


'''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      __lowercase     (						_lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      torch.exp(_lowerCamelCase  )
     __UpperCAmelCase     :			Dict  							=      torch.sum(_lowerCamelCase      , dim=1  )  # sum of exp(x_i)
     __UpperCAmelCase     :			List[Any]  							=      torch.sum(x * exp_x      , dim=1  )  # sum of x_i * exp(x_i)
     return torch.log(_lowerCamelCase  ) - B / A



class      a    (		nn.Module	):


     """simple docstring"""






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

          __UpperCAmelCase     :			int  							=      [-1 for _ in range(config.num_hidden_layers      )]

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

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		Union[str, Any]      )						->							Optional[Any]:
          __UpperCAmelCase     :			int  							=      pooler.state_dict()
          for highway in self.highway:
               for name, param in highway.pooler.state_dict().items():
                    param.copy_(loaded_model[name]      )






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Any							,			snake_case					:		Any=None							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		str=None							,			snake_case					:		str=None							,			)						->							List[str]:
          __UpperCAmelCase     :			str  							=      ()
          __UpperCAmelCase     :			List[str]  							=      ()
          __UpperCAmelCase     :			Tuple  							=      ()
          for i, layer_module in enumerate(self.layer      ):
               if self.output_hidden_states:
                    __UpperCAmelCase     :			List[Any]  							=      all_hidden_states + (hidden_states,)

               __UpperCAmelCase     :			str  							=      layer_module(
                   snake_case							,			snake_case							,			head_mask[i]							,			snake_case							,			snake_case      )
               __UpperCAmelCase     :			str  							=      layer_outputs[0]

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

               __UpperCAmelCase     :			int  							=      (hidden_states,)
               if self.output_hidden_states:
                    __UpperCAmelCase     :			Dict  							=      current_outputs + (all_hidden_states,)
               if self.output_attentions:
                    __UpperCAmelCase     :			Union[str, Any]  							=      current_outputs + (all_attentions,)

               __UpperCAmelCase     :			Optional[int]  							=      self.highway[i](snake_case      )
               # logits, pooled_output

               if not self.training:
                    __UpperCAmelCase     :			Dict  							=      highway_exit[0]
                    __UpperCAmelCase     :			str  							=      entropy(snake_case      )
                    __UpperCAmelCase     :			List[Any]  							=      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                    __UpperCAmelCase     :			str  							=      all_highway_exits + (highway_exit,)

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

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

          __UpperCAmelCase     :			Union[str, Any]  							=      (hidden_states,)
          if self.output_hidden_states:
               __UpperCAmelCase     :			Any  							=      outputs + (all_hidden_states,)
          if self.output_attentions:
               __UpperCAmelCase     :			Tuple  							=      outputs + (all_attentions,)

          __UpperCAmelCase     :			Any  							=      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      a    (		a__	):


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		Dict      )						->							str:
          super().__init__(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      config

          __UpperCAmelCase     :			Dict  							=      BertEmbeddings(snake_case      )
          __UpperCAmelCase     :			Any  							=      DeeBertEncoder(snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      BertPooler(snake_case      )

          self.init_weights()

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							str:
          self.encoder.init_highway_pooler(self.pooler      )

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Dict:
          return self.embeddings.word_embeddings

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Tuple      )						->							Any:
          __UpperCAmelCase     :			Union[str, Any]  							=      value

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






     @add_start_docstrings_to_model_forward(snake_case      )
     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Any=None							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		Optional[Any]=None							,			snake_case					:		List[str]=None							,			snake_case					:		List[str]=None							,			snake_case					:		List[str]=None							,			snake_case					:		Any=None							,			snake_case					:		Union[str, Any]=None							,			)						->							List[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:
               __UpperCAmelCase     :			Optional[int]  							=      input_ids.size()
          elif inputs_embeds is not None:
               __UpperCAmelCase     :			str  							=      inputs_embeds.size()[:-1]
          else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds'''      )

          __UpperCAmelCase     :			Tuple  							=      input_ids.device if input_ids is not None else inputs_embeds.device

          if attention_mask is None:
               __UpperCAmelCase     :			Optional[Any]  							=      torch.ones(snake_case							,			device=snake_case      )
          if encoder_attention_mask is None:
               __UpperCAmelCase     :			Optional[int]  							=      torch.ones(snake_case							,			device=snake_case      )
          if token_type_ids is None:
               __UpperCAmelCase     :			Dict  							=      torch.zeros(snake_case							,			dtype=torch.long							,			device=snake_case      )

          # 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.
          __UpperCAmelCase     :			torch.Tensor  							=      self.get_extended_attention_mask(snake_case							,			snake_case							,			snake_case      )

          # 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:
               __UpperCAmelCase     :			Optional[int]  							=      encoder_attention_mask[:, None, :, :]
          if encoder_attention_mask.dim() == 2:
               __UpperCAmelCase     :			Optional[Any]  							=      encoder_attention_mask[:, None, None, :]

          __UpperCAmelCase     :			List[Any]  							=      encoder_extended_attention_mask.to(
              dtype=next(self.parameters()      ).dtype      )  # fp16 compatibility
          __UpperCAmelCase     :			Dict  							=      (1.0 - encoder_extended_attention_mask) * -1_0000.0

          # 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]
          __UpperCAmelCase     :			int  							=      self.get_head_mask(snake_case							,			self.config.num_hidden_layers      )

          __UpperCAmelCase     :			str  							=      self.embeddings(
              input_ids=snake_case							,			position_ids=snake_case							,			token_type_ids=snake_case							,			inputs_embeds=snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      self.encoder(
              snake_case							,			attention_mask=snake_case							,			head_mask=snake_case							,			encoder_hidden_states=snake_case							,			encoder_attention_mask=snake_case							,			)
          __UpperCAmelCase     :			str  							=      encoder_outputs[0]
          __UpperCAmelCase     :			Tuple  							=      self.pooler(snake_case      )

          __UpperCAmelCase     :			Tuple  							=      (
              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      a    (		a__	):


     """simple docstring"""






     def __init__(    self					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		List[str]      )						->							int:
          __UpperCAmelCase     :			str  							=      message
          __UpperCAmelCase     :			Dict  							=      exit_layer  # start from 1!



class      a    (		nn.Module	):


     """simple docstring"""






     def __init__(    self					:		str							,			snake_case					:		int      )						->							Dict:
          super().__init__()
          __UpperCAmelCase     :			Union[str, Any]  							=      BertPooler(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      nn.Dropout(config.hidden_dropout_prob      )
          __UpperCAmelCase     :			str  							=      nn.Linear(config.hidden_size							,			config.num_labels      )






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

          # BertModel
          __UpperCAmelCase     :			Union[str, Any]  							=      (pooler_input, pooler_output) + encoder_outputs[1:]
          # "return" bmodel_output

          # Dropout and classification
          __UpperCAmelCase     :			Union[str, Any]  							=      bmodel_output[1]

          __UpperCAmelCase     :			int  							=      self.dropout(snake_case      )
          __UpperCAmelCase     :			Dict  							=      self.classifier(snake_case      )

          return logits, pooled_output



@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "     ,  a__     ,  )
class      a    (		a__	):


     """simple docstring"""






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

          __UpperCAmelCase     :			Union[str, Any]  							=      DeeBertModel(snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      nn.Dropout(config.hidden_dropout_prob      )
          __UpperCAmelCase     :			str  							=      nn.Linear(config.hidden_size							,			self.config.num_labels      )

          self.init_weights()






     @add_start_docstrings_to_model_forward(snake_case      )
     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		int=None							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		Any=None							,			snake_case					:		Optional[Any]=None							,			snake_case					:		List[str]=None							,			snake_case					:		Any=None							,			snake_case					:		Optional[int]=None							,			snake_case					:		int=-1							,			snake_case					:		Optional[int]=False							,			)						->							str:
          __UpperCAmelCase     :			int  							=      self.num_layers
          try:
               __UpperCAmelCase     :			Optional[Any]  							=      self.bert(
                   snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			position_ids=snake_case							,			head_mask=snake_case							,			inputs_embeds=snake_case							,			)
               # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

               __UpperCAmelCase     :			Any  							=      outputs[1]

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

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

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

               if train_highway:
                    __UpperCAmelCase     :			Union[str, Any]  							=      (sum(highway_losses[:-1]      ),) + outputs
                    # exclude the final highway, of course
               else:
                    __UpperCAmelCase     :			Any  							=      (loss,) + outputs
          if not self.training:
               __UpperCAmelCase     :			Any  							=      outputs + ((original_entropy, highway_entropy), exit_layer)
               if output_layer >= 0:
                    __UpperCAmelCase     :			int  							=      (
                        (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 argparse import ArgumentParser

from .env import EnvironmentCommand




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      ArgumentParser('''Diffusers CLI tool'''      , usage='''diffusers-cli <command> [<args>]'''  )
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.add_subparsers(help='''diffusers-cli command helpers'''  )

     # Register commands
     EnvironmentCommand.register_subcommand(_lowercase  )

     # Let's go
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.parse_args()

     if not hasattr(_lowercase      , '''func'''  ):
          parser.print_help()
          exit(1  )

     # Run
     __UpperCAmelCase     :			Any  							=      args.func(_lowercase  )
     service.run()


if __name__ == "__main__":
      main()