
'''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 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




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

class 			UpperCAmelCase__    (							nn.Module			):



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

          _lowerCamelCase					: List[str]		    = [-1 for _ in range(config.num_hidden_layers  )]



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							int  ):
          if (type(__A  ) is float) or (type(__A  ) is int):
                for i in range(len(self.early_exit_entropy  )  ):
                      _lowerCamelCase					: List[Any]		    = x
          else:
                _lowerCamelCase					: List[str]		    = x



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							Union[str, Any]  ):
          _lowerCamelCase					: List[str]		    = 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   :							List[str],__A   :							int,__A   :							Any=None,__A   :							Any=None,__A   :							int=None,__A   :							Optional[int]=None,):
          _lowerCamelCase					: Tuple		    = ()
          _lowerCamelCase					: Tuple		    = ()
          _lowerCamelCase					: Optional[Any]		    = ()
          for i, layer_module in enumerate(self.layer  ):
                if self.output_hidden_states:
                      _lowerCamelCase					: int		    = all_hidden_states + (hidden_states,)

                _lowerCamelCase					: List[Any]		    = layer_module(
                    __A,__A,head_mask[i],__A,__A  )
                _lowerCamelCase					: Dict		    = layer_outputs[0]

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

                _lowerCamelCase					: List[str]		    = (hidden_states,)
                if self.output_hidden_states:
                      _lowerCamelCase					: str		    = current_outputs + (all_hidden_states,)
                if self.output_attentions:
                      _lowerCamelCase					: Dict		    = current_outputs + (all_attentions,)

                _lowerCamelCase					: Any		    = self.highway[i](__A  )
                # logits, pooled_output

                if not self.training:
                      _lowerCamelCase					: int		    = highway_exit[0]
                      _lowerCamelCase					: Optional[Any]		    = entropy(__A  )
                      _lowerCamelCase					: Tuple		    = highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
                      _lowerCamelCase					: List[str]		    = all_highway_exits + (highway_exit,)

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

        # Add last layer
          if self.output_hidden_states:
                _lowerCamelCase					: Optional[Any]		    = all_hidden_states + (hidden_states,)

          _lowerCamelCase					: List[Any]		    = (hidden_states,)
          if self.output_hidden_states:
                _lowerCamelCase					: int		    = outputs + (all_hidden_states,)
          if self.output_attentions:
                _lowerCamelCase					: Optional[int]		    = outputs + (all_attentions,)

          _lowerCamelCase					: Tuple		    = 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 			UpperCAmelCase__    (							A			):



    def __init__(						self   :							Any,__A   :							int  ):
          super().__init__(__A  )
          _lowerCamelCase					: str		    = config

          _lowerCamelCase					: str		    = BertEmbeddings(__A  )
          _lowerCamelCase					: Tuple		    = DeeBertEncoder(__A  )
          _lowerCamelCase					: List[str]		    = BertPooler(__A  )

          self.init_weights()



    def 	lowerCamelCase_	(						self   :							Dict  ):
          self.encoder.init_highway_pooler(self.pooler  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          return self.embeddings.word_embeddings



    def 	lowerCamelCase_	(						self   :							Any,__A   :							Optional[int]  ):
          _lowerCamelCase					: str		    = value



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							int  ):
          for layer, heads in heads_to_prune.items():
                self.encoder.layer[layer].attention.prune_heads(__A  )



    @add_start_docstrings_to_model_forward(__A  )
    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							List[str]=None,__A   :							int=None,__A   :							Union[str, Any]=None,__A   :							Tuple=None,__A   :							Dict=None,__A   :							int=None,__A   :							Dict=None,__A   :							List[str]=None,):
          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:
                _lowerCamelCase					: List[str]		    = input_ids.size()
          elif inputs_embeds is not None:
                _lowerCamelCase					: Optional[int]		    = inputs_embeds.size()[:-1]
          else:
                raise ValueError("You have to specify either input_ids or inputs_embeds"  )

          _lowerCamelCase					: Optional[Any]		    = input_ids.device if input_ids is not None else inputs_embeds.device

          if attention_mask is None:
                _lowerCamelCase					: List[str]		    = torch.ones(__A,device=__A  )
          if encoder_attention_mask is None:
                _lowerCamelCase					: List[Any]		    = torch.ones(__A,device=__A  )
          if token_type_ids is None:
                _lowerCamelCase					: List[str]		    = 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.
          _lowerCamelCase					: torch.Tensor		    = 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:
                _lowerCamelCase					: List[str]		    = encoder_attention_mask[:, None, :, :]
          if encoder_attention_mask.dim() == 2:
                _lowerCamelCase					: Optional[int]		    = encoder_attention_mask[:, None, None, :]

          _lowerCamelCase					: Union[str, Any]		    = encoder_extended_attention_mask.to(
              dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
          _lowerCamelCase					: Any		    = (1.0 - encoder_extended_attention_mask) * -10000.0

          # 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]
          _lowerCamelCase					: Union[str, Any]		    = self.get_head_mask(__A,self.config.num_hidden_layers  )

          _lowerCamelCase					: List[Any]		    = self.embeddings(
              input_ids=__A,position_ids=__A,token_type_ids=__A,inputs_embeds=__A  )
          _lowerCamelCase					: Union[str, Any]		    = self.encoder(
              __A,attention_mask=__A,head_mask=__A,encoder_hidden_states=__A,encoder_attention_mask=__A,)
          _lowerCamelCase					: Union[str, Any]		    = encoder_outputs[0]
          _lowerCamelCase					: Tuple		    = self.pooler(__A  )

          _lowerCamelCase					: Optional[int]		    = (
              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 			UpperCAmelCase__    (							A			):



    def __init__(						self   :							List[Any],__A   :							int,__A   :							int  ):
          _lowerCamelCase					: Tuple		    = message
          _lowerCamelCase					: Union[str, Any]		    = exit_layer  # start from 1!


class 			UpperCAmelCase__    (							nn.Module			):



    def __init__(						self   :							int,__A   :							List[Any]  ):
          super().__init__()
          _lowerCamelCase					: List[Any]		    = BertPooler(__A  )
          _lowerCamelCase					: List[Any]		    = nn.Dropout(config.hidden_dropout_prob  )
          _lowerCamelCase					: Tuple		    = nn.Linear(config.hidden_size,config.num_labels  )



    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							Union[str, Any]  ):
          # Pooler
          _lowerCamelCase					: int		    = encoder_outputs[0]
          _lowerCamelCase					: Tuple		    = self.pooler(__A  )
          # "return" pooler_output

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

          # Dropout and classification
          _lowerCamelCase					: List[Any]		    = bmodel_output[1]

          _lowerCamelCase					: Optional[int]		    = self.dropout(__A  )
          _lowerCamelCase					: Optional[Any]		    = self.classifier(__A  )

          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 			UpperCAmelCase__    (							A			):



    def __init__(						self   :							str,__A   :							Optional[int]  ):
          super().__init__(__A  )
          _lowerCamelCase					: Tuple		    = config.num_labels
          _lowerCamelCase					: Dict		    = config.num_hidden_layers

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

          self.init_weights()



    @add_start_docstrings_to_model_forward(__A  )
    def 	lowerCamelCase_	(						self   :							Dict,__A   :							Any=None,__A   :							Optional[Any]=None,__A   :							Union[str, Any]=None,__A   :							Union[str, Any]=None,__A   :							Optional[int]=None,__A   :							Optional[int]=None,__A   :							Union[str, Any]=None,__A   :							Optional[int]=-1,__A   :							Any=False,):
          _lowerCamelCase					: Optional[Any]		    = self.num_layers
          try:
                _lowerCamelCase					: Optional[Any]		    = 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

                _lowerCamelCase					: Union[str, Any]		    = outputs[1]

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

          if not self.training:
                _lowerCamelCase					: List[Any]		    = entropy(__A  )
                _lowerCamelCase					: Dict		    = []
                _lowerCamelCase					: Optional[Any]		    = []
          if labels is not None:
                if self.num_labels == 1:
                      #  We are doing regression
                      _lowerCamelCase					: List[Any]		    = MSELoss()
                      _lowerCamelCase					: Tuple		    = loss_fct(logits.view(-1  ),labels.view(-1  )  )
                else:
                      _lowerCamelCase					: Optional[int]		    = CrossEntropyLoss()
                      _lowerCamelCase					: Optional[int]		    = loss_fct(logits.view(-1,self.num_labels  ),labels.view(-1  )  )

                # work with highway exits
                _lowerCamelCase					: Union[str, Any]		    = []
                for highway_exit in outputs[-1]:
                      _lowerCamelCase					: Any		    = 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
                            _lowerCamelCase					: List[str]		    = MSELoss()
                            _lowerCamelCase					: str		    = loss_fct(highway_logits.view(-1  ),labels.view(-1  )  )
                      else:
                            _lowerCamelCase					: Optional[int]		    = CrossEntropyLoss()
                            _lowerCamelCase					: str		    = loss_fct(highway_logits.view(-1,self.num_labels  ),labels.view(-1  )  )
                      highway_losses.append(__A  )

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a	 =      {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PegasusXForConditionalGeneration""",
     """PegasusXModel""",
     """PegasusXPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 __a	 =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



