



'''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 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   UpperCamelCase					=  torch.exp(A__    )
   UpperCamelCase					=  torch.sum(A__    ,						dim=1    )  # sum of exp(x_i)
   UpperCamelCase					=  torch.sum(x * exp_x    ,						dim=1    )  # sum of x_i * exp(x_i)
   return torch.log(A__    ) - B / A

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  config.output_attentions
       UpperCamelCase					=  config.output_hidden_states
       UpperCamelCase					=  nn.ModuleList([BertLayer(UpperCamelCase__ ) for _ in range(config.num_hidden_layers )] )
       UpperCamelCase					=  nn.ModuleList([BertHighway(UpperCamelCase__ ) for _ in range(config.num_hidden_layers )] )

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






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       if (type(UpperCamelCase__ ) is float) or (type(UpperCamelCase__ ) is int):
          for i in range(len(self.early_exit_entropy ) ):
             UpperCamelCase					=  x
       else:
          UpperCamelCase					=  x






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  pooler.state_dict()
       for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
             param.copy_(loaded_model[name] )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					List[Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  ()
       UpperCamelCase					=  ()
       UpperCamelCase					=  ()
       for i, layer_module in enumerate(self.layer ):
          if self.output_hidden_states:
             UpperCamelCase					=  all_hidden_states + (hidden_states,)

          UpperCamelCase					=  layer_module(
              UpperCamelCase__  ,						UpperCamelCase__  ,						head_mask[i]  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          UpperCamelCase					=  layer_outputs[0]

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

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

          UpperCamelCase					=  self.highway[i](UpperCamelCase__ )
          # logits, pooled_output

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

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

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

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

       UpperCamelCase					=  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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config

       UpperCamelCase					=  BertEmbeddings(UpperCamelCase__ )
       UpperCamelCase					=  DeeBertEncoder(UpperCamelCase__ )
       UpperCamelCase					=  BertPooler(UpperCamelCase__ )

       self.init_weights()






    def  A       (  self      :					int ):


       """simple docstring"""







       self.encoder.init_highway_pooler(self.pooler )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       return self.embeddings.word_embeddings






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  value






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(UpperCamelCase__ )






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					List[Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					Tuple=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						):


       """simple docstring"""







       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					=  input_ids.size()
       elif inputs_embeds is not None:
          UpperCamelCase					=  inputs_embeds.size()[:-1]
       else:
          raise ValueError('You have to specify either input_ids or inputs_embeds' )

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

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

       # 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					=  self.get_extended_attention_mask(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

       # 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					=  encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
          UpperCamelCase					=  encoder_attention_mask[:, None, None, :]

       UpperCamelCase					=  encoder_extended_attention_mask.to(
           dtype=next(self.parameters() ).dtype )  # fp16 compatibility
       UpperCamelCase					=  (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

       # 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					=  self.get_head_mask(UpperCamelCase__  ,						self.config.num_hidden_layers )

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

       UpperCamelCase					=  (
           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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  message
       UpperCamelCase					=  exit_layer  # start from 1!

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Union[str, Any] ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  BertPooler(UpperCamelCase__ )
       UpperCamelCase					=  nn.Dropout(config.hidden_dropout_prob )
       UpperCamelCase					=  nn.Linear(config.hidden_size  ,						config.num_labels )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  encoder_outputs[0]
       UpperCamelCase					=  self.pooler(UpperCamelCase__ )
       # "return" pooler_output

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

       # Dropout and classification
       UpperCamelCase					=  bmodel_output[1]

       UpperCamelCase					=  self.dropout(UpperCamelCase__ )
       UpperCamelCase					=  self.classifier(UpperCamelCase__ )

       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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )
       UpperCamelCase					=  config.num_labels
       UpperCamelCase					=  config.num_hidden_layers

       UpperCamelCase					=  DeeBertModel(UpperCamelCase__ )
       UpperCamelCase					=  nn.Dropout(config.hidden_dropout_prob )
       UpperCamelCase					=  nn.Linear(config.hidden_size  ,						self.config.num_labels )

       self.init_weights()






    @add_start_docstrings_to_model_forward(UpperCamelCase__ )
    def  A       (  self      :					str  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					List[Any]=None  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					str=-1  ,						UpperCamelCase__      :					Union[str, Any]=False  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.num_layers
       try:
          UpperCamelCase					=  self.bert(
              UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						head_mask=UpperCamelCase__  ,						inputs_embeds=UpperCamelCase__  ,						)
          # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

          UpperCamelCase					=  outputs[1]

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

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

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

          if train_highway:
             UpperCamelCase					=  (sum(highway_losses[:-1] ),) + outputs
             # exclude the final highway, of course
          else:
             UpperCamelCase					=  (loss,) + outputs
       if not self.training:
          UpperCamelCase					=  outputs + ((original_entropy, highway_entropy), exit_layer)
          if output_layer >= 0:
             UpperCamelCase					=  (
                 (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 torch import nn

def 				snake_case      (   A__						):
			if act_fn in ["swish", "silu"]:
						return nn.SiLU()
			elif act_fn == "mish":
						return nn.Mish()
			elif act_fn == "gelu":
						return nn.GELU()
			else:
						raise ValueError(F"""Unsupported activation function: {act_fn}"""						)



