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if isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer):
default_dtype = {"dtype": torch.int64}
# Convert dtype to np.int64 if it's either np.uint16 or np.uint32 to ensure compatibility.
# np.uint64 is excluded from this conversion as there is no compatible PyTorch dtype that can handle it without loss.
if value.dtype in [np.uint16, np.uint32]:
value = value.astype(np.int64)
elif isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating):
default_dtype = {"dtype": torch.float32}
if config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(value, PIL.Image.Image):
value = np.asarray(value)
if value.ndim == 2:
value = value[:, :, np.newaxis] | 256 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/torch_formatter.py |
value = value.transpose((2, 0, 1))
if config.DECORD_AVAILABLE and "decord" in sys.modules:
from decord import VideoReader
from decord.bridge import to_torch
if isinstance(value, VideoReader):
value._hf_bridge_out = to_torch
return value
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
def _recursive_tensorize(self, data_struct):
import torch | 256 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/torch_formatter.py |
# support for torch, tf, jax etc.
if hasattr(data_struct, "__array__") and not isinstance(data_struct, torch.Tensor):
data_struct = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(data_struct, np.ndarray):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
elif isinstance(data_struct, (list, tuple)):
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
return self._tensorize(data_struct)
def recursive_tensorize(self, data_struct: dict):
return map_nested(self._recursive_tensorize, data_struct, map_list=False) | 256 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/torch_formatter.py |
def format_row(self, pa_table: pa.Table) -> Mapping:
row = self.numpy_arrow_extractor().extract_row(pa_table)
row = self.python_features_decoder.decode_row(row)
return self.recursive_tensorize(row)
def format_column(self, pa_table: pa.Table) -> "torch.Tensor":
column = self.numpy_arrow_extractor().extract_column(pa_table)
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
column = self.recursive_tensorize(column)
column = self._consolidate(column)
return column
def format_batch(self, pa_table: pa.Table) -> Mapping:
batch = self.numpy_arrow_extractor().extract_batch(pa_table)
batch = self.python_features_decoder.decode_batch(batch)
batch = self.recursive_tensorize(batch)
for column_name in batch:
batch[column_name] = self._consolidate(batch[column_name])
return batch | 256 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/torch_formatter.py |
class JaxFormatter(TensorFormatter[Mapping, "jax.Array", Mapping]):
def __init__(self, features=None, device=None, token_per_repo_id=None, **jnp_array_kwargs):
super().__init__(features=features, token_per_repo_id=token_per_repo_id)
import jax
from jaxlib.xla_client import Device | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
if isinstance(device, Device):
raise ValueError(
f"Expected {device} to be a `str` not {type(device)}, as `jaxlib.xla_extension.Device` "
"is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
"the device with `str()` to get its string identifier that will be internally mapped "
"to the actual `jaxlib.xla_extension.Device`."
)
self.device = device if isinstance(device, str) else str(jax.devices()[0])
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
DEVICE_MAPPING = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys()):
logger.warning( | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
f"Device with string identifier {self.device} not listed among the available "
f"devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default "
f"device: {str(jax.devices()[0])}."
)
self.device = str(jax.devices()[0])
self.jnp_array_kwargs = jnp_array_kwargs | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
@staticmethod
def _map_devices_to_str() -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(device): device for device in jax.devices()}
def _consolidate(self, column):
import jax
import jax.numpy as jnp
if isinstance(column, list) and column:
if all(
isinstance(x, jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column
):
return jnp.stack(column, axis=0)
return column
def _tensorize(self, value):
import jax
import jax.numpy as jnp
if isinstance(value, (str, bytes, type(None))):
return value
elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
return value.tolist()
default_dtype = {} | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
if isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_x64:
default_dtype = {"dtype": jnp.int64}
else:
default_dtype = {"dtype": jnp.int32}
elif isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating):
default_dtype = {"dtype": jnp.float32}
if config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
if isinstance(value, PIL.Image.Image):
value = np.asarray(value)
if config.DECORD_AVAILABLE and "decord" in sys.modules:
# We need to import torch first, otherwise later it can cause issues
# e.g. "RuntimeError: random_device could not be read"
# when running `torch.tensor(value).share_memory_()`
if config.TORCH_AVAILABLE:
import torch # noqa
from decord import VideoReader
if isinstance(value, VideoReader):
value._hf_bridge_out = lambda x: jnp.array(np.asarray(x))
return value
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
DEVICE_MAPPING = self._map_devices_to_str() | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
with jax.default_device(DEVICE_MAPPING[self.device]):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})
def _recursive_tensorize(self, data_struct):
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
if isinstance(data_struct, torch.Tensor):
return self._tensorize(data_struct.detach().cpu().numpy()[()])
if hasattr(data_struct, "__array__") and not isinstance(data_struct, jax.Array):
data_struct = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(data_struct, np.ndarray):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
elif isinstance(data_struct, (list, tuple)):
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
return self._tensorize(data_struct)
def recursive_tensorize(self, data_struct: dict):
return map_nested(self._recursive_tensorize, data_struct, map_list=False) | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
def format_row(self, pa_table: pa.Table) -> Mapping:
row = self.numpy_arrow_extractor().extract_row(pa_table)
row = self.python_features_decoder.decode_row(row)
return self.recursive_tensorize(row)
def format_column(self, pa_table: pa.Table) -> "jax.Array":
column = self.numpy_arrow_extractor().extract_column(pa_table)
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
column = self.recursive_tensorize(column)
column = self._consolidate(column)
return column
def format_batch(self, pa_table: pa.Table) -> Mapping:
batch = self.numpy_arrow_extractor().extract_batch(pa_table)
batch = self.python_features_decoder.decode_batch(batch)
batch = self.recursive_tensorize(batch)
for column_name in batch:
batch[column_name] = self._consolidate(batch[column_name])
return batch | 257 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/jax_formatter.py |
class TFFormatter(TensorFormatter[Mapping, "tf.Tensor", Mapping]):
def __init__(self, features=None, token_per_repo_id=None, **tf_tensor_kwargs):
super().__init__(features=features, token_per_repo_id=token_per_repo_id)
self.tf_tensor_kwargs = tf_tensor_kwargs
import tensorflow as tf # noqa: F401 - import tf at initialization
def _consolidate(self, column):
import tensorflow as tf
if isinstance(column, list) and column:
if all(
isinstance(x, tf.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column
):
return tf.stack(column)
elif all(
isinstance(x, (tf.Tensor, tf.RaggedTensor)) and x.ndim == 1 and x.dtype == column[0].dtype
for x in column
):
# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
return tf.ragged.stack(column) | 258 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/tf_formatter.py |
return column
def _tensorize(self, value):
import tensorflow as tf
if value is None:
return value
default_dtype = {}
if isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer):
default_dtype = {"dtype": tf.int64}
elif isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating):
default_dtype = {"dtype": tf.float32}
if config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image | 258 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/tf_formatter.py |
if isinstance(value, PIL.Image.Image):
value = np.asarray(value)
if config.DECORD_AVAILABLE and "decord" in sys.modules:
# We need to import torch first, otherwise later it can cause issues
# e.g. "RuntimeError: random_device could not be read"
# when running `torch.tensor(value).share_memory_()`
if config.TORCH_AVAILABLE:
import torch # noqa
from decord import VideoReader
from decord.bridge import to_tensorflow
if isinstance(value, VideoReader):
value._hf_bridge_out = to_tensorflow
return value
return tf.convert_to_tensor(value, **{**default_dtype, **self.tf_tensor_kwargs})
def _recursive_tensorize(self, data_struct):
import tensorflow as tf
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch | 258 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/tf_formatter.py |
if isinstance(data_struct, torch.Tensor):
return self._tensorize(data_struct.detach().cpu().numpy()[()])
if hasattr(data_struct, "__array__") and not isinstance(data_struct, tf.Tensor):
data_struct = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(data_struct, np.ndarray):
if data_struct.dtype == object: # tf tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
elif isinstance(data_struct, (list, tuple)):
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
return self._tensorize(data_struct)
def recursive_tensorize(self, data_struct: dict):
return map_nested(self._recursive_tensorize, data_struct, map_list=False) | 258 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/tf_formatter.py |
def format_row(self, pa_table: pa.Table) -> Mapping:
row = self.numpy_arrow_extractor().extract_row(pa_table)
row = self.python_features_decoder.decode_row(row)
return self.recursive_tensorize(row)
def format_column(self, pa_table: pa.Table) -> "tf.Tensor":
column = self.numpy_arrow_extractor().extract_column(pa_table)
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
column = self.recursive_tensorize(column)
column = self._consolidate(column)
return column
def format_batch(self, pa_table: pa.Table) -> Mapping:
batch = self.numpy_arrow_extractor().extract_batch(pa_table)
batch = self.python_features_decoder.decode_batch(batch)
batch = self.recursive_tensorize(batch)
for column_name in batch:
batch[column_name] = self._consolidate(batch[column_name])
return batch | 258 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/tf_formatter.py |
class NumpyFormatter(TensorFormatter[Mapping, np.ndarray, Mapping]):
def __init__(self, features=None, token_per_repo_id=None, **np_array_kwargs):
super().__init__(features=features, token_per_repo_id=token_per_repo_id)
self.np_array_kwargs = np_array_kwargs
def _consolidate(self, column):
if isinstance(column, list):
if column and all(
isinstance(x, np.ndarray) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column
):
return np.stack(column)
else:
# don't use np.array(column, dtype=object)
# since it fails in certain cases
# see https://stackoverflow.com/q/51005699
out = np.empty(len(column), dtype=object)
out[:] = column
return out
return column | 259 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/np_formatter.py |
def _tensorize(self, value):
if isinstance(value, (str, bytes, type(None))):
return value
elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
return value
elif isinstance(value, np.number):
return value
default_dtype = {}
if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.integer):
default_dtype = {"dtype": np.int64}
elif isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.floating):
default_dtype = {"dtype": np.float32}
if config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image | 259 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/np_formatter.py |
if isinstance(value, PIL.Image.Image):
return np.asarray(value, **self.np_array_kwargs)
if config.DECORD_AVAILABLE and "decord" in sys.modules:
# We need to import torch first, otherwise later it can cause issues
# e.g. "RuntimeError: random_device could not be read"
# when running `torch.tensor(value).share_memory_()`
if config.TORCH_AVAILABLE:
import torch # noqa
from decord import VideoReader
if isinstance(value, VideoReader):
value._hf_bridge_out = np.asarray
return value
return np.asarray(value, **{**default_dtype, **self.np_array_kwargs})
def _recursive_tensorize(self, data_struct):
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch | 259 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/np_formatter.py |
if isinstance(data_struct, torch.Tensor):
return self._tensorize(data_struct.detach().cpu().numpy()[()])
if hasattr(data_struct, "__array__") and not isinstance(data_struct, (np.ndarray, np.character, np.number)):
data_struct = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(data_struct, np.ndarray):
if data_struct.dtype == object:
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
if isinstance(data_struct, (list, tuple)):
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
return self._tensorize(data_struct)
def recursive_tensorize(self, data_struct: dict):
return map_nested(self._recursive_tensorize, data_struct, map_list=False) | 259 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/np_formatter.py |
def format_row(self, pa_table: pa.Table) -> Mapping:
row = self.numpy_arrow_extractor().extract_row(pa_table)
row = self.python_features_decoder.decode_row(row)
return self.recursive_tensorize(row)
def format_column(self, pa_table: pa.Table) -> np.ndarray:
column = self.numpy_arrow_extractor().extract_column(pa_table)
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
column = self.recursive_tensorize(column)
column = self._consolidate(column)
return column
def format_batch(self, pa_table: pa.Table) -> Mapping:
batch = self.numpy_arrow_extractor().extract_batch(pa_table)
batch = self.python_features_decoder.decode_batch(batch)
batch = self.recursive_tensorize(batch)
for column_name in batch:
batch[column_name] = self._consolidate(batch[column_name])
return batch | 259 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/formatting/np_formatter.py |
class DeleteFromHubCommand(BaseDatasetsCLICommand):
@staticmethod
def register_subcommand(parser):
parser: ArgumentParser = parser.add_parser("delete_from_hub", help="Delete dataset config from the Hub")
parser.add_argument(
"dataset_id", help="source dataset ID, e.g. USERNAME/DATASET_NAME or ORGANIZATION/DATASET_NAME"
)
parser.add_argument("config_name", help="config name to delete")
parser.add_argument("--token", help="access token to the Hugging Face Hub")
parser.add_argument("--revision", help="source revision")
parser.set_defaults(func=_command_factory)
def __init__(
self,
dataset_id: str,
config_name: str,
token: Optional[str],
revision: Optional[str],
):
self._dataset_id = dataset_id
self._config_name = config_name
self._token = token
self._revision = revision | 260 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/delete_from_hub.py |
def run(self) -> None:
_ = delete_from_hub(self._dataset_id, self._config_name, revision=self._revision, token=self._token) | 260 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/delete_from_hub.py |
class EnvironmentCommand(BaseDatasetsCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env", help="Print relevant system environment info.")
download_parser.set_defaults(func=info_command_factory)
def run(self):
info = {
"`datasets` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"`huggingface_hub` version": huggingface_hub.__version__,
"PyArrow version": pyarrow.__version__,
"Pandas version": pandas.__version__,
"`fsspec` version": fsspec.__version__,
}
print("\nCopy-and-paste the text below in your GitHub issue.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d):
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n" | 261 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/env.py |
class ConvertToParquetCommand(BaseDatasetsCLICommand):
@staticmethod
def register_subcommand(parser):
parser: ArgumentParser = parser.add_parser("convert_to_parquet", help="Convert dataset to Parquet")
parser.add_argument(
"dataset_id", help="source dataset ID, e.g. USERNAME/DATASET_NAME or ORGANIZATION/DATASET_NAME"
)
parser.add_argument("--token", help="access token to the Hugging Face Hub (defaults to logged-in user's one)")
parser.add_argument("--revision", help="source revision")
parser.add_argument(
"--trust_remote_code", action="store_true", help="whether to trust the code execution of the load script"
)
parser.set_defaults(func=_command_factory) | 262 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert_to_parquet.py |
def __init__(
self,
dataset_id: str,
token: Optional[str],
revision: Optional[str],
trust_remote_code: bool,
):
self._dataset_id = dataset_id
self._token = token
self._revision = revision
self._trust_remote_code = trust_remote_code
def run(self) -> None:
_ = convert_to_parquet(
self._dataset_id, revision=self._revision, token=self._token, trust_remote_code=self._trust_remote_code
) | 262 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert_to_parquet.py |
class ConvertCommand(BaseDatasetsCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the datasets-cli
Args:
parser: Root parser to register command-specific arguments
"""
train_parser = parser.add_parser(
"convert",
help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",
)
train_parser.add_argument(
"--tfds_path",
type=str,
required=True,
help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",
)
train_parser.add_argument(
"--datasets_directory", type=str, required=True, help="Path to the HuggingFace Datasets folder."
)
train_parser.set_defaults(func=convert_command_factory) | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
def __init__(self, tfds_path: str, datasets_directory: str, *args):
self._logger = get_logger("datasets-cli/converting")
self._tfds_path = tfds_path
self._datasets_directory = datasets_directory
def run(self):
if os.path.isdir(self._tfds_path):
abs_tfds_path = os.path.abspath(self._tfds_path)
elif os.path.isfile(self._tfds_path):
abs_tfds_path = os.path.dirname(self._tfds_path)
else:
raise ValueError("--tfds_path is neither a directory nor a file. Please check path.")
abs_datasets_path = os.path.abspath(self._datasets_directory)
self._logger.info(f"Converting datasets from {abs_tfds_path} to {abs_datasets_path}")
utils_files = []
with_manual_update = []
imports_to_builder_map = {}
if os.path.isdir(self._tfds_path):
file_names = os.listdir(abs_tfds_path)
else:
file_names = [os.path.basename(self._tfds_path)] | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
for f_name in file_names:
self._logger.info(f"Looking at file {f_name}")
input_file = os.path.join(abs_tfds_path, f_name)
output_file = os.path.join(abs_datasets_path, f_name)
if not os.path.isfile(input_file) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info("Skipping file")
continue
with open(input_file, encoding="utf-8") as f:
lines = f.readlines()
out_lines = []
is_builder = False
needs_manual_update = False
tfds_imports = []
for line in lines:
out_line = line | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
out_line = "import datasets\n"
elif "import tensorflow" in out_line:
# order is important here
out_line = ""
continue
elif "from absl import logging" in out_line:
out_line = "from datasets import logging\n"
elif "getLogger" in out_line:
out_line = out_line.replace("getLogger", "get_logger")
elif any(expression in out_line for expression in TO_HIGHLIGHT):
needs_manual_update = True | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
to_remove = list(filter(lambda e: e in out_line, TO_HIGHLIGHT))
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(to_remove) + "\n")
out_lines.append(out_line)
out_lines.append(HIGHLIGHT_MESSAGE_POST)
continue
else:
for pattern, replacement in TO_CONVERT:
out_line = re.sub(pattern, replacement, out_line) | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
match = re.match(r"from\stensorflow_datasets.*import\s([^\.\r\n]+)", out_line)
tfds_imports.extend(imp.strip() for imp in match.group(1).split(","))
out_line = "from . import " + match.group(1)
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(f"Error converting {out_line.strip()}")
if "GeneratorBasedBuilder" in out_line:
is_builder = True
out_lines.append(out_line) | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
dir_name = f_name.replace(".py", "")
output_dir = os.path.join(abs_datasets_path, dir_name)
output_file = os.path.join(output_dir, f_name)
os.makedirs(output_dir, exist_ok=True)
self._logger.info(f"Adding directory {output_dir}")
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
else:
# Utilities will be moved at the end
utils_files.append(output_file)
if needs_manual_update:
with_manual_update.append(output_file)
with open(output_file, "w", encoding="utf-8") as f:
f.writelines(out_lines)
self._logger.info(f"Converted in {output_file}") | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
for utils_file in utils_files:
try:
f_name = os.path.basename(utils_file)
dest_folder = imports_to_builder_map[f_name.replace(".py", "")]
self._logger.info(f"Moving {dest_folder} to {utils_file}")
shutil.copy(utils_file, dest_folder)
except KeyError:
self._logger.error(f"Cannot find destination folder for {utils_file}. Please copy manually.")
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
f"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."
) | 263 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/convert.py |
class BaseDatasetsCLICommand(ABC):
@staticmethod
@abstractmethod
def register_subcommand(parser: ArgumentParser):
raise NotImplementedError()
@abstractmethod
def run(self):
raise NotImplementedError() | 264 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/__init__.py |
class TestCommand(BaseDatasetsCLICommand):
__test__ = False # to tell pytest it's not a test class | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
@staticmethod
def register_subcommand(parser: ArgumentParser):
test_parser = parser.add_parser("test", help="Test dataset implementation.")
test_parser.add_argument("--name", type=str, default=None, help="Dataset processing name")
test_parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="Cache directory where the datasets are stored.",
)
test_parser.add_argument(
"--data_dir",
type=str,
default=None,
help="Can be used to specify a manual directory to get the files from.",
)
test_parser.add_argument("--all_configs", action="store_true", help="Test all dataset configurations")
test_parser.add_argument(
"--save_info", action="store_true", help="Save the dataset infos in the dataset card (README.md)"
)
test_parser.add_argument(
"--ignore_verifications",
action="store_true", | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
help="Run the test without checksums and splits checks.",
)
test_parser.add_argument("--force_redownload", action="store_true", help="Force dataset redownload")
test_parser.add_argument(
"--clear_cache",
action="store_true",
help="Remove downloaded files and cached datasets after each config test",
)
test_parser.add_argument("--num_proc", type=int, default=None, help="Number of processes")
test_parser.add_argument(
"--trust_remote_code", action="store_true", help="whether to trust the code execution of the load script"
)
# aliases
test_parser.add_argument("--save_infos", action="store_true", help="alias to save_info")
test_parser.add_argument("dataset", type=str, help="Name of the dataset to download")
test_parser.set_defaults(func=_test_command_factory) | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
def __init__(
self,
dataset: str,
name: str,
cache_dir: str,
data_dir: str,
all_configs: bool,
save_infos: bool,
ignore_verifications: bool,
force_redownload: bool,
clear_cache: bool,
num_proc: int,
trust_remote_code: Optional[bool],
):
self._dataset = dataset
self._name = name
self._cache_dir = cache_dir
self._data_dir = data_dir
self._all_configs = all_configs
self._save_infos = save_infos
self._ignore_verifications = ignore_verifications
self._force_redownload = force_redownload
self._clear_cache = clear_cache
self._num_proc = num_proc
self._trust_remote_code = trust_remote_code
if clear_cache and not cache_dir:
print(
"When --clear_cache is used, specifying a cache directory is mandatory.\n" | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
"The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
"Please provide a --cache_dir that will be used to test the dataset script."
)
exit(1)
if save_infos:
self._ignore_verifications = True | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
def run(self):
logging.getLogger("filelock").setLevel(ERROR)
if self._name is not None and self._all_configs:
print("Both parameters `config` and `all_configs` can't be used at once.")
exit(1)
path, config_name = self._dataset, self._name
module = dataset_module_factory(path, trust_remote_code=self._trust_remote_code)
builder_cls = import_main_class(module.module_path)
n_builders = len(builder_cls.BUILDER_CONFIGS) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1 | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
def get_builders() -> Generator[DatasetBuilder, None, None]:
if self._all_configs and builder_cls.BUILDER_CONFIGS:
for i, config in enumerate(builder_cls.BUILDER_CONFIGS):
if "config_name" in module.builder_kwargs:
yield builder_cls(
cache_dir=self._cache_dir,
data_dir=self._data_dir,
**module.builder_kwargs,
)
else:
yield builder_cls(
config_name=config.name,
cache_dir=self._cache_dir,
data_dir=self._data_dir,
**module.builder_kwargs,
)
else:
if "config_name" in module.builder_kwargs:
yield builder_cls(cache_dir=self._cache_dir, data_dir=self._data_dir, **module.builder_kwargs) | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
else:
yield builder_cls(
config_name=config_name,
cache_dir=self._cache_dir,
data_dir=self._data_dir,
**module.builder_kwargs,
) | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
for j, builder in enumerate(get_builders()):
print(f"Testing builder '{builder.config.name}' ({j + 1}/{n_builders})")
builder._record_infos = os.path.exists(
os.path.join(builder.get_imported_module_dir(), datasets.config.DATASETDICT_INFOS_FILENAME)
) # record checksums only if we need to update a (deprecated) dataset_infos.json
builder.download_and_prepare(
download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
if not self._force_redownload
else DownloadMode.FORCE_REDOWNLOAD,
verification_mode=VerificationMode.NO_CHECKS
if self._ignore_verifications
else VerificationMode.ALL_CHECKS,
num_proc=self._num_proc,
)
builder.as_dataset()
if self._save_infos:
builder._save_infos() | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
# The dataset_infos are saved in the YAML part of the README.md | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
# Let's move it to the original directory of the dataset script, to allow the user to
# upload them on S3 at the same time afterwards.
if self._save_infos:
dataset_readme_path = os.path.join(
builder_cls.get_imported_module_dir(), datasets.config.REPOCARD_FILENAME
)
name = Path(path).name + ".py"
combined_path = os.path.join(path, name)
if os.path.isfile(path):
dataset_dir = os.path.dirname(path)
elif os.path.isfile(combined_path):
dataset_dir = path
elif os.path.isdir(path): # for local directories containing only data files
dataset_dir = path
else: # in case of a remote dataset
dataset_dir = None
print(f"Dataset card saved at {dataset_readme_path}") | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
# Move dataset_info back to the user
if dataset_dir is not None:
user_dataset_readme_path = os.path.join(dataset_dir, datasets.config.REPOCARD_FILENAME)
copyfile(dataset_readme_path, user_dataset_readme_path)
print(f"Dataset card saved at {user_dataset_readme_path}")
# If clear_cache=True, the download folder and the dataset builder cache directory are deleted
if self._clear_cache:
if os.path.isdir(builder._cache_dir):
logger.warning(f"Clearing cache at {builder._cache_dir}")
rmtree(builder._cache_dir)
download_dir = os.path.join(self._cache_dir, datasets.config.DOWNLOADED_DATASETS_DIR)
if os.path.isdir(download_dir):
logger.warning(f"Clearing cache at {download_dir}")
rmtree(download_dir)
print("Test successful.") | 265 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/commands/test.py |
class BaseCompressedFileFileSystem(AbstractArchiveFileSystem):
"""Read contents of compressed file as a filesystem with one file inside."""
root_marker = ""
protocol: str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
compression: str = None # compression type in fsspec. ex: "gzip"
extension: str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__(
self, fo: str = "", target_protocol: Optional[str] = None, target_options: Optional[dict] = None, **kwargs
):
"""
The compressed file system can be instantiated from any compressed file.
It reads the contents of compressed file as a filesystem with one file inside, as if it was an archive.
The single file inside the filesystem is named after the compresssed file,
without the compression extension at the end of the filename. | 266 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
Args:
fo (:obj:``str``): Path to compressed file. Will fetch file using ``fsspec.open()``
mode (:obj:``str``): Currently, only 'rb' accepted
target_protocol(:obj:``str``, optional): To override the FS protocol inferred from a URL.
target_options (:obj:``dict``, optional): Kwargs passed when instantiating the target FS.
"""
super().__init__(self, **kwargs)
self.fo = fo.__fspath__() if hasattr(fo, "__fspath__") else fo
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
self._open_with_fsspec = partial(
fsspec.open,
self.fo,
mode="rb",
protocol=target_protocol,
compression=self.compression,
client_kwargs={
"requote_redirect_url": False, # see https://github.com/huggingface/datasets/pull/5459
"trust_env": True, # Enable reading proxy env variables. | 266 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
**(target_options or {}).pop("client_kwargs", {}), # To avoid issues if it was already passed.
},
**(target_options or {}),
)
self.compressed_name = os.path.basename(self.fo.split("::")[0])
self.uncompressed_name = (
self.compressed_name[: self.compressed_name.rindex(".")]
if "." in self.compressed_name
else self.compressed_name
)
self.dir_cache = None | 266 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
@classmethod
def _strip_protocol(cls, path):
# compressed file paths are always relative to the archive root
return super()._strip_protocol(path).lstrip("/")
def _get_dirs(self):
if self.dir_cache is None:
f = {**self._open_with_fsspec().fs.info(self.fo), "name": self.uncompressed_name}
self.dir_cache = {f["name"]: f}
def cat(self, path: str):
with self._open_with_fsspec().open() as f:
return f.read()
def _open(
self,
path: str,
mode: str = "rb",
block_size=None,
autocommit=True,
cache_options=None,
**kwargs,
):
path = self._strip_protocol(path)
if mode != "rb":
raise ValueError(f"Tried to read with mode {mode} on file {self.fo} opened with mode 'rb'")
return self._open_with_fsspec().open() | 266 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
class Bz2FileSystem(BaseCompressedFileFileSystem):
"""Read contents of BZ2 file as a filesystem with one file inside."""
protocol = "bz2"
compression = "bz2"
extension = ".bz2" | 267 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
class GzipFileSystem(BaseCompressedFileFileSystem):
"""Read contents of GZIP file as a filesystem with one file inside."""
protocol = "gzip"
compression = "gzip"
extension = ".gz" | 268 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
class Lz4FileSystem(BaseCompressedFileFileSystem):
"""Read contents of LZ4 file as a filesystem with one file inside."""
protocol = "lz4"
compression = "lz4"
extension = ".lz4" | 269 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
class XzFileSystem(BaseCompressedFileFileSystem):
"""Read contents of .xz (LZMA) file as a filesystem with one file inside."""
protocol = "xz"
compression = "xz"
extension = ".xz" | 270 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |
class ZstdFileSystem(BaseCompressedFileFileSystem):
"""
Read contents of .zstd file as a filesystem with one file inside.
"""
protocol = "zstd"
compression = "zstd"
extension = ".zst" | 271 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/filesystems/compression.py |