





'''simple docstring'''







import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->str:
 snake_case				=			torch.exp(a     )
 snake_case				=			torch.sum(a					,   dim=1     )  # sum of exp(x_i)
 snake_case				=			torch.sum(x * exp_x					,   dim=1     )  # sum of x_i * exp(x_i)
 return torch.log(a     ) - B / A
class 			_lowercase						(       nn.Module					):







     def __init__( self    ,   A__    ) ->	Any:
      super().__init__()
      snake_case				=			config.output_attentions
      snake_case				=			config.output_hidden_states
      snake_case				=			nn.ModuleList([BertLayer(A__    ) for _ in range(config.num_hidden_layers    )]    )
      snake_case				=			nn.ModuleList([BertHighway(A__    ) for _ in range(config.num_hidden_layers    )]    )

      snake_case				=			[-1 for _ in range(config.num_hidden_layers    )]







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      if (type(A__    ) is float) or (type(A__    ) is int):
       for i in range(len(self.early_exit_entropy    )    ):
        snake_case				=			x
      else:
       snake_case				=			x







     def     UpperCamelCase			( self    ,   A__    ) ->	Optional[Any]:
      snake_case				=			pooler.state_dict()
      for highway in self.highway:
       for name, param in highway.pooler.state_dict().items():
        param.copy_(loaded_model[name]    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   ) ->	int:
      snake_case				=			()
      snake_case				=			()
      snake_case				=			()
      for i, layer_module in enumerate(self.layer    ):
       if self.output_hidden_states:
        snake_case				=			all_hidden_states + (hidden_states,)

       snake_case				=			layer_module(
           A__    ,   A__    ,   head_mask[i]    ,   A__    ,   A__    )
       snake_case				=			layer_outputs[0]

       if self.output_attentions:
        snake_case				=			all_attentions + (layer_outputs[1],)

       snake_case				=			(hidden_states,)
       if self.output_hidden_states:
        snake_case				=			current_outputs + (all_hidden_states,)
       if self.output_attentions:
        snake_case				=			current_outputs + (all_attentions,)

       snake_case				=			self.highway[i](A__    )
       # logits, pooled_output

       if not self.training:
        snake_case				=			highway_exit[0]
        snake_case				=			entropy(A__    )
        snake_case				=			highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
        snake_case				=			all_highway_exits + (highway_exit,)

        if highway_entropy < self.early_exit_entropy[i]:
         snake_case				=			(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
         raise HighwayException(A__    ,   i + 1    )
       else:
        snake_case				=			all_highway_exits + (highway_exit,)

        # Add last layer
      if self.output_hidden_states:
       snake_case				=			all_hidden_states + (hidden_states,)

      snake_case				=			(hidden_states,)
      if self.output_hidden_states:
       snake_case				=			outputs + (all_hidden_states,)
      if self.output_attentions:
       snake_case				=			outputs + (all_attentions,)

      snake_case				=			outputs + (all_highway_exits,)
      return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''				,		__a				,		)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ) ->	str:
      super().__init__(A__    )
      snake_case				=			config

      snake_case				=			BertEmbeddings(A__    )
      snake_case				=			DeeBertEncoder(A__    )
      snake_case				=			BertPooler(A__    )

      self.init_weights()







     def     UpperCamelCase			( self    ) ->	Dict:
      self.encoder.init_highway_pooler(self.pooler    )







     def     UpperCamelCase			( self    ) ->	str:
      return self.embeddings.word_embeddings







     def     UpperCamelCase			( self    ,   A__    ) ->	Any:
      snake_case				=			value







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      for layer, heads in heads_to_prune.items():
       self.encoder.layer[layer].attention.prune_heads(A__    )







     @add_start_docstrings_to_model_forward(A__    )
     def     UpperCamelCase			( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   ) ->	str:
      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:
       snake_case				=			input_ids.size()
      elif inputs_embeds is not None:
       snake_case				=			inputs_embeds.size()[:-1]
      else:
       raise ValueError('''You have to specify either input_ids or inputs_embeds'''    )

      snake_case				=			input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
       snake_case				=			torch.ones(A__    ,   device=A__    )
      if encoder_attention_mask is None:
       snake_case				=			torch.ones(A__    ,   device=A__    )
      if token_type_ids is None:
       snake_case				=			torch.zeros(A__    ,   dtype=torch.long    ,   device=A__    )

      # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
      # ourselves in which case we just need to make it broadcastable to all heads.
      snake_case				=			self.get_extended_attention_mask(A__    ,   A__    ,   A__    )

      # If a 2D ou 3D attention mask is provided for the cross-attention
      # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
      if encoder_attention_mask.dim() == 3:
       snake_case				=			encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.dim() == 2:
       snake_case				=			encoder_attention_mask[:, None, None, :]

      snake_case				=			encoder_extended_attention_mask.to(
          dtype=next(self.parameters()    ).dtype    )  # fp16 compatibility
      snake_case				=			(1.0 - encoder_extended_attention_mask) * -1_00_00.0

      # Prepare head mask if needed
      # 1.0 in head_mask indicate we keep the head
      # attention_probs has shape bsz x n_heads x N x N
      # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
      # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
      snake_case				=			self.get_head_mask(A__    ,   self.config.num_hidden_layers    )

      snake_case				=			self.embeddings(
          input_ids=A__    ,   position_ids=A__    ,   token_type_ids=A__    ,   inputs_embeds=A__    )
      snake_case				=			self.encoder(
          A__    ,   attention_mask=A__    ,   head_mask=A__    ,   encoder_hidden_states=A__    ,   encoder_attention_mask=A__    ,   )
      snake_case				=			encoder_outputs[0]
      snake_case				=			self.pooler(A__    )

      snake_case				=			(
          sequence_output,
          pooled_output,
      ) + encoder_outputs[
          1:
      ]  # add hidden_states and attentions if they are here
      return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ,   A__    ) ->	Any:
      snake_case				=			message
      snake_case				=			exit_layer  # start from 1!




class 			_lowercase						(       nn.Module					):







     def __init__( self    ,   A__    ) ->	str:
      super().__init__()
      snake_case				=			BertPooler(A__    )
      snake_case				=			nn.Dropout(config.hidden_dropout_prob    )
      snake_case				=			nn.Linear(config.hidden_size    ,   config.num_labels    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Optional[Any]:
      # Pooler
      snake_case				=			encoder_outputs[0]
      snake_case				=			self.pooler(A__    )
      # "return" pooler_output

      # BertModel
      snake_case				=			(pooler_input, pooler_output) + encoder_outputs[1:]
      # "return" bmodel_output

      # Dropout and classification
      snake_case				=			bmodel_output[1]

      snake_case				=			self.dropout(A__    )
      snake_case				=			self.classifier(A__    )

      return logits, pooled_output




@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''				,		__a				,		)
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ) ->	Union[str, Any]:
      super().__init__(A__    )
      snake_case				=			config.num_labels
      snake_case				=			config.num_hidden_layers

      snake_case				=			DeeBertModel(A__    )
      snake_case				=			nn.Dropout(config.hidden_dropout_prob    )
      snake_case				=			nn.Linear(config.hidden_size    ,   self.config.num_labels    )

      self.init_weights()







     @add_start_docstrings_to_model_forward(A__    )
     def     UpperCamelCase			( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=-1    ,   A__=False    ,   ) ->	Tuple:
      snake_case				=			self.num_layers
      try:
       snake_case				=			self.bert(
           A__    ,   attention_mask=A__    ,   token_type_ids=A__    ,   position_ids=A__    ,   head_mask=A__    ,   inputs_embeds=A__    ,   )
       # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

       snake_case				=			outputs[1]

       snake_case				=			self.dropout(A__    )
       snake_case				=			self.classifier(A__    )
       snake_case				=			(logits,) + outputs[2:]  # add hidden states and attention if they are here
      except HighwayException as e:
       snake_case				=			e.message
       snake_case				=			e.exit_layer
       snake_case				=			outputs[0]

      if not self.training:
       snake_case				=			entropy(A__    )
       snake_case				=			[]
       snake_case				=			[]
      if labels is not None:
       if self.num_labels == 1:
        #  We are doing regression
        snake_case				=			MSELoss()
        snake_case				=			loss_fct(logits.view(-1    )    ,   labels.view(-1    )    )
       else:
        snake_case				=			CrossEntropyLoss()
        snake_case				=			loss_fct(logits.view(-1    ,   self.num_labels    )    ,   labels.view(-1    )    )

       # work with highway exits
       snake_case				=			[]
       for highway_exit in outputs[-1]:
        snake_case				=			highway_exit[0]
        if not self.training:
         highway_logits_all.append(A__    )
         highway_entropy.append(highway_exit[2]    )
        if self.num_labels == 1:
         #  We are doing regression
         snake_case				=			MSELoss()
         snake_case				=			loss_fct(highway_logits.view(-1    )    ,   labels.view(-1    )    )
        else:
         snake_case				=			CrossEntropyLoss()
         snake_case				=			loss_fct(highway_logits.view(-1    ,   self.num_labels    )    ,   labels.view(-1    )    )
        highway_losses.append(A__    )

       if train_highway:
        snake_case				=			(sum(highway_losses[:-1]    ),) + outputs
        # exclude the final highway, of course
       else:
        snake_case				=			(loss,) + outputs
      if not self.training:
       snake_case				=			outputs + ((original_entropy, highway_entropy), exit_layer)
       if output_layer >= 0:
        snake_case				=			(
            (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
        )  # use the highway of the last layer

      return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)








'''simple docstring'''







from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor

