update files for device agnostic inference

.ipynb_checkpoints/app-checkpoint.py

@@ -2,6 +2,7 @@ import gradio as gr
 import json
 import torch
 import wavio
+import numpy as np
 from tqdm import tqdm
 from huggingface_hub import snapshot_download
 
@@ -23,6 +24,7 @@ class MusicFeaturePredictor:
     def __init__(self, path, device="cuda:0", cache_dir=None, local_files_only=False):
         self.beats_tokenizer = AutoTokenizer.from_pretrained(
             "microsoft/deberta-v3-large",
+            use_fast=False,
             cache_dir=cache_dir,
             local_files_only=local_files_only,
         )
@@ -164,6 +166,7 @@ class Mustango:
             main_config["scheduler_name"],
             unet_model_config_path=f"{path}/configs/music_diffusion_model_config.json",
         ).to(device)
+        self.model.device = device
 
         vae_weights = torch.load(
             f"{path}/vae/pytorch_model_vae.bin", map_location=device
@@ -213,9 +216,11 @@ class Mustango:
 
 # Initialize Mustango
 if torch.cuda.is_available():
-    mustango = Mustango()
+    mustango = Mustango(device="cpu")
 else:
     mustango = Mustango(device="cpu")
+    
+output_wave = mustango.generate("This techno song features a synth lead playing the main melody.", 5, 3, disable_progress=False)
 
 def gradio_generate(prompt, steps, guidance):
     output_wave = mustango.generate(prompt, steps, guidance)
@@ -225,6 +230,7 @@ def gradio_generate(prompt, steps, guidance):
     
     return output_filename
 
+
 # description_text = """
 # <p><a href="https://huggingface.co/spaces/declare-lab/mustango/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
 # Generate music using Mustango by providing a text prompt.

.ipynb_checkpoints/modelling_deberta_v2-checkpoint.py

@@ -0,0 +1,1750 @@
+# coding=utf-8
+# Copyright 2020 Microsoft and the Hugging Face Inc. team.
+#
+# 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.
+""" PyTorch DeBERTa-v2 model."""
+
+from collections.abc import Sequence
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    ModelOutput,
+    BaseModelOutput,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import softmax_backward_data
+from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from transformers.models.deberta_v2.configuration_deberta_v2 import DebertaV2Config
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "DebertaV2Config"
+_CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge"
+_QA_TARGET_START_INDEX = 2
+_QA_TARGET_END_INDEX = 9
+
+DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "microsoft/deberta-v2-xlarge",
+    "microsoft/deberta-v2-xxlarge",
+    "microsoft/deberta-v2-xlarge-mnli",
+    "microsoft/deberta-v2-xxlarge-mnli",
+]
+
+
+# Copied from transformers.models.deberta.modeling_deberta.ContextPooler
+class ContextPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
+        self.dropout = StableDropout(config.pooler_dropout)
+        self.config = config
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+
+        context_token = hidden_states[:, 0]
+        context_token = self.dropout(context_token)
+        pooled_output = self.dense(context_token)
+        pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)
+        return pooled_output
+
+    @property
+    def output_dim(self):
+        return self.config.hidden_size
+
+
+# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2
+class XSoftmax(torch.autograd.Function):
+    """
+    Masked Softmax which is optimized for saving memory
+
+    Args:
+        input (`torch.tensor`): The input tensor that will apply softmax.
+        mask (`torch.IntTensor`):
+            The mask matrix where 0 indicate that element will be ignored in the softmax calculation.
+        dim (int): The dimension that will apply softmax
+
+    Example:
+
+    ```python
+    >>> import torch
+    >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax
+
+    >>> # Make a tensor
+    >>> x = torch.randn([4, 20, 100])
+
+    >>> # Create a mask
+    >>> mask = (x > 0).int()
+
+    >>> # Specify the dimension to apply softmax
+    >>> dim = -1
+
+    >>> y = XSoftmax.apply(x, mask, dim)
+    ```"""
+
+    @staticmethod
+    def forward(self, input, mask, dim):
+        self.dim = dim
+        rmask = ~(mask.to(torch.bool))
+
+        output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min))
+        output = torch.softmax(output, self.dim)
+        output.masked_fill_(rmask, 0)
+        self.save_for_backward(output)
+        return output
+
+    @staticmethod
+    def backward(self, grad_output):
+        (output,) = self.saved_tensors
+        inputGrad = softmax_backward_data(self, grad_output, output, self.dim, output)
+        return inputGrad, None, None
+
+    @staticmethod
+    def symbolic(g, self, mask, dim):
+        import torch.onnx.symbolic_helper as sym_help
+        from torch.onnx.symbolic_opset9 import masked_fill, softmax
+
+        mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"])
+        r_mask = g.op(
+            "Cast",
+            g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value),
+            to_i=sym_help.cast_pytorch_to_onnx["Bool"],
+        )
+        output = masked_fill(
+            g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min))
+        )
+        output = softmax(g, output, dim)
+        return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.bool)))
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DropoutContext
+class DropoutContext(object):
+    def __init__(self):
+        self.dropout = 0
+        self.mask = None
+        self.scale = 1
+        self.reuse_mask = True
+
+
+# Copied from transformers.models.deberta.modeling_deberta.get_mask
+def get_mask(input, local_context):
+    if not isinstance(local_context, DropoutContext):
+        dropout = local_context
+        mask = None
+    else:
+        dropout = local_context.dropout
+        dropout *= local_context.scale
+        mask = local_context.mask if local_context.reuse_mask else None
+
+    if dropout > 0 and mask is None:
+        mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool)
+
+    if isinstance(local_context, DropoutContext):
+        if local_context.mask is None:
+            local_context.mask = mask
+
+    return mask, dropout
+
+
+# Copied from transformers.models.deberta.modeling_deberta.XDropout
+class XDropout(torch.autograd.Function):
+    """Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
+
+    @staticmethod
+    def forward(ctx, input, local_ctx):
+        mask, dropout = get_mask(input, local_ctx)
+        ctx.scale = 1.0 / (1 - dropout)
+        if dropout > 0:
+            ctx.save_for_backward(mask)
+            return input.masked_fill(mask, 0) * ctx.scale
+        else:
+            return input
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.scale > 1:
+            (mask,) = ctx.saved_tensors
+            return grad_output.masked_fill(mask, 0) * ctx.scale, None
+        else:
+            return grad_output, None
+
+    @staticmethod
+    def symbolic(g: torch._C.Graph, input: torch._C.Value, local_ctx: Union[float, DropoutContext]) -> torch._C.Value:
+        from torch.onnx import symbolic_opset12
+
+        dropout_p = local_ctx
+        if isinstance(local_ctx, DropoutContext):
+            dropout_p = local_ctx.dropout
+        # StableDropout only calls this function when training.
+        train = True
+        # TODO: We should check if the opset_version being used to export
+        # is > 12 here, but there's no good way to do that. As-is, if the
+        # opset_version < 12, export will fail with a CheckerError.
+        # Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like:
+        # if opset_version < 12:
+        #   return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train)
+        return symbolic_opset12.dropout(g, input, dropout_p, train)
+
+
+# Copied from transformers.models.deberta.modeling_deberta.StableDropout
+class StableDropout(nn.Module):
+    """
+    Optimized dropout module for stabilizing the training
+
+    Args:
+        drop_prob (float): the dropout probabilities
+    """
+
+    def __init__(self, drop_prob):
+        super().__init__()
+        self.drop_prob = drop_prob
+        self.count = 0
+        self.context_stack = None
+
+    def forward(self, x):
+        """
+        Call the module
+
+        Args:
+            x (`torch.tensor`): The input tensor to apply dropout
+        """
+        if self.training and self.drop_prob > 0:
+            return XDropout.apply(x, self.get_context())
+        return x
+
+    def clear_context(self):
+        self.count = 0
+        self.context_stack = None
+
+    def init_context(self, reuse_mask=True, scale=1):
+        if self.context_stack is None:
+            self.context_stack = []
+        self.count = 0
+        for c in self.context_stack:
+            c.reuse_mask = reuse_mask
+            c.scale = scale
+
+    def get_context(self):
+        if self.context_stack is not None:
+            if self.count >= len(self.context_stack):
+                self.context_stack.append(DropoutContext())
+            ctx = self.context_stack[self.count]
+            ctx.dropout = self.drop_prob
+            self.count += 1
+            return ctx
+        else:
+            return self.drop_prob
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm
+class DebertaV2SelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
+        self.dropout = StableDropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2
+class DebertaV2Attention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = DisentangledSelfAttention(config)
+        self.output = DebertaV2SelfOutput(config)
+        self.config = config
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_attentions=False,
+        query_states=None,
+        relative_pos=None,
+        rel_embeddings=None,
+    ):
+        self_output = self.self(
+            hidden_states,
+            attention_mask,
+            output_attentions,
+            query_states=query_states,
+            relative_pos=relative_pos,
+            rel_embeddings=rel_embeddings,
+        )
+        if output_attentions:
+            self_output, att_matrix = self_output
+        if query_states is None:
+            query_states = hidden_states
+        attention_output = self.output(self_output, query_states)
+
+        if output_attentions:
+            return (attention_output, att_matrix)
+        else:
+            return attention_output
+
+
+# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2
+class DebertaV2Intermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm
+class DebertaV2Output(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
+        self.dropout = StableDropout(config.hidden_dropout_prob)
+        self.config = config
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2
+class DebertaV2Layer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attention = DebertaV2Attention(config)
+        self.intermediate = DebertaV2Intermediate(config)
+        self.output = DebertaV2Output(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        query_states=None,
+        relative_pos=None,
+        rel_embeddings=None,
+        output_attentions=False,
+    ):
+        attention_output = self.attention(
+            hidden_states,
+            attention_mask,
+            output_attentions=output_attentions,
+            query_states=query_states,
+            relative_pos=relative_pos,
+            rel_embeddings=rel_embeddings,
+        )
+        if output_attentions:
+            attention_output, att_matrix = attention_output
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        if output_attentions:
+            return (layer_output, att_matrix)
+        else:
+            return layer_output
+
+
+class ConvLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        kernel_size = getattr(config, "conv_kernel_size", 3)
+        groups = getattr(config, "conv_groups", 1)
+        self.conv_act = getattr(config, "conv_act", "tanh")
+        self.conv = nn.Conv1d(
+            config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups
+        )
+        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
+        self.dropout = StableDropout(config.hidden_dropout_prob)
+        self.config = config
+
+    def forward(self, hidden_states, residual_states, input_mask):
+        out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()
+        rmask = (1 - input_mask).bool()
+        out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)
+        out = ACT2FN[self.conv_act](self.dropout(out))
+
+        layer_norm_input = residual_states + out
+        output = self.LayerNorm(layer_norm_input).to(layer_norm_input)
+
+        if input_mask is None:
+            output_states = output
+        else:
+            if input_mask.dim() != layer_norm_input.dim():
+                if input_mask.dim() == 4:
+                    input_mask = input_mask.squeeze(1).squeeze(1)
+                input_mask = input_mask.unsqueeze(2)
+
+            input_mask = input_mask.to(output.dtype)
+            output_states = output * input_mask
+
+        return output_states
+
+
+class DebertaV2Encoder(nn.Module):
+    """Modified BertEncoder with relative position bias support"""
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])
+        self.relative_attention = getattr(config, "relative_attention", False)
+
+        if self.relative_attention:
+            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
+            if self.max_relative_positions < 1:
+                self.max_relative_positions = config.max_position_embeddings
+
+            self.position_buckets = getattr(config, "position_buckets", -1)
+            pos_ebd_size = self.max_relative_positions * 2
+
+            if self.position_buckets > 0:
+                pos_ebd_size = self.position_buckets * 2
+
+            self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)
+
+        self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")]
+
+        if "layer_norm" in self.norm_rel_ebd:
+            self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)
+
+        self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None
+        self.gradient_checkpointing = False
+
+    def get_rel_embedding(self):
+        rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None
+        if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd):
+            rel_embeddings = self.LayerNorm(rel_embeddings)
+        return rel_embeddings
+
+    def get_attention_mask(self, attention_mask):
+        if attention_mask.dim() <= 2:
+            extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)
+        elif attention_mask.dim() == 3:
+            attention_mask = attention_mask.unsqueeze(1)
+
+        return attention_mask
+
+    def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):
+        if self.relative_attention and relative_pos is None:
+            q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)
+            relative_pos = build_relative_position(
+                q,
+                hidden_states.size(-2),
+                bucket_size=self.position_buckets,
+                max_position=self.max_relative_positions,
+                device=hidden_states.device,
+            )
+        return relative_pos
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_hidden_states=True,
+        output_attentions=False,
+        query_states=None,
+        relative_pos=None,
+        return_dict=True,
+    ):
+        if attention_mask.dim() <= 2:
+            input_mask = attention_mask
+        else:
+            input_mask = attention_mask.sum(-2) > 0
+        attention_mask = self.get_attention_mask(attention_mask)
+        relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        if isinstance(hidden_states, Sequence):
+            next_kv = hidden_states[0]
+        else:
+            next_kv = hidden_states
+        rel_embeddings = self.get_rel_embedding()
+        output_states = next_kv
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (output_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                output_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    next_kv,
+                    attention_mask,
+                    query_states,
+                    relative_pos,
+                    rel_embeddings,
+                )
+            else:
+                output_states = layer_module(
+                    next_kv,
+                    attention_mask,
+                    query_states=query_states,
+                    relative_pos=relative_pos,
+                    rel_embeddings=rel_embeddings,
+                    output_attentions=output_attentions,
+                )
+
+            if output_attentions:
+                output_states, att_m = output_states
+
+            if i == 0 and self.conv is not None:
+                output_states = self.conv(hidden_states, output_states, input_mask)
+
+            if query_states is not None:
+                query_states = output_states
+                if isinstance(hidden_states, Sequence):
+                    next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None
+            else:
+                next_kv = output_states
+
+            if output_attentions:
+                all_attentions = all_attentions + (att_m,)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (output_states,)
+
+        if not return_dict:
+            return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions
+        )
+
+
+def make_log_bucket_position(relative_pos, bucket_size, max_position):
+    sign = torch.sign(relative_pos)
+    mid = bucket_size // 2
+    abs_pos = torch.where(
+        (relative_pos < mid) & (relative_pos > -mid),
+        torch.tensor(mid - 1).type_as(relative_pos),
+        torch.abs(relative_pos),
+    )
+    log_pos = (
+        torch.ceil(torch.log(abs_pos / mid) / torch.log(torch.tensor((max_position - 1) / mid)) * (mid - 1)) + mid
+    )
+    bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos), log_pos * sign)
+    return bucket_pos
+
+
+def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1, device=None):
+    """
+    Build relative position according to the query and key
+
+    We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
+    \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q -
+    P_k\\)
+
+    Args:
+        query_size (int): the length of query
+        key_size (int): the length of key
+        bucket_size (int): the size of position bucket
+        max_position (int): the maximum allowed absolute position
+        device (`torch.device`): the device on which tensors will be created.
+
+    Return:
+        `torch.LongTensor`: A tensor with shape [1, query_size, key_size]
+    """
+
+    q_ids = torch.arange(0, query_size, device=device)
+    k_ids = torch.arange(0, key_size, device=device)
+    rel_pos_ids = q_ids[:, None] - k_ids[None, :]
+    if bucket_size > 0 and max_position > 0:
+        rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)
+    rel_pos_ids = rel_pos_ids.to(torch.long)
+    rel_pos_ids = rel_pos_ids[:query_size, :]
+    rel_pos_ids = rel_pos_ids.unsqueeze(0)
+    return rel_pos_ids
+
+
+@torch.jit.script
+# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand
+def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):
+    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])
+
+
+@torch.jit.script
+# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand
+def p2c_dynamic_expand(c2p_pos, query_layer, key_layer):
+    return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])
+
+
+@torch.jit.script
+# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand
+def pos_dynamic_expand(pos_index, p2c_att, key_layer):
+    return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))
+
+
+class DisentangledSelfAttention(nn.Module):
+    """
+    Disentangled self-attention module
+
+    Parameters:
+        config (`DebertaV2Config`):
+            A model config class instance with the configuration to build a new model. The schema is similar to
+            *BertConfig*, for more details, please refer [`DebertaV2Config`]
+
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+        self.num_attention_heads = config.num_attention_heads
+        _attention_head_size = config.hidden_size // config.num_attention_heads
+        self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+        self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+        self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+
+        self.share_att_key = getattr(config, "share_att_key", False)
+        self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []
+        self.relative_attention = getattr(config, "relative_attention", False)
+
+        if self.relative_attention:
+            self.position_buckets = getattr(config, "position_buckets", -1)
+            self.max_relative_positions = getattr(config, "max_relative_positions", -1)
+            if self.max_relative_positions < 1:
+                self.max_relative_positions = config.max_position_embeddings
+            self.pos_ebd_size = self.max_relative_positions
+            if self.position_buckets > 0:
+                self.pos_ebd_size = self.position_buckets
+
+            self.pos_dropout = StableDropout(config.hidden_dropout_prob)
+
+            if not self.share_att_key:
+                if "c2p" in self.pos_att_type:
+                    self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+                if "p2c" in self.pos_att_type:
+                    self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = StableDropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x, attention_heads):
+        new_x_shape = x.size()[:-1] + (attention_heads, -1)
+        x = x.view(new_x_shape)
+        return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1))
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        output_attentions=False,
+        query_states=None,
+        relative_pos=None,
+        rel_embeddings=None,
+    ):
+        """
+        Call the module
+
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                Input states to the module usually the output from previous layer, it will be the Q,K and V in
+                *Attention(Q,K,V)*
+
+            attention_mask (`torch.BoolTensor`):
+                An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum
+                sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j*
+                th token.
+
+            output_attentions (`bool`, optional):
+                Whether return the attention matrix.
+
+            query_states (`torch.FloatTensor`, optional):
+                The *Q* state in *Attention(Q,K,V)*.
+
+            relative_pos (`torch.LongTensor`):
+                The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with
+                values ranging in [*-max_relative_positions*, *max_relative_positions*].
+
+            rel_embeddings (`torch.FloatTensor`):
+                The embedding of relative distances. It's a tensor of shape [\\(2 \\times
+                \\text{max_relative_positions}\\), *hidden_size*].
+
+
+        """
+        if query_states is None:
+            query_states = hidden_states
+        query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads)
+        key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads)
+        value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads)
+
+        rel_att = None
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        scale_factor = 1
+        if "c2p" in self.pos_att_type:
+            scale_factor += 1
+        if "p2c" in self.pos_att_type:
+            scale_factor += 1
+        scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
+        attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale.to(dtype=query_layer.dtype)
+        if self.relative_attention:
+            rel_embeddings = self.pos_dropout(rel_embeddings)
+            rel_att = self.disentangled_attention_bias(
+                query_layer, key_layer, relative_pos, rel_embeddings, scale_factor
+            )
+
+        if rel_att is not None:
+            attention_scores = attention_scores + rel_att
+        attention_scores = attention_scores
+        attention_scores = attention_scores.view(
+            -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)
+        )
+
+        # bsz x height x length x dimension
+        attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)
+        attention_probs = self.dropout(attention_probs)
+        context_layer = torch.bmm(
+            attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer
+        )
+        context_layer = (
+            context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1))
+            .permute(0, 2, 1, 3)
+            .contiguous()
+        )
+        new_context_layer_shape = context_layer.size()[:-2] + (-1,)
+        context_layer = context_layer.view(new_context_layer_shape)
+        if output_attentions:
+            return (context_layer, attention_probs)
+        else:
+            return context_layer
+
+    def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
+        if relative_pos is None:
+            q = query_layer.size(-2)
+            relative_pos = build_relative_position(
+                q,
+                key_layer.size(-2),
+                bucket_size=self.position_buckets,
+                max_position=self.max_relative_positions,
+                device=query_layer.device,
+            )
+        if relative_pos.dim() == 2:
+            relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)
+        elif relative_pos.dim() == 3:
+            relative_pos = relative_pos.unsqueeze(1)
+        # bsz x height x query x key
+        elif relative_pos.dim() != 4:
+            raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}")
+
+        att_span = self.pos_ebd_size
+        relative_pos = relative_pos.long().to(query_layer.device)
+
+        rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0)
+        if self.share_att_key:
+            pos_query_layer = self.transpose_for_scores(
+                self.query_proj(rel_embeddings), self.num_attention_heads
+            ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)
+            pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat(
+                query_layer.size(0) // self.num_attention_heads, 1, 1
+            )
+        else:
+            if "c2p" in self.pos_att_type:
+                pos_key_layer = self.transpose_for_scores(
+                    self.pos_key_proj(rel_embeddings), self.num_attention_heads
+                ).repeat(
+                    query_layer.size(0) // self.num_attention_heads, 1, 1
+                )  # .split(self.all_head_size, dim=-1)
+            if "p2c" in self.pos_att_type:
+                pos_query_layer = self.transpose_for_scores(
+                    self.pos_query_proj(rel_embeddings), self.num_attention_heads
+                ).repeat(
+                    query_layer.size(0) // self.num_attention_heads, 1, 1
+                )  # .split(self.all_head_size, dim=-1)
+
+        score = 0
+        # content->position
+        if "c2p" in self.pos_att_type:
+            scale = torch.sqrt(torch.tensor(pos_key_layer.size(-1), dtype=torch.float) * scale_factor)
+            c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))
+            c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)
+            c2p_att = torch.gather(
+                c2p_att,
+                dim=-1,
+                index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),
+            )
+            score += c2p_att / scale.to(dtype=c2p_att.dtype)
+
+        # position->content
+        if "p2c" in self.pos_att_type:
+            scale = torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor)
+            if key_layer.size(-2) != query_layer.size(-2):
+                r_pos = build_relative_position(
+                    key_layer.size(-2),
+                    key_layer.size(-2),
+                    bucket_size=self.position_buckets,
+                    max_position=self.max_relative_positions,
+                    device=query_layer.device,
+                )
+                r_pos = r_pos.unsqueeze(0)
+            else:
+                r_pos = relative_pos
+
+            p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)
+            p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))
+            p2c_att = torch.gather(
+                p2c_att,
+                dim=-1,
+                index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),
+            ).transpose(-1, -2)
+            score += p2c_att / scale.to(dtype=p2c_att.dtype)
+
+        return score
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm
+class DebertaV2Embeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        pad_token_id = getattr(config, "pad_token_id", 0)
+        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
+        self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)
+
+        self.position_biased_input = getattr(config, "position_biased_input", True)
+        if not self.position_biased_input:
+            self.position_embeddings = None
+        else:
+            self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)
+
+        if config.type_vocab_size > 0:
+            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)
+
+        if self.embedding_size != config.hidden_size:
+            self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)
+        self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
+        self.dropout = StableDropout(config.hidden_dropout_prob)
+        self.config = config
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        if self.position_embeddings is not None:
+            position_embeddings = self.position_embeddings(position_ids.long())
+        else:
+            position_embeddings = torch.zeros_like(inputs_embeds)
+
+        embeddings = inputs_embeds
+        if self.position_biased_input:
+            embeddings += position_embeddings
+        if self.config.type_vocab_size > 0:
+            token_type_embeddings = self.token_type_embeddings(token_type_ids)
+            embeddings += token_type_embeddings
+
+        if self.embedding_size != self.config.hidden_size:
+            embeddings = self.embed_proj(embeddings)
+
+        embeddings = self.LayerNorm(embeddings)
+
+        if mask is not None:
+            if mask.dim() != embeddings.dim():
+                if mask.dim() == 4:
+                    mask = mask.squeeze(1).squeeze(1)
+                mask = mask.unsqueeze(2)
+            mask = mask.to(embeddings.dtype)
+
+            embeddings = embeddings * mask
+
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2
+class DebertaV2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = DebertaV2Config
+    base_model_prefix = "deberta"
+    _keys_to_ignore_on_load_missing = ["position_ids"]
+    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, DebertaV2Encoder):
+            module.gradient_checkpointing = value
+
+
+DEBERTA_START_DOCSTRING = r"""
+    The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
+    Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build
+    on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two
+    improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+
+    Parameters:
+        config ([`DebertaV2Config`]): 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.
+"""
+
+DEBERTA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `({0})`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+
+            [What are token type IDs?](../glossary#token-type-ids)
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.",
+    DEBERTA_START_DOCSTRING,
+)
+# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2
+class DebertaV2Model(DebertaV2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embeddings = DebertaV2Embeddings(config)
+        self.encoder = DebertaV2Encoder(config)
+        self.z_steps = 0
+        self.config = config
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, new_embeddings):
+        self.embeddings.word_embeddings = new_embeddings
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        raise NotImplementedError("The prune function is not implemented in DeBERTa model.")
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask,
+            output_hidden_states=True,
+            output_attentions=output_attentions,
+            return_dict=return_dict,
+        )
+        encoded_layers = encoder_outputs[1]
+
+        if self.z_steps > 1:
+            hidden_states = encoded_layers[-2]
+            layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
+            query_states = encoded_layers[-1]
+            rel_embeddings = self.encoder.get_rel_embedding()
+            attention_mask = self.encoder.get_attention_mask(attention_mask)
+            rel_pos = self.encoder.get_rel_pos(embedding_output)
+            for layer in layers[1:]:
+                query_states = layer(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions=False,
+                    query_states=query_states,
+                    relative_pos=rel_pos,
+                    rel_embeddings=rel_embeddings,
+                )
+                encoded_layers.append(query_states)
+
+        sequence_output = encoded_layers[-1]
+
+        if not return_dict:
+            return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
+
+        return BaseModelOutput(
+            last_hidden_state=sequence_output,
+            hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+@add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
+class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.deberta = DebertaV2Model(config)
+        self.cls = DebertaV2OnlyMLMHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MaskedLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+        mask="[MASK]",
+    )
+    # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM.forward with Deberta->DebertaV2
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
+            config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
+            loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.deberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[1:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta
+class DebertaV2PredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+# copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta
+class DebertaV2LMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = DebertaV2PredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta
+class DebertaV2OnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = DebertaV2LMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+@add_start_docstrings(
+    """
+    DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
+    pooled output) e.g. for GLUE tasks.
+    """,
+    DEBERTA_START_DOCSTRING,
+)
+class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        num_labels = getattr(config, "num_labels", 2)
+        self.num_labels = num_labels
+
+        self.deberta = DebertaV2Model(config)
+        self.pooler = ContextPooler(config)
+        output_dim = self.pooler.output_dim
+
+        self.classifier = nn.Linear(output_dim, num_labels)
+        drop_out = getattr(config, "cls_dropout", None)
+        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
+        self.dropout = StableDropout(drop_out)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.deberta.get_input_embeddings()
+
+    def set_input_embeddings(self, new_embeddings):
+        self.deberta.set_input_embeddings(new_embeddings)
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=SequenceClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.deberta(
+            input_ids,
+            token_type_ids=token_type_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        encoder_layer = outputs[0]
+        pooled_output = self.pooler(encoder_layer)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    # regression task
+                    loss_fn = nn.MSELoss()
+                    logits = logits.view(-1).to(labels.dtype)
+                    loss = loss_fn(logits, labels.view(-1))
+                elif labels.dim() == 1 or labels.size(-1) == 1:
+                    label_index = (labels >= 0).nonzero()
+                    labels = labels.long()
+                    if label_index.size(0) > 0:
+                        labeled_logits = torch.gather(
+                            logits, 0, label_index.expand(label_index.size(0), logits.size(1))
+                        )
+                        labels = torch.gather(labels, 0, label_index.view(-1))
+                        loss_fct = CrossEntropyLoss()
+                        loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
+                    else:
+                        loss = torch.tensor(0).to(logits)
+                else:
+                    log_softmax = nn.LogSoftmax(-1)
+                    loss = -((log_softmax(logits) * labels).sum(-1)).mean()
+            elif self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+        )
+
+
+@add_start_docstrings(
+    """
+    DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+    Named-Entity-Recognition (NER) tasks.
+    """,
+    DEBERTA_START_DOCSTRING,
+)
+# Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2
+class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.deberta = DebertaV2Model(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.deberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+        )
+    
+class TokenClassifierRegressionOutput(ModelOutput):
+    """
+    Base class for outputs of token classification models.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
+            Classification loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`):
+            Classification scores (before SoftMax).
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    values: torch.FloatTensor = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    
+class DebertaV2ForTokenClassificationRegression(DebertaV2PreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = 4
+
+        self.deberta = DebertaV2Model(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        
+        self.hidden1 = nn.Linear(config.hidden_size, config.hidden_size)
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
+        
+        self.hidden2 = nn.Linear(config.hidden_size, config.hidden_size)
+        self.regressor = nn.Linear(config.hidden_size, 1)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TokenClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, TokenClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.deberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        sequence_output = self.dropout(sequence_output)
+        
+        logits = self.classifier(self.hidden1(sequence_output))
+        values = self.regressor(self.hidden2(sequence_output))
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierRegressionOutput(
+            loss=loss, logits=logits, values=values, hidden_states=outputs.hidden_states, attentions=outputs.attentions
+        )
+
+
+@add_start_docstrings(
+    """
+    DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    DEBERTA_START_DOCSTRING,
+)
+class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+
+        self.deberta = DebertaV2Model(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=QuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+        qa_target_start_index=_QA_TARGET_START_INDEX,
+        qa_target_end_index=_QA_TARGET_END_INDEX,
+    )
+    # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        start_positions: Optional[torch.Tensor] = None,
+        end_positions: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.deberta(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[1:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    DeBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
+    softmax) e.g. for RocStories/SWAG tasks.
+    """,
+    DEBERTA_START_DOCSTRING,
+)
+class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        num_labels = getattr(config, "num_labels", 2)
+        self.num_labels = num_labels
+
+        self.deberta = DebertaV2Model(config)
+        self.pooler = ContextPooler(config)
+        output_dim = self.pooler.output_dim
+
+        self.classifier = nn.Linear(output_dim, 1)
+        drop_out = getattr(config, "cls_dropout", None)
+        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
+        self.dropout = StableDropout(drop_out)
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.deberta.get_input_embeddings()
+
+    def set_input_embeddings(self, new_embeddings):
+        self.deberta.set_input_embeddings(new_embeddings)
+
+    @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=MultipleChoiceModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MultipleChoiceModelOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+            `input_ids` above)
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+
+        flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+        flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
+        flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
+        flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
+        flat_inputs_embeds = (
+            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+            if inputs_embeds is not None
+            else None
+        )
+
+        outputs = self.deberta(
+            flat_input_ids,
+            position_ids=flat_position_ids,
+            token_type_ids=flat_token_type_ids,
+            attention_mask=flat_attention_mask,
+            inputs_embeds=flat_inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        encoder_layer = outputs[0]
+        pooled_output = self.pooler(encoder_layer)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        reshaped_logits = logits.view(-1, num_choices)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(reshaped_logits, labels)
+
+        if not return_dict:
+            output = (reshaped_logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MultipleChoiceModelOutput(
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

.ipynb_checkpoints/models-checkpoint.py

@@ -28,711 +28,713 @@ from diffusers import AutoencoderKL as DiffuserAutoencoderKL
 from layers.layers import chord_tokenizer, beat_tokenizer, Chord_Embedding, Beat_Embedding, Music_PositionalEncoding, Fundamental_Music_Embedding
 
 def build_pretrained_models(name):
-	checkpoint = torch.load(get_metadata()[name]["path"], map_location="cpu")
-	scale_factor = checkpoint["state_dict"]["scale_factor"].item()
+    checkpoint = torch.load(get_metadata()[name]["path"], map_location="cpu")
+    scale_factor = checkpoint["state_dict"]["scale_factor"].item()
 
-	vae_state_dict = {k[18:]: v for k, v in checkpoint["state_dict"].items() if "first_stage_model." in k}
+    vae_state_dict = {k[18:]: v for k, v in checkpoint["state_dict"].items() if "first_stage_model." in k}
 
-	config = default_audioldm_config(name)
-	vae_config = config["model"]["params"]["first_stage_config"]["params"]
-	vae_config["scale_factor"] = scale_factor
+    config = default_audioldm_config(name)
+    vae_config = config["model"]["params"]["first_stage_config"]["params"]
+    vae_config["scale_factor"] = scale_factor
 
-	vae = AutoencoderKL(**vae_config)
-	vae.load_state_dict(vae_state_dict)
+    vae = AutoencoderKL(**vae_config)
+    vae.load_state_dict(vae_state_dict)
 
-	fn_STFT = TacotronSTFT(
-		config["preprocessing"]["stft"]["filter_length"],
-		config["preprocessing"]["stft"]["hop_length"],
-		config["preprocessing"]["stft"]["win_length"],
-		config["preprocessing"]["mel"]["n_mel_channels"],
-		config["preprocessing"]["audio"]["sampling_rate"],
-		config["preprocessing"]["mel"]["mel_fmin"],
-		config["preprocessing"]["mel"]["mel_fmax"],
-	)
+    fn_STFT = TacotronSTFT(
+        config["preprocessing"]["stft"]["filter_length"],
+        config["preprocessing"]["stft"]["hop_length"],
+        config["preprocessing"]["stft"]["win_length"],
+        config["preprocessing"]["mel"]["n_mel_channels"],
+        config["preprocessing"]["audio"]["sampling_rate"],
+        config["preprocessing"]["mel"]["mel_fmin"],
+        config["preprocessing"]["mel"]["mel_fmax"],
+    )
 
-	vae.eval()
-	fn_STFT.eval()
-	return vae, fn_STFT
+    vae.eval()
+    fn_STFT.eval()
+    return vae, fn_STFT
 
 
 class AudioDiffusion(nn.Module):
-	def __init__(
-		self,
-		text_encoder_name,
-		scheduler_name,
-		unet_model_name=None,
-		unet_model_config_path=None,
-		snr_gamma=None,
-		freeze_text_encoder=True,
-		uncondition=False,
-
-	):
-		super().__init__()
-
-		assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
-
-		self.text_encoder_name = text_encoder_name
-		self.scheduler_name = scheduler_name
-		self.unet_model_name = unet_model_name
-		self.unet_model_config_path = unet_model_config_path
-		self.snr_gamma = snr_gamma
-		self.freeze_text_encoder = freeze_text_encoder
-		self.uncondition = uncondition
-
-		# https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview
-		self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-		self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-
-		if unet_model_config_path:
-			unet_config = UNet2DConditionModel.load_config(unet_model_config_path)
-			self.unet = UNet2DConditionModel.from_config(unet_config, subfolder="unet")
-			self.set_from = "random"
-			print("UNet initialized randomly.")
-		else:
-			self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet")
-			self.set_from = "pre-trained"
-			self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4))
-			self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8))
-			print("UNet initialized from stable diffusion checkpoint.")
-
-		if "stable-diffusion" in self.text_encoder_name:
-			self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer")
-			self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder")
-		elif "t5" in self.text_encoder_name:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name)
-		else:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name)
-
-	def compute_snr(self, timesteps):
-		"""
-		Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-		"""
-		alphas_cumprod = self.noise_scheduler.alphas_cumprod
-		sqrt_alphas_cumprod = alphas_cumprod**0.5
-		sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-		# Expand the tensors.
-		# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
-		sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
-		alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
-
-		sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
-		sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
-
-		# Compute SNR.
-		snr = (alpha / sigma) ** 2
-		return snr
-
-	def encode_text(self, prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
-
-		if self.freeze_text_encoder:
-			with torch.no_grad():
-				encoder_hidden_states = self.text_encoder(
-					input_ids=input_ids, attention_mask=attention_mask
-				)[0]
-		else:
-			encoder_hidden_states = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-
-		boolean_encoder_mask = (attention_mask == 1).to(device)
-		return encoder_hidden_states, boolean_encoder_mask
-
-	def forward(self, latents, prompt, validation_mode=False):
-		device = self.text_encoder.device
-		num_train_timesteps = self.noise_scheduler.num_train_timesteps
-		self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
-
-		encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt)
-
-		if self.uncondition:
-			mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1]
-			if len(mask_indices) > 0:
-				encoder_hidden_states[mask_indices] = 0
-
-		bsz = latents.shape[0]
-
-		if validation_mode:
-			timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
-		else:
-			# Sample a random timestep for each instance
-			timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
-			# print('in if ', timesteps)
-		timesteps = timesteps.long()
-		# print('outside if ' , timesteps)
-		noise = torch.randn_like(latents)
-		noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
-
-		# Get the target for loss depending on the prediction type
-		if self.noise_scheduler.config.prediction_type == "epsilon":
-			target = noise
-		elif self.noise_scheduler.config.prediction_type == "v_prediction":
-			target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
-		else:
-			raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
-
-		if self.set_from == "random":
-			model_pred = self.unet(
-				noisy_latents, timesteps, encoder_hidden_states, 
-				encoder_attention_mask=boolean_encoder_mask
-			).sample
-
-		elif self.set_from == "pre-trained":
-			compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-			model_pred = self.unet(
-				compressed_latents, timesteps, encoder_hidden_states, 
-				encoder_attention_mask=boolean_encoder_mask
-			).sample
-			model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-
-		if self.snr_gamma is None:
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-		else:
-			# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-			# Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
-			snr = self.compute_snr(timesteps)
-			mse_loss_weights = (
-				torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-			)
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-			loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-			loss = loss.mean()
-
-		return loss
-
-	@torch.no_grad()
-	def inference(self, prompt, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
-					disable_progress=True):
-		device = self.text_encoder.device
-		classifier_free_guidance = guidance_scale > 1.0
-		batch_size = len(prompt) * num_samples_per_prompt
-
-		if classifier_free_guidance:
-			prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt)
-		else:
-			prompt_embeds, boolean_prompt_mask = self.encode_text(prompt)
-			prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-			boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		inference_scheduler.set_timesteps(num_steps, device=device)
-		timesteps = inference_scheduler.timesteps
-
-		num_channels_latents = self.unet.in_channels
-		latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
-
-		num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
-		progress_bar = tqdm(range(num_steps), disable=disable_progress)
-
-		for i, t in enumerate(timesteps):
-			# expand the latents if we are doing classifier free guidance
-			latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
-			latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
-
-			noise_pred = self.unet(
-				latent_model_input, t, encoder_hidden_states=prompt_embeds,
-				encoder_attention_mask=boolean_prompt_mask
-			).sample
-
-			# perform guidance
-			if classifier_free_guidance:
-				noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-				noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-			# compute the previous noisy sample x_t -> x_t-1
-			latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
-
-			# call the callback, if provided
-			if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
-				progress_bar.update(1)
-
-		if self.set_from == "pre-trained":
-			latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-		return latents
-
-	def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
-		shape = (batch_size, num_channels_latents, 256, 16)
-		latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
-		# scale the initial noise by the standard deviation required by the scheduler
-		latents = latents * inference_scheduler.init_noise_sigma
-		return latents
-
-	def encode_text_classifier_free(self, prompt, num_samples_per_prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			prompt_embeds = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-				
-		prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# get unconditional embeddings for classifier free guidance
-		uncond_tokens = [""] * len(prompt)
-
-		max_length = prompt_embeds.shape[1]
-		uncond_batch = self.tokenizer(
-			uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
-		)
-		uncond_input_ids = uncond_batch.input_ids.to(device)
-		uncond_attention_mask = uncond_batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			negative_prompt_embeds = self.text_encoder(
-				input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
-			)[0]
-				
-		negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# For classifier free guidance, we need to do two forward passes.
-		# We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
-		prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-		prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
-		boolean_prompt_mask = (prompt_mask == 1).to(device)
-
-		return prompt_embeds, boolean_prompt_mask
-	
+    def __init__(
+        self,
+        text_encoder_name,
+        scheduler_name,
+        unet_model_name=None,
+        unet_model_config_path=None,
+        snr_gamma=None,
+        freeze_text_encoder=True,
+        uncondition=False,
+
+    ):
+        super().__init__()
+
+        assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
+
+        self.text_encoder_name = text_encoder_name
+        self.scheduler_name = scheduler_name
+        self.unet_model_name = unet_model_name
+        self.unet_model_config_path = unet_model_config_path
+        self.snr_gamma = snr_gamma
+        self.freeze_text_encoder = freeze_text_encoder
+        self.uncondition = uncondition
+
+        # https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview
+        self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
+        self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
+
+        if unet_model_config_path:
+            unet_config = UNet2DConditionModel.load_config(unet_model_config_path)
+            self.unet = UNet2DConditionModel.from_config(unet_config, subfolder="unet")
+            self.set_from = "random"
+            print("UNet initialized randomly.")
+        else:
+            self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet")
+            self.set_from = "pre-trained"
+            self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4))
+            self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8))
+            print("UNet initialized from stable diffusion checkpoint.")
+
+        if "stable-diffusion" in self.text_encoder_name:
+            self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer")
+            self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder")
+        elif "t5" in self.text_encoder_name:
+            self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
+            self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name)
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
+            self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name)
+
+    def compute_snr(self, timesteps):
+        """
+        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+        """
+        alphas_cumprod = self.noise_scheduler.alphas_cumprod
+        sqrt_alphas_cumprod = alphas_cumprod**0.5
+        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+        # Expand the tensors.
+        # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
+        sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
+        while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+            sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+        alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
+        while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+            sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+        sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+        # Compute SNR.
+        snr = (alpha / sigma) ** 2
+        return snr
+
+    def encode_text(self, prompt):
+        device = self.text_encoder.device
+        batch = self.tokenizer(
+            prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
+        )
+        input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
+
+        if self.freeze_text_encoder:
+            with torch.no_grad():
+                encoder_hidden_states = self.text_encoder(
+                    input_ids=input_ids, attention_mask=attention_mask
+                )[0]
+        else:
+            encoder_hidden_states = self.text_encoder(
+                input_ids=input_ids, attention_mask=attention_mask
+            )[0]
+
+        boolean_encoder_mask = (attention_mask == 1).to(device)
+        return encoder_hidden_states, boolean_encoder_mask
+
+    def forward(self, latents, prompt, validation_mode=False):
+        device = self.text_encoder.device
+        num_train_timesteps = self.noise_scheduler.num_train_timesteps
+        self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
+
+        encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt)
+
+        if self.uncondition:
+            mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1]
+            if len(mask_indices) > 0:
+                encoder_hidden_states[mask_indices] = 0
+
+        bsz = latents.shape[0]
+
+        if validation_mode:
+            timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
+        else:
+            # Sample a random timestep for each instance
+            timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
+            # print('in if ', timesteps)
+        timesteps = timesteps.long()
+        # print('outside if ' , timesteps)
+        noise = torch.randn_like(latents)
+        noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # Get the target for loss depending on the prediction type
+        if self.noise_scheduler.config.prediction_type == "epsilon":
+            target = noise
+        elif self.noise_scheduler.config.prediction_type == "v_prediction":
+            target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
+        else:
+            raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
+
+        if self.set_from == "random":
+            model_pred = self.unet(
+                noisy_latents, timesteps, encoder_hidden_states, 
+                encoder_attention_mask=boolean_encoder_mask
+            ).sample
+
+        elif self.set_from == "pre-trained":
+            compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
+            model_pred = self.unet(
+                compressed_latents, timesteps, encoder_hidden_states, 
+                encoder_attention_mask=boolean_encoder_mask
+            ).sample
+            model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
+
+        if self.snr_gamma is None:
+            loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+        else:
+            # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
+            # Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
+            snr = self.compute_snr(timesteps)
+            mse_loss_weights = (
+                torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
+            )
+            loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
+            loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
+            loss = loss.mean()
+
+        return loss
+
+    @torch.no_grad()
+    def inference(self, prompt, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
+                    disable_progress=True):
+        device = self.text_encoder.device
+        classifier_free_guidance = guidance_scale > 1.0
+        batch_size = len(prompt) * num_samples_per_prompt
+
+        if classifier_free_guidance:
+            prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt)
+        else:
+            prompt_embeds, boolean_prompt_mask = self.encode_text(prompt)
+            prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
+            boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        inference_scheduler.set_timesteps(num_steps, device=device)
+        timesteps = inference_scheduler.timesteps
+
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
+
+        num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
+        progress_bar = tqdm(range(num_steps), disable=disable_progress)
+
+        for i, t in enumerate(timesteps):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
+            latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
+
+            noise_pred = self.unet(
+                latent_model_input, t, encoder_hidden_states=prompt_embeds,
+                encoder_attention_mask=boolean_prompt_mask
+            ).sample
+
+            # perform guidance
+            if classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
+
+            # call the callback, if provided
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
+                progress_bar.update(1)
+
+        if self.set_from == "pre-trained":
+            latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
+        return latents
+
+    def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
+        shape = (batch_size, num_channels_latents, 256, 16)
+        latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * inference_scheduler.init_noise_sigma
+        return latents
+
+    def encode_text_classifier_free(self, prompt, num_samples_per_prompt):
+        device = self.text_encoder.device
+        batch = self.tokenizer(
+            prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
+        )
+        input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
+
+        with torch.no_grad():
+            prompt_embeds = self.text_encoder(
+                input_ids=input_ids, attention_mask=attention_mask
+            )[0]
+                
+        prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
+        attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        # get unconditional embeddings for classifier free guidance
+        uncond_tokens = [""] * len(prompt)
+
+        max_length = prompt_embeds.shape[1]
+        uncond_batch = self.tokenizer(
+            uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
+        )
+        uncond_input_ids = uncond_batch.input_ids.to(device)
+        uncond_attention_mask = uncond_batch.attention_mask.to(device)
+
+        with torch.no_grad():
+            negative_prompt_embeds = self.text_encoder(
+                input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
+            )[0]
+                
+        negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
+        uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        # For classifier free guidance, we need to do two forward passes.
+        # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
+        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+        prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
+        boolean_prompt_mask = (prompt_mask == 1).to(device)
+
+        return prompt_embeds, boolean_prompt_mask
+    
 
 class MusicAudioDiffusion(nn.Module):
-	def __init__(
-		self,
-		text_encoder_name,
-		scheduler_name,
-		unet_model_name=None,
-		unet_model_config_path=None,
-		snr_gamma=None,
-		freeze_text_encoder=True,
-		uncondition=False,
-
-		d_fme = 1024,  #FME
-		fme_type = "se", 
-		base = 1, 
-		if_trainable = True, 
-		translation_bias_type = "nd",
-		emb_nn = True,
-		d_pe = 1024, #PE
-		if_index = True, 
-		if_global_timing = True,
-		if_modulo_timing = False,
-		d_beat = 1024, #Beat
-		d_oh_beat_type = 7, 
-		beat_len = 50,
-		d_chord = 1024, #Chord
-		d_oh_chord_type = 12,
-		d_oh_inv_type = 4,
-		chord_len = 20,
-
-	):
-		super().__init__()
-
-		assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
-
-		self.text_encoder_name = text_encoder_name
-		self.scheduler_name = scheduler_name
-		self.unet_model_name = unet_model_name
-		self.unet_model_config_path = unet_model_config_path
-		self.snr_gamma = snr_gamma
-		self.freeze_text_encoder = freeze_text_encoder
-		self.uncondition = uncondition
-
-		# https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview
-		self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-		self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
-
-		if unet_model_config_path:
-			unet_config = UNet2DConditionModelMusic.load_config(unet_model_config_path)
-			self.unet = UNet2DConditionModelMusic.from_config(unet_config, subfolder="unet")
-			self.set_from = "random"
-			print("UNet initialized randomly.")
-		else:
-			self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet")
-			self.set_from = "pre-trained"
-			self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4))
-			self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8))
-			print("UNet initialized from stable diffusion checkpoint.")
-
-		if "stable-diffusion" in self.text_encoder_name:
-			self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer")
-			self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder")
-		elif "t5" in self.text_encoder_name:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name)
-		else:
-			self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
-			self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name)
-
-		self.device = self.text_encoder.device
-		#Music Feature Encoder
-		self.FME = Fundamental_Music_Embedding(d_model = d_fme, base= base, if_trainable = False, type = fme_type,emb_nn=emb_nn,translation_bias_type = translation_bias_type)
-		self.PE = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device)
-		# self.PE2 = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device)
-		self.beat_tokenizer = beat_tokenizer(seq_len_beat=beat_len, if_pad = True)
-		self.beat_embedding_layer = Beat_Embedding(self.PE, d_model = d_beat, d_oh_beat_type = d_oh_beat_type)
-		self.chord_embedding_layer = Chord_Embedding(self.FME, self.PE, d_model = d_chord, d_oh_type = d_oh_chord_type, d_oh_inv = d_oh_inv_type)
-		self.chord_tokenizer = chord_tokenizer(seq_len_chord=chord_len, if_pad = True)
-
-
-	def compute_snr(self, timesteps):
-		"""
-		Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
-		"""
-		alphas_cumprod = self.noise_scheduler.alphas_cumprod
-		sqrt_alphas_cumprod = alphas_cumprod**0.5
-		sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
-
-		# Expand the tensors.
-		# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
-		sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
-		alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
-
-		sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
-		while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
-			sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
-		sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
-
-		# Compute SNR.
-		snr = (alpha / sigma) ** 2
-		return snr
-
-	def encode_text(self, prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) #cuda
-		if self.freeze_text_encoder:
-			with torch.no_grad():
-				encoder_hidden_states = self.text_encoder(
-					input_ids=input_ids, attention_mask=attention_mask
-				)[0] #batch, len_text, dim
-		else:
-			encoder_hidden_states = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-		boolean_encoder_mask = (attention_mask == 1).to(device) ##batch, len_text
-		return encoder_hidden_states, boolean_encoder_mask
-
-	def encode_beats(self, beats): 
-		# device = self.beat_embedding_layer.device
-		out_beat = []
-		out_beat_timing = []
-		out_mask = []
-		for beat in beats:
-			tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
-			out_beat.append(tokenized_beats)
-			out_beat_timing.append(tokenized_beats_timing)
-			out_mask.append(tokenized_beat_mask)
-		out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).cuda(), torch.tensor(out_beat_timing).cuda(), torch.tensor(out_mask).cuda() #batch, len_beat
-		embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing)
-
-		return embedded_beat, out_mask
-
-	def encode_chords(self, chords,chords_time):
-		out_chord_root = []
-		out_chord_type = []
-		out_chord_inv = []
-		out_chord_timing = []
-		out_mask = []
-		for chord, chord_time in zip(chords,chords_time): #batch loop
-			tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
-			out_chord_root.append(tokenized_chord_root)
-			out_chord_type.append(tokenized_chord_type)
-			out_chord_inv.append(tokenized_chord_inv)
-			out_chord_timing.append(tokenized_chord_time)
-			out_mask.append(tokenized_chord_mask)
-		#chords: (B, LEN, 4)
-		out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).cuda(), torch.tensor(out_chord_type).cuda(), torch.tensor(out_chord_inv).cuda(), torch.tensor(out_chord_timing).cuda(), torch.tensor(out_mask).cuda()
-		embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing)
-		return embedded_chord, out_mask
-		# return out_chord_root, out_mask
-
-
-	def forward(self, latents, prompt, beats, chords,chords_time, validation_mode=False):
-		device = self.text_encoder.device
-		num_train_timesteps = self.noise_scheduler.num_train_timesteps
-		self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
-
-		encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt)
-		
-		# with torch.no_grad():
-		encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats
-		encoded_chords, chord_mask = self.encode_chords(chords,chords_time)
-
-
-		if self.uncondition:
-			mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1]
-			if len(mask_indices) > 0:
-				encoder_hidden_states[mask_indices] = 0
-				encoded_chords[mask_indices] = 0
-				encoded_beats[mask_indices] = 0
-
-		bsz = latents.shape[0]
-
-		if validation_mode:
-			timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
-		else:
-			timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
-		
-		
-		timesteps = timesteps.long()
-
-		noise = torch.randn_like(latents)
-		noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
-
-		# Get the target for loss depending on the prediction type
-		if self.noise_scheduler.config.prediction_type == "epsilon":
-			target = noise
-		elif self.noise_scheduler.config.prediction_type == "v_prediction":
-			target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
-		else:
-			raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
-
-		if self.set_from == "random":
-			# model_pred = torch.zeros((bsz,8,256,16)).to(device)
-			model_pred = self.unet(
-				noisy_latents, timesteps, encoder_hidden_states, encoded_beats, encoded_chords,
-				encoder_attention_mask=boolean_encoder_mask, beat_attention_mask = beat_mask, chord_attention_mask = chord_mask
-			).sample
-
-		elif self.set_from == "pre-trained":
-			compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-			model_pred = self.unet(
-				compressed_latents, timesteps, encoder_hidden_states, 
-				encoder_attention_mask=boolean_encoder_mask
-			).sample
-			model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-
-		if self.snr_gamma is None:
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-		else:
-			# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
-			# Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
-			snr = self.compute_snr(timesteps)
-			mse_loss_weights = (
-				torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
-			)
-			loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
-			loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
-			loss = loss.mean()
-
-		return loss
-
-	@torch.no_grad()
-	def inference(self, prompt, beats, chords,chords_time, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
-				  disable_progress=True):
-		device = self.text_encoder.device
-		classifier_free_guidance = guidance_scale > 1.0
-		batch_size = len(prompt) * num_samples_per_prompt
-
-		if classifier_free_guidance:
-			prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt)
-			encoded_beats, beat_mask = self.encode_beats_classifier_free(beats, num_samples_per_prompt) #batch, len_beats, dim; batch, len_beats
-			encoded_chords, chord_mask = self.encode_chords_classifier_free(chords, chords_time, num_samples_per_prompt)
-		else:
-			prompt_embeds, boolean_prompt_mask = self.encode_text(prompt)
-			prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-			boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-			encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats
-			encoded_beats = encoded_beats.repeat_interleave(num_samples_per_prompt, 0)
-			beat_mask = beat_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-			encoded_chords, chord_mask = self.encode_chords(chords,chords_time)
-			encoded_chords = encoded_chords.repeat_interleave(num_samples_per_prompt, 0)
-			chord_mask = chord_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# print(f"encoded_chords:{encoded_chords.shape}, chord_mask:{chord_mask.shape}, prompt_embeds:{prompt_embeds.shape},boolean_prompt_mask:{boolean_prompt_mask.shape} ")
-		inference_scheduler.set_timesteps(num_steps, device=device)
-		timesteps = inference_scheduler.timesteps
-
-		num_channels_latents = self.unet.in_channels
-		latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
-
-		num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
-		progress_bar = tqdm(range(num_steps), disable=disable_progress)
-
-		for i, t in enumerate(timesteps):
-			# expand the latents if we are doing classifier free guidance
-			latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
-			latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
-
-			noise_pred = self.unet(
-				latent_model_input, t, encoder_hidden_states=prompt_embeds,
-				encoder_attention_mask=boolean_prompt_mask, 
-				beat_features = encoded_beats, beat_attention_mask = beat_mask, chord_features = encoded_chords,chord_attention_mask = chord_mask
-			).sample
-
-			# perform guidance
-			if classifier_free_guidance: #should work for beats and chords too
-				noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-				noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-			# compute the previous noisy sample x_t -> x_t-1
-			latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
-
-			# call the callback, if provided
-			if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
-				progress_bar.update(1)
-
-		if self.set_from == "pre-trained":
-			latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
-		return latents
-
-	def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
-		shape = (batch_size, num_channels_latents, 256, 16)
-		latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
-		# scale the initial noise by the standard deviation required by the scheduler
-		latents = latents * inference_scheduler.init_noise_sigma
-		return latents
-
-	def encode_text_classifier_free(self, prompt, num_samples_per_prompt):
-		device = self.text_encoder.device
-		batch = self.tokenizer(
-			prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
-		)
-		input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			prompt_embeds = self.text_encoder(
-				input_ids=input_ids, attention_mask=attention_mask
-			)[0]
-				
-		prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# get unconditional embeddings for classifier free guidance
-		# print(len(prompt), 'this is prompt len')
-		uncond_tokens = [""] * len(prompt)
-
-		max_length = prompt_embeds.shape[1]
-		uncond_batch = self.tokenizer(
-			uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
-		)
-		uncond_input_ids = uncond_batch.input_ids.to(device)
-		uncond_attention_mask = uncond_batch.attention_mask.to(device)
-
-		with torch.no_grad():
-			negative_prompt_embeds = self.text_encoder(
-				input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
-			)[0]
-				
-		negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
-		uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		# For classifier free guidance, we need to do two forward passes.
-		# We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
-		prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-		prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
-		boolean_prompt_mask = (prompt_mask == 1).to(device)
-
-		return prompt_embeds, boolean_prompt_mask
-	
-
-	def encode_beats_classifier_free(self, beats, num_samples_per_prompt):
-		with torch.no_grad():
-			out_beat = []
-			out_beat_timing = []
-			out_mask = []
-			for beat in beats:
-				tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
-				out_beat.append(tokenized_beats)
-				out_beat_timing.append(tokenized_beats_timing)
-				out_mask.append(tokenized_beat_mask)
-			out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).cuda(), torch.tensor(out_beat_timing).cuda(), torch.tensor(out_mask).cuda() #batch, len_beat
-			embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing)	
-			
-		embedded_beat = embedded_beat.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		uncond_beats = [[[],[]]] * len(beats)
-
-		max_length = embedded_beat.shape[1]
-		with torch.no_grad():
-			out_beat_unc = []
-			out_beat_timing_unc = []
-			out_mask_unc = []
-			for beat in uncond_beats:
-				tokenized_beats, tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
-				out_beat_unc.append(tokenized_beats)
-				out_beat_timing_unc.append(tokenized_beats_timing)
-				out_mask_unc.append(tokenized_beat_mask)
-			out_beat_unc, out_beat_timing_unc, out_mask_unc = torch.tensor(out_beat_unc).cuda(), torch.tensor(out_beat_timing_unc).cuda(), torch.tensor(out_mask_unc).cuda() #batch, len_beat
-			embedded_beat_unc = self.beat_embedding_layer(out_beat_unc, out_beat_timing_unc)
-
-		embedded_beat_unc = embedded_beat_unc.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0)
-
-		embedded_beat = torch.cat([embedded_beat_unc, embedded_beat])
-		out_mask = torch.cat([out_mask_unc, out_mask])
-
-		return embedded_beat, out_mask
-
-
-	def encode_chords_classifier_free(self, chords, chords_time, num_samples_per_prompt):
-
-		with torch.no_grad():
-			out_chord_root = []
-			out_chord_type = []
-			out_chord_inv = []
-			out_chord_timing = []
-			out_mask = []
-			for chord, chord_time in zip(chords,chords_time): #batch loop
-				tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
-				out_chord_root.append(tokenized_chord_root)
-				out_chord_type.append(tokenized_chord_type)
-				out_chord_inv.append(tokenized_chord_inv)
-				out_chord_timing.append(tokenized_chord_time)
-				out_mask.append(tokenized_chord_mask)	
-			out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).cuda(), torch.tensor(out_chord_type).cuda(), torch.tensor(out_chord_inv).cuda(), torch.tensor(out_chord_timing).cuda(), torch.tensor(out_mask).cuda()
-			embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing)
-		
-		embedded_chord = embedded_chord.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0)
-
-		chords_unc=[[]] * len(chords)
-		chords_time_unc=[[]] * len(chords_time)
-
-		max_length = embedded_chord.shape[1]
-
-		with torch.no_grad():
-			out_chord_root_unc = []
-			out_chord_type_unc = []
-			out_chord_inv_unc = []
-			out_chord_timing_unc = []
-			out_mask_unc = []
-			for chord, chord_time in zip(chords_unc,chords_time_unc): #batch loop
-				tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
-				out_chord_root_unc.append(tokenized_chord_root)
-				out_chord_type_unc.append(tokenized_chord_type)
-				out_chord_inv_unc.append(tokenized_chord_inv)
-				out_chord_timing_unc.append(tokenized_chord_time)
-				out_mask_unc.append(tokenized_chord_mask)	
-			out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc, out_mask_unc = torch.tensor(out_chord_root_unc).cuda(), torch.tensor(out_chord_type_unc).cuda(), torch.tensor(out_chord_inv_unc).cuda(), torch.tensor(out_chord_timing_unc).cuda(), torch.tensor(out_mask_unc).cuda()
-			embedded_chord_unc = self.chord_embedding_layer(out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc)
-		
-
-		embedded_chord_unc = embedded_chord_unc.repeat_interleave(num_samples_per_prompt, 0)
-		out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0)
-
-		embedded_chord = torch.cat([embedded_chord_unc, embedded_chord])
-		out_mask = torch.cat([out_mask_unc, out_mask])
-
-		return embedded_chord, out_mask
+    def __init__(
+        self,
+        text_encoder_name,
+        scheduler_name,
+        unet_model_name=None,
+        unet_model_config_path=None,
+        snr_gamma=None,
+        freeze_text_encoder=True,
+        uncondition=False,
+
+        d_fme = 1024,  #FME
+        fme_type = "se", 
+        base = 1, 
+        if_trainable = True, 
+        translation_bias_type = "nd",
+        emb_nn = True,
+        d_pe = 1024, #PE
+        if_index = True, 
+        if_global_timing = True,
+        if_modulo_timing = False,
+        d_beat = 1024, #Beat
+        d_oh_beat_type = 7, 
+        beat_len = 50,
+        d_chord = 1024, #Chord
+        d_oh_chord_type = 12,
+        d_oh_inv_type = 4,
+        chord_len = 20,
+
+    ):
+        super().__init__()
+
+        assert unet_model_name is not None or unet_model_config_path is not None, "Either UNet pretrain model name or a config file path is required"
+
+        self.text_encoder_name = text_encoder_name
+        self.scheduler_name = scheduler_name
+        self.unet_model_name = unet_model_name
+        self.unet_model_config_path = unet_model_config_path
+        self.snr_gamma = snr_gamma
+        self.freeze_text_encoder = freeze_text_encoder
+        self.uncondition = uncondition
+
+        # https://huggingface.co/docs/diffusers/v0.14.0/en/api/schedulers/overview
+        self.noise_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
+        self.inference_scheduler = DDPMScheduler.from_pretrained(self.scheduler_name, subfolder="scheduler")
+
+        if unet_model_config_path:
+            unet_config = UNet2DConditionModelMusic.load_config(unet_model_config_path)
+            self.unet = UNet2DConditionModelMusic.from_config(unet_config, subfolder="unet")
+            self.set_from = "random"
+            print("UNet initialized randomly.")
+        else:
+            self.unet = UNet2DConditionModel.from_pretrained(unet_model_name, subfolder="unet")
+            self.set_from = "pre-trained"
+            self.group_in = nn.Sequential(nn.Linear(8, 512), nn.Linear(512, 4))
+            self.group_out = nn.Sequential(nn.Linear(4, 512), nn.Linear(512, 8))
+            print("UNet initialized from stable diffusion checkpoint.")
+
+        if "stable-diffusion" in self.text_encoder_name:
+            self.tokenizer = CLIPTokenizer.from_pretrained(self.text_encoder_name, subfolder="tokenizer")
+            self.text_encoder = CLIPTextModel.from_pretrained(self.text_encoder_name, subfolder="text_encoder")
+        elif "t5" in self.text_encoder_name:
+            self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
+            self.text_encoder = T5EncoderModel.from_pretrained(self.text_encoder_name)
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(self.text_encoder_name)
+            self.text_encoder = AutoModel.from_pretrained(self.text_encoder_name)
+
+        self.device = self.text_encoder.device
+        #Music Feature Encoder
+        self.FME = Fundamental_Music_Embedding(d_model = d_fme, base= base, if_trainable = False, type = fme_type,emb_nn=emb_nn,translation_bias_type = translation_bias_type)
+        self.PE = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device)
+        # self.PE2 = Music_PositionalEncoding(d_model = d_pe, if_index = if_index, if_global_timing = if_global_timing, if_modulo_timing = if_modulo_timing, device = self.device)
+        self.beat_tokenizer = beat_tokenizer(seq_len_beat=beat_len, if_pad = True)
+        self.beat_embedding_layer = Beat_Embedding(self.PE, d_model = d_beat, d_oh_beat_type = d_oh_beat_type)
+        self.chord_embedding_layer = Chord_Embedding(self.FME, self.PE, d_model = d_chord, d_oh_type = d_oh_chord_type, d_oh_inv = d_oh_inv_type)
+        self.chord_tokenizer = chord_tokenizer(seq_len_chord=chord_len, if_pad = True)
+
+
+    def compute_snr(self, timesteps):
+        """
+        Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+        """
+        alphas_cumprod = self.noise_scheduler.alphas_cumprod
+        sqrt_alphas_cumprod = alphas_cumprod**0.5
+        sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+        # Expand the tensors.
+        # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
+        sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
+        while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+            sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+        alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
+        while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+            sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+        sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+        # Compute SNR.
+        snr = (alpha / sigma) ** 2
+        return snr
+
+    def encode_text(self, prompt):
+        device = self.text_encoder.device
+        batch = self.tokenizer(
+            prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
+        )
+        input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device) #cuda
+        if self.freeze_text_encoder:
+            with torch.no_grad():
+                encoder_hidden_states = self.text_encoder(
+                    input_ids=input_ids, attention_mask=attention_mask
+                )[0] #batch, len_text, dim
+        else:
+            encoder_hidden_states = self.text_encoder(
+                input_ids=input_ids, attention_mask=attention_mask
+            )[0]
+        boolean_encoder_mask = (attention_mask == 1).to(device) ##batch, len_text
+        return encoder_hidden_states, boolean_encoder_mask
+
+    def encode_beats(self, beats): 
+        device = self.device
+        out_beat = []
+        out_beat_timing = []
+        out_mask = []
+        for beat in beats:
+            tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
+            out_beat.append(tokenized_beats)
+            out_beat_timing.append(tokenized_beats_timing)
+            out_mask.append(tokenized_beat_mask)
+        out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).to(device), torch.tensor(out_beat_timing).to(device), torch.tensor(out_mask).to(device) #batch, len_beat
+        embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing, device)
+
+        return embedded_beat, out_mask
+
+    def encode_chords(self, chords,chords_time):
+        device = self.device
+        out_chord_root = []
+        out_chord_type = []
+        out_chord_inv = []
+        out_chord_timing = []
+        out_mask = []
+        for chord, chord_time in zip(chords,chords_time): #batch loop
+            tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
+            out_chord_root.append(tokenized_chord_root)
+            out_chord_type.append(tokenized_chord_type)
+            out_chord_inv.append(tokenized_chord_inv)
+            out_chord_timing.append(tokenized_chord_time)
+            out_mask.append(tokenized_chord_mask)
+        #chords: (B, LEN, 4)
+        out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).to(device), torch.tensor(out_chord_type).to(device), torch.tensor(out_chord_inv).to(device), torch.tensor(out_chord_timing).to(device), torch.tensor(out_mask).to(device)
+        embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, device)
+        return embedded_chord, out_mask
+        # return out_chord_root, out_mask
+
+
+    def forward(self, latents, prompt, beats, chords,chords_time, validation_mode=False):
+        device = self.text_encoder.device
+        num_train_timesteps = self.noise_scheduler.num_train_timesteps
+        self.noise_scheduler.set_timesteps(num_train_timesteps, device=device)
+
+        encoder_hidden_states, boolean_encoder_mask = self.encode_text(prompt)
+        
+        # with torch.no_grad():
+        encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats
+        encoded_chords, chord_mask = self.encode_chords(chords,chords_time)
+
+
+        if self.uncondition:
+            mask_indices = [k for k in range(len(prompt)) if random.random() < 0.1]
+            if len(mask_indices) > 0:
+                encoder_hidden_states[mask_indices] = 0
+                encoded_chords[mask_indices] = 0
+                encoded_beats[mask_indices] = 0
+
+        bsz = latents.shape[0]
+
+        if validation_mode:
+            timesteps = (self.noise_scheduler.num_train_timesteps//2) * torch.ones((bsz,), dtype=torch.int64, device=device)
+        else:
+            timesteps = torch.randint(0, self.noise_scheduler.num_train_timesteps, (bsz,), device=device)
+        
+        
+        timesteps = timesteps.long()
+
+        noise = torch.randn_like(latents)
+        noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps)
+
+        # Get the target for loss depending on the prediction type
+        if self.noise_scheduler.config.prediction_type == "epsilon":
+            target = noise
+        elif self.noise_scheduler.config.prediction_type == "v_prediction":
+            target = self.noise_scheduler.get_velocity(latents, noise, timesteps)
+        else:
+            raise ValueError(f"Unknown prediction type {self.noise_scheduler.config.prediction_type}")
+
+        if self.set_from == "random":
+            # model_pred = torch.zeros((bsz,8,256,16)).to(device)
+            model_pred = self.unet(
+                noisy_latents, timesteps, encoder_hidden_states, encoded_beats, encoded_chords,
+                encoder_attention_mask=boolean_encoder_mask, beat_attention_mask = beat_mask, chord_attention_mask = chord_mask
+            ).sample
+
+        elif self.set_from == "pre-trained":
+            compressed_latents = self.group_in(noisy_latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
+            model_pred = self.unet(
+                compressed_latents, timesteps, encoder_hidden_states, 
+                encoder_attention_mask=boolean_encoder_mask
+            ).sample
+            model_pred = self.group_out(model_pred.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
+
+        if self.snr_gamma is None:
+            loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+        else:
+            # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
+            # Adaptef from huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
+            snr = self.compute_snr(timesteps)
+            mse_loss_weights = (
+                torch.stack([snr, self.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
+            )
+            loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
+            loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
+            loss = loss.mean()
+
+        return loss
+
+    @torch.no_grad()
+    def inference(self, prompt, beats, chords,chords_time, inference_scheduler, num_steps=20, guidance_scale=3, num_samples_per_prompt=1, 
+                  disable_progress=True):
+        device = self.text_encoder.device
+        classifier_free_guidance = guidance_scale > 1.0
+        batch_size = len(prompt) * num_samples_per_prompt
+
+        if classifier_free_guidance:
+            prompt_embeds, boolean_prompt_mask = self.encode_text_classifier_free(prompt, num_samples_per_prompt)
+            encoded_beats, beat_mask = self.encode_beats_classifier_free(beats, num_samples_per_prompt) #batch, len_beats, dim; batch, len_beats
+            encoded_chords, chord_mask = self.encode_chords_classifier_free(chords, chords_time, num_samples_per_prompt)
+        else:
+            prompt_embeds, boolean_prompt_mask = self.encode_text(prompt)
+            prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
+            boolean_prompt_mask = boolean_prompt_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+            encoded_beats, beat_mask = self.encode_beats(beats) #batch, len_beats, dim; batch, len_beats
+            encoded_beats = encoded_beats.repeat_interleave(num_samples_per_prompt, 0)
+            beat_mask = beat_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+            encoded_chords, chord_mask = self.encode_chords(chords,chords_time)
+            encoded_chords = encoded_chords.repeat_interleave(num_samples_per_prompt, 0)
+            chord_mask = chord_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        # print(f"encoded_chords:{encoded_chords.shape}, chord_mask:{chord_mask.shape}, prompt_embeds:{prompt_embeds.shape},boolean_prompt_mask:{boolean_prompt_mask.shape} ")
+        inference_scheduler.set_timesteps(num_steps, device=device)
+        timesteps = inference_scheduler.timesteps
+
+        num_channels_latents = self.unet.in_channels
+        latents = self.prepare_latents(batch_size, inference_scheduler, num_channels_latents, prompt_embeds.dtype, device)
+
+        num_warmup_steps = len(timesteps) - num_steps * inference_scheduler.order
+        progress_bar = tqdm(range(num_steps), disable=disable_progress)
+
+        for i, t in enumerate(timesteps):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if classifier_free_guidance else latents
+            latent_model_input = inference_scheduler.scale_model_input(latent_model_input, t)
+
+            noise_pred = self.unet(
+                latent_model_input, t, encoder_hidden_states=prompt_embeds,
+                encoder_attention_mask=boolean_prompt_mask, 
+                beat_features = encoded_beats, beat_attention_mask = beat_mask, chord_features = encoded_chords,chord_attention_mask = chord_mask
+            ).sample
+
+            # perform guidance
+            if classifier_free_guidance: #should work for beats and chords too
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = inference_scheduler.step(noise_pred, t, latents).prev_sample
+
+            # call the callback, if provided
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % inference_scheduler.order == 0):
+                progress_bar.update(1)
+
+        if self.set_from == "pre-trained":
+            latents = self.group_out(latents.permute(0, 2, 3, 1).contiguous()).permute(0, 3, 1, 2).contiguous()
+        return latents
+
+    def prepare_latents(self, batch_size, inference_scheduler, num_channels_latents, dtype, device):
+        shape = (batch_size, num_channels_latents, 256, 16)
+        latents = randn_tensor(shape, generator=None, device=device, dtype=dtype)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * inference_scheduler.init_noise_sigma
+        return latents
+
+    def encode_text_classifier_free(self, prompt, num_samples_per_prompt):
+        device = self.text_encoder.device
+        batch = self.tokenizer(
+            prompt, max_length=self.tokenizer.model_max_length, padding=True, truncation=True, return_tensors="pt"
+        )
+        input_ids, attention_mask = batch.input_ids.to(device), batch.attention_mask.to(device)
+
+        with torch.no_grad():
+            prompt_embeds = self.text_encoder(
+                input_ids=input_ids, attention_mask=attention_mask
+            )[0]
+                
+        prompt_embeds = prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
+        attention_mask = attention_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        # get unconditional embeddings for classifier free guidance
+        # print(len(prompt), 'this is prompt len')
+        uncond_tokens = [""] * len(prompt)
+
+        max_length = prompt_embeds.shape[1]
+        uncond_batch = self.tokenizer(
+            uncond_tokens, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt",
+        )
+        uncond_input_ids = uncond_batch.input_ids.to(device)
+        uncond_attention_mask = uncond_batch.attention_mask.to(device)
+
+        with torch.no_grad():
+            negative_prompt_embeds = self.text_encoder(
+                input_ids=uncond_input_ids, attention_mask=uncond_attention_mask
+            )[0]
+                
+        negative_prompt_embeds = negative_prompt_embeds.repeat_interleave(num_samples_per_prompt, 0)
+        uncond_attention_mask = uncond_attention_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        # For classifier free guidance, we need to do two forward passes.
+        # We concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes
+        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+        prompt_mask = torch.cat([uncond_attention_mask, attention_mask])
+        boolean_prompt_mask = (prompt_mask == 1).to(device)
+
+        return prompt_embeds, boolean_prompt_mask
+    
+
+    def encode_beats_classifier_free(self, beats, num_samples_per_prompt):
+        device = self.device
+        with torch.no_grad():
+            out_beat = []
+            out_beat_timing = []
+            out_mask = []
+            for beat in beats:
+                tokenized_beats,tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
+                out_beat.append(tokenized_beats)
+                out_beat_timing.append(tokenized_beats_timing)
+                out_mask.append(tokenized_beat_mask)
+            out_beat, out_beat_timing, out_mask = torch.tensor(out_beat).to(device), torch.tensor(out_beat_timing).to(device), torch.tensor(out_mask).to(device) #batch, len_beat
+            embedded_beat = self.beat_embedding_layer(out_beat, out_beat_timing, device)    
+            
+        embedded_beat = embedded_beat.repeat_interleave(num_samples_per_prompt, 0)
+        out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        uncond_beats = [[[],[]]] * len(beats)
+
+        max_length = embedded_beat.shape[1]
+        with torch.no_grad():
+            out_beat_unc = []
+            out_beat_timing_unc = []
+            out_mask_unc = []
+            for beat in uncond_beats:
+                tokenized_beats, tokenized_beats_timing, tokenized_beat_mask = self.beat_tokenizer(beat)
+                out_beat_unc.append(tokenized_beats)
+                out_beat_timing_unc.append(tokenized_beats_timing)
+                out_mask_unc.append(tokenized_beat_mask)
+            out_beat_unc, out_beat_timing_unc, out_mask_unc = torch.tensor(out_beat_unc).to(device), torch.tensor(out_beat_timing_unc).to(device), torch.tensor(out_mask_unc).to(device) #batch, len_beat
+            embedded_beat_unc = self.beat_embedding_layer(out_beat_unc, out_beat_timing_unc, device)
+
+        embedded_beat_unc = embedded_beat_unc.repeat_interleave(num_samples_per_prompt, 0)
+        out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0)
+
+        embedded_beat = torch.cat([embedded_beat_unc, embedded_beat])
+        out_mask = torch.cat([out_mask_unc, out_mask])
+
+        return embedded_beat, out_mask
+
+
+    def encode_chords_classifier_free(self, chords, chords_time, num_samples_per_prompt):
+        device = self.device
+        with torch.no_grad():
+            out_chord_root = []
+            out_chord_type = []
+            out_chord_inv = []
+            out_chord_timing = []
+            out_mask = []
+            for chord, chord_time in zip(chords,chords_time): #batch loop
+                tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
+                out_chord_root.append(tokenized_chord_root)
+                out_chord_type.append(tokenized_chord_type)
+                out_chord_inv.append(tokenized_chord_inv)
+                out_chord_timing.append(tokenized_chord_time)
+                out_mask.append(tokenized_chord_mask)    
+            out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, out_mask = torch.tensor(out_chord_root).to(device), torch.tensor(out_chord_type).to(device), torch.tensor(out_chord_inv).to(device), torch.tensor(out_chord_timing).to(device), torch.tensor(out_mask).to(device)
+            embedded_chord = self.chord_embedding_layer(out_chord_root, out_chord_type, out_chord_inv, out_chord_timing, device)
+        
+        embedded_chord = embedded_chord.repeat_interleave(num_samples_per_prompt, 0)
+        out_mask = out_mask.repeat_interleave(num_samples_per_prompt, 0)
+
+        chords_unc=[[]] * len(chords)
+        chords_time_unc=[[]] * len(chords_time)
+
+        max_length = embedded_chord.shape[1]
+
+        with torch.no_grad():
+            out_chord_root_unc = []
+            out_chord_type_unc = []
+            out_chord_inv_unc = []
+            out_chord_timing_unc = []
+            out_mask_unc = []
+            for chord, chord_time in zip(chords_unc,chords_time_unc): #batch loop
+                tokenized_chord_root, tokenized_chord_type, tokenized_chord_inv, tokenized_chord_time, tokenized_chord_mask = self.chord_tokenizer(chord, chord_time)
+                out_chord_root_unc.append(tokenized_chord_root)
+                out_chord_type_unc.append(tokenized_chord_type)
+                out_chord_inv_unc.append(tokenized_chord_inv)
+                out_chord_timing_unc.append(tokenized_chord_time)
+                out_mask_unc.append(tokenized_chord_mask)    
+            out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc, out_mask_unc = torch.tensor(out_chord_root_unc).to(device), torch.tensor(out_chord_type_unc).to(device), torch.tensor(out_chord_inv_unc).to(device), torch.tensor(out_chord_timing_unc).to(device), torch.tensor(out_mask_unc).to(device)
+            embedded_chord_unc = self.chord_embedding_layer(out_chord_root_unc, out_chord_type_unc, out_chord_inv_unc, out_chord_timing_unc, device)
+        
+
+        embedded_chord_unc = embedded_chord_unc.repeat_interleave(num_samples_per_prompt, 0)
+        out_mask_unc = out_mask_unc.repeat_interleave(num_samples_per_prompt, 0)
+
+        embedded_chord = torch.cat([embedded_chord_unc, embedded_chord])
+        out_mask = torch.cat([out_mask_unc, out_mask])
+
+        return embedded_chord, out_mask

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -1,12 +1,12 @@
-torch==1.13.1
-torchaudio==0.13.1
-torchvision==0.14.1
-transformers==4.27.0
-accelerate==0.18.0
+torch==2.0.1
+torchaudio==2.0.2
+torchvision==0.15.2
+transformers==4.31.0
+accelerate==0.21.0
 datasets==2.1.0
 einops==0.6.1
 h5py==3.8.0
-huggingface_hub==0.13.3
+huggingface_hub==0.19.4
 importlib_metadata==6.3.0
 librosa==0.9.2
 matplotlib==3.5.2
@@ -17,6 +17,7 @@ pandas==1.4.1
 progressbar33==2.4
 protobuf==3.20.*
 resampy==0.4.2
+safetensors==0.3.2
 sentencepiece==0.1.99
 scikit_image==0.19.3
 scikit_learn==1.2.2

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import json
 import torch
 import wavio
+import numpy as np
 from tqdm import tqdm
 from huggingface_hub import snapshot_download
 
@@ -23,6 +24,7 @@ class MusicFeaturePredictor:
     def __init__(self, path, device="cuda:0", cache_dir=None, local_files_only=False):
         self.beats_tokenizer = AutoTokenizer.from_pretrained(
             "microsoft/deberta-v3-large",
+            use_fast=False,
             cache_dir=cache_dir,
             local_files_only=local_files_only,
         )
@@ -164,6 +166,7 @@ class Mustango:
             main_config["scheduler_name"],
             unet_model_config_path=f"{path}/configs/music_diffusion_model_config.json",
         ).to(device)
+        self.model.device = device
 
         vae_weights = torch.load(
             f"{path}/vae/pytorch_model_vae.bin", map_location=device
@@ -213,9 +216,11 @@ class Mustango:
 
 # Initialize Mustango
 if torch.cuda.is_available():
-    mustango = Mustango()
+    mustango = Mustango(device="cpu")
 else:
     mustango = Mustango(device="cpu")
+    
+output_wave = mustango.generate("This techno song features a synth lead playing the main melody.", 5, 3, disable_progress=False)
 
 def gradio_generate(prompt, steps, guidance):
     output_wave = mustango.generate(prompt, steps, guidance)
@@ -225,6 +230,7 @@ def gradio_generate(prompt, steps, guidance):
     
     return output_filename
 
+
 # description_text = """
 # <p><a href="https://huggingface.co/spaces/declare-lab/mustango/blob/main/app.py?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> For faster inference without waiting in queue, you may duplicate the space and upgrade to a GPU in the settings. <br/><br/>
 # Generate music using Mustango by providing a text prompt.