






"""simple docstring"""






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)



def 					_A			(__a    ,				__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		os.path.abspath(_lowerCamelCase							)
    logger.info(f'Converting TensorFlow checkpoint from {tf_path}'							)
    # Load weights from TF model
    SCREAMING_SNAKE_CASE_      :  int    						=		tf.train.list_variables(_lowerCamelCase							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		[]
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[]
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        SCREAMING_SNAKE_CASE_      :  Dict    						=		full_name.split('''/'''							)
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(f'Skipping non-model layer {full_name}'							)
            continue
        if "optimizer" in full_name:
            logger.info(f'Skipping optimization layer {full_name}'							)
            continue
        if name[0] == "model":
            # ignore initial 'model'
            SCREAMING_SNAKE_CASE_      :  Dict    						=		name[1:]
        # figure out how many levels deep the name is
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		0
        for _name in name:
            if _name.startswith('''layer_with_weights'''							):
                depth += 1
            else:
                break
        layer_depth.append(_lowerCamelCase							)
        # read data
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tf.train.load_variable(_lowerCamelCase    ,				_lowerCamelCase							)
        names.append('''/'''.join(_lowerCamelCase							)							)
        arrays.append(_lowerCamelCase							)
    logger.info(f'Read a total of {len(_lowerCamelCase							):,} layers'							)

    # Sanity check
    if len(set(_lowerCamelCase							)							) != 1:
        raise ValueError(f'Found layer names with different depths (layer depth {list(set(_lowerCamelCase							)							)})'							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		list(set(_lowerCamelCase							)							)[0]
    if layer_depth != 1:
        raise ValueError(
            '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
            ''' heads.'''							)

    # convert layers
    logger.info('''Converting weights...'''							)
    for full_name, array in zip(_lowerCamelCase    ,				_lowerCamelCase							):
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		full_name.split('''/'''							)
        SCREAMING_SNAKE_CASE_      :  str    						=		model
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		[]
        for i, m_name in enumerate(_lowerCamelCase							):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('''layer_with_weights'''							):
                SCREAMING_SNAKE_CASE_      :  Dict    						=		int(m_name.split('''-'''							)[-1]							)
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(['''embeddings''', '''LayerNorm''']							)
                    SCREAMING_SNAKE_CASE_      :  int    						=		getattr(_lowerCamelCase    ,				'''embeddings'''							)
                    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		getattr(_lowerCamelCase    ,				'''LayerNorm'''							)
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['''encoder''', '''layer''', str(layer_num - 4							)]							)
                    SCREAMING_SNAKE_CASE_      :  int    						=		getattr(_lowerCamelCase    ,				'''encoder'''							)
                    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		getattr(_lowerCamelCase    ,				'''layer'''							)
                    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['''pooler''', '''dense''']							)
                    SCREAMING_SNAKE_CASE_      :  int    						=		getattr(_lowerCamelCase    ,				'''pooler'''							)
                    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		getattr(_lowerCamelCase    ,				'''dense'''							)
            elif m_name == "embeddings":
                trace.append('''embeddings'''							)
                SCREAMING_SNAKE_CASE_      :  Tuple    						=		getattr(_lowerCamelCase    ,				'''embeddings'''							)
                if layer_num == 0:
                    trace.append('''word_embeddings'''							)
                    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		getattr(_lowerCamelCase    ,				'''word_embeddings'''							)
                elif layer_num == 1:
                    trace.append('''position_embeddings'''							)
                    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		getattr(_lowerCamelCase    ,				'''position_embeddings'''							)
                elif layer_num == 2:
                    trace.append('''token_type_embeddings'''							)
                    SCREAMING_SNAKE_CASE_      :  str    						=		getattr(_lowerCamelCase    ,				'''token_type_embeddings'''							)
                else:
                    raise ValueError(f'Unknown embedding layer with name {full_name}'							)
                trace.append('''weight'''							)
                SCREAMING_SNAKE_CASE_      :  Dict    						=		getattr(_lowerCamelCase    ,				'''weight'''							)
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['''attention''', '''self''']							)
                SCREAMING_SNAKE_CASE_      :  Tuple    						=		getattr(_lowerCamelCase    ,				'''attention'''							)
                SCREAMING_SNAKE_CASE_      :  str    						=		getattr(_lowerCamelCase    ,				'''self'''							)
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['''attention''', '''output''', '''LayerNorm''']							)
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		getattr(_lowerCamelCase    ,				'''attention'''							)
                SCREAMING_SNAKE_CASE_      :  Tuple    						=		getattr(_lowerCamelCase    ,				'''output'''							)
                SCREAMING_SNAKE_CASE_      :  Dict    						=		getattr(_lowerCamelCase    ,				'''LayerNorm'''							)
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['''attention''', '''output''', '''dense''']							)
                SCREAMING_SNAKE_CASE_      :  int    						=		getattr(_lowerCamelCase    ,				'''attention'''							)
                SCREAMING_SNAKE_CASE_      :  int    						=		getattr(_lowerCamelCase    ,				'''output'''							)
                SCREAMING_SNAKE_CASE_      :  List[Any]    						=		getattr(_lowerCamelCase    ,				'''dense'''							)
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['''output''', '''dense''']							)
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		getattr(_lowerCamelCase    ,				'''output'''							)
                SCREAMING_SNAKE_CASE_      :  Any    						=		getattr(_lowerCamelCase    ,				'''dense'''							)
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['''output''', '''LayerNorm''']							)
                SCREAMING_SNAKE_CASE_      :  Tuple    						=		getattr(_lowerCamelCase    ,				'''output'''							)
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		getattr(_lowerCamelCase    ,				'''LayerNorm'''							)
            elif m_name == "_key_dense":
                # attention key
                trace.append('''key'''							)
                SCREAMING_SNAKE_CASE_      :  str    						=		getattr(_lowerCamelCase    ,				'''key'''							)
            elif m_name == "_query_dense":
                # attention query
                trace.append('''query'''							)
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		getattr(_lowerCamelCase    ,				'''query'''							)
            elif m_name == "_value_dense":
                # attention value
                trace.append('''value'''							)
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		getattr(_lowerCamelCase    ,				'''value'''							)
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['''intermediate''', '''dense''']							)
                SCREAMING_SNAKE_CASE_      :  str    						=		getattr(_lowerCamelCase    ,				'''intermediate'''							)
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		getattr(_lowerCamelCase    ,				'''dense'''							)
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('''output'''							)
                SCREAMING_SNAKE_CASE_      :  Any    						=		getattr(_lowerCamelCase    ,				'''output'''							)
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('''bias'''							)
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		getattr(_lowerCamelCase    ,				'''bias'''							)
            elif m_name in ["kernel", "gamma"]:
                trace.append('''weight'''							)
                SCREAMING_SNAKE_CASE_      :  str    						=		getattr(_lowerCamelCase    ,				'''weight'''							)
            else:
                logger.warning(f'Ignored {m_name}'							)
                # for certain layers reshape is necessary
        SCREAMING_SNAKE_CASE_      :  Any    						=		""".""".join(_lowerCamelCase							)
        if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''    ,				_lowerCamelCase							) or re.match(
            R'''(\S+)\.attention\.output\.dense\.weight'''    ,				_lowerCamelCase							):
            SCREAMING_SNAKE_CASE_      :  str    						=		array.reshape(pointer.data.shape							)
        if "kernel" in full_name:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		array.transpose()
        if pointer.shape == array.shape:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.from_numpy(_lowerCamelCase							)
        else:
            raise ValueError(
                f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                f' {array.shape}'							)
        logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'							)
    return model



def 					_A			(__a    ,				__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    logger.info(f'Loading model based on config from {config_path}...'							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		BertConfig.from_json_file(_lowerCamelCase							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		BertModel(_lowerCamelCase							)

    # Load weights from checkpoint
    logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'							)
    load_tfa_weights_in_bert(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase							)

    # Save pytorch-model
    logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'							)
    torch.save(model.state_dict()    ,				_lowerCamelCase							)


if __name__ == "__main__":
  UpperCAmelCase_   : Union[str, Any]   			= argparse.ArgumentParser()
  parser.add_argument(
      """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      type=str,
      required=True,
      help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""",
      type=str,
      required=True,
      help="""Path to the output PyTorch model (must include filename).""",
  )
  UpperCAmelCase_   : List[str]   			= parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



"""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)




