self.assertEqual(outputs.logits.shape , UpperCamelCase__ ) snake_case : Dict = jnp.array([-0.4_180, -1.5_051, -3.4_836] ) self.assertTrue(jnp.allclose(outputs.logits[0, :3] , UpperCamelCase__ , atol=1e-4 ) )