






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_   : List[Any]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Union[str, Any]   			= {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}




class 							lowerCAmelCase__				(			SCREAMING_SNAKE_CASE_				,		SCREAMING_SNAKE_CASE_ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "bit"
      __UpperCamelCase	 =  ["preactivation", "bottleneck"]
      __UpperCamelCase	 =  ["SAME", "VALID"]
      def __init__(						self     :  Dict						,	lowercase_     :  List[str]=3						,	lowercase_     :  Optional[Any]=64						,	lowercase_     :  List[Any]=[256, 512, 1024, 2048]						,	lowercase_     :  Optional[int]=[3, 4, 6, 3]						,	lowercase_     :  Optional[int]="preactivation"						,	lowercase_     :  Optional[Any]="relu"						,	lowercase_     :  List[Any]=None						,	lowercase_     :  Any=32						,	lowercase_     :  Dict=0.0						,	lowercase_     :  Union[str, Any]=False						,	lowercase_     :  Any=32						,	lowercase_     :  Union[str, Any]=1						,	lowercase_     :  Optional[Any]=None						,	lowercase_     :  Any=None						,	**lowercase_     :  Dict						,	):




          '''simple docstring'''


          super().__init__(**a__)
          if layer_type not in self.layer_types:
              raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types)}')
          if global_padding is not None:
              if global_padding.upper() in self.supported_padding:
                  SCREAMING_SNAKE_CASE_      :  List[str]    						=		global_padding.upper()
              else:
                  raise ValueError(F'Padding strategy {global_padding} not supported')
          SCREAMING_SNAKE_CASE_      :  Dict    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		embedding_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		depths
          SCREAMING_SNAKE_CASE_      :  Any    						=		layer_type
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		global_padding
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_groups
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		drop_path_rate
          SCREAMING_SNAKE_CASE_      :  Any    						=		embedding_dynamic_padding
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		output_stride
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		width_factor

          SCREAMING_SNAKE_CASE_      :  Any    						=		["""stem"""] + [F'stage{idx}' for idx in range(1						,	len(a__) + 1)]
          SCREAMING_SNAKE_CASE_      :  int    						=		get_aligned_output_features_output_indices(
              out_features=a__						,	out_indices=a__						,	stage_names=self.stage_names)



"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Dict:
  _lowerCAmelCase						:       List[Any]  			=     torch.exp(_lowerCamelCase	)
  _lowerCAmelCase						:       List[Any]  			=     torch.sum(_lowerCamelCase			,dim=1	)  # sum of exp(x_i)
  _lowerCAmelCase						:       Dict  			=     torch.sum(x * exp_x			,dim=1	)  # sum of x_i * exp(x_i)
  return torch.log(_lowerCamelCase	) - B / A
class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       int  			=     config.output_attentions
       _lowerCAmelCase						:       Any  			=     config.output_hidden_states
       _lowerCAmelCase						:       List[Any]  			=     nn.ModuleList([BertLayer(a__  ) for _ in range(config.num_hidden_layers  )]  )
       _lowerCAmelCase						:       Any  			=     nn.ModuleList([BertHighway(a__  ) for _ in range(config.num_hidden_layers  )]  )

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






     def     __A  (		self				,     a__  ):
       if (type(a__  ) is float) or (type(a__  ) is int):
         for i in range(len(self.early_exit_entropy  )  ):
           _lowerCAmelCase						:       Tuple  			=     x
       else:
         _lowerCAmelCase						:       Optional[int]  			=     x






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

         _lowerCAmelCase						:       List[str]  			=     layer_module(
             a__				,     a__				,     head_mask[i]				,     a__				,     a__  )
         _lowerCAmelCase						:       Union[str, Any]  			=     layer_outputs[0]

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

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

         _lowerCAmelCase						:       Optional[Any]  			=     self.highway[i](a__  )
         # logits, pooled_output

         if not self.training:
           _lowerCAmelCase						:       Tuple  			=     highway_exit[0]
           _lowerCAmelCase						:       Any  			=     entropy(a__  )
           _lowerCAmelCase						:       Optional[Any]  			=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
           _lowerCAmelCase						:       Union[str, Any]  			=     all_highway_exits + (highway_exit,)

           if highway_entropy < self.early_exit_entropy[i]:
             _lowerCAmelCase						:       List[str]  			=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
             raise HighwayException(a__				,     i + 1  )
         else:
           _lowerCAmelCase						:       Dict  			=     all_highway_exits + (highway_exit,)

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

       _lowerCAmelCase						:       List[Any]  			=     (hidden_states,)
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[str]  			=     outputs + (all_hidden_states,)
       if self.output_attentions:
         _lowerCAmelCase						:       Any  			=     outputs + (all_attentions,)

       _lowerCAmelCase						:       Optional[int]  			=     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). "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       Any  			=     config

       _lowerCAmelCase						:       Tuple  			=     BertEmbeddings(a__  )
       _lowerCAmelCase						:       Tuple  			=     DeeBertEncoder(a__  )
       _lowerCAmelCase						:       List[str]  			=     BertPooler(a__  )

       self.init_weights()






     def     __A  (		self  ):
       self.encoder.init_highway_pooler(self.pooler  )






     def     __A  (		self  ):
       return self.embeddings.word_embeddings






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     value






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






     @add_start_docstrings_to_model_forward(a__  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=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						:       Any  			=     input_ids.size()
       elif inputs_embeds is not None:
         _lowerCAmelCase						:       List[str]  			=     inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

       _lowerCAmelCase						:       str  			=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
         _lowerCAmelCase						:       List[Any]  			=     torch.ones(a__				,     device=a__  )
       if encoder_attention_mask is None:
         _lowerCAmelCase						:       Optional[Any]  			=     torch.ones(a__				,     device=a__  )
       if token_type_ids is None:
         _lowerCAmelCase						:       Dict  			=     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						:       Dict  			=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
         _lowerCAmelCase						:       Tuple  			=     encoder_attention_mask[:, None, None, :]

       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
       _lowerCAmelCase						:       Optional[Any]  			=     (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]
       _lowerCAmelCase						:       Optional[int]  			=     self.get_head_mask(a__				,     self.config.num_hidden_layers  )

       _lowerCAmelCase						:       Dict  			=     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						:       Dict  			=     encoder_outputs[0]
       _lowerCAmelCase						:       Union[str, Any]  			=     self.pooler(a__  )

       _lowerCAmelCase						:       Dict  			=     (
           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							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     message
       _lowerCAmelCase						:       str  			=     exit_layer  # start from 1!







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Any  			=     BertPooler(a__  )
       _lowerCAmelCase						:       str  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Linear(config.hidden_size				,     config.num_labels  )






     def     __A  (		self				,     a__  ):
       # Pooler
       _lowerCAmelCase						:       Tuple  			=     encoder_outputs[0]
       _lowerCAmelCase						:       int  			=     self.pooler(a__  )
       # "return" pooler_output

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

       # Dropout and classification
       _lowerCAmelCase						:       Optional[int]  			=     bmodel_output[1]

       _lowerCAmelCase						:       Tuple  			=     self.dropout(a__  )
       _lowerCAmelCase						:       Dict  			=     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. "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       List[str]  			=     config.num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     config.num_hidden_layers

       _lowerCAmelCase						:       str  			=     DeeBertModel(a__  )
       _lowerCAmelCase						:       Tuple  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       List[Any]  			=     nn.Linear(config.hidden_size				,     self.config.num_labels  )

       self.init_weights()






     @add_start_docstrings_to_model_forward(a__  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=-1				,     a__=False				,     ):
       _lowerCAmelCase						:       Dict  			=     self.num_layers
       try:
         _lowerCAmelCase						:       str  			=     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						:       Any  			=     outputs[1]

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

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

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

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




