

import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__A      		=     logging.get_logger(__name__)


__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
__A      		=     {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__A      		=     {
    '''allenai/led-base-16384''': 1_63_84,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 				snake_case_()							->     int:



  """simple docstring"""






  _snake_case						=							(
      list(range(ord('''!'''					)			,	ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)			,	ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)			,	ord('''ÿ'''					) + 1					)					)
  )
  _snake_case						=							bs[:]
  _snake_case						=							0
  for b in range(2**8					):
    if b not in bs:
      bs.append(_UpperCamelCase					)
      cs.append(2**8 + n					)
      n += 1
  _snake_case						=							[chr(_UpperCamelCase					) for n in cs]
  return dict(zip(_UpperCamelCase			,	_UpperCamelCase					)					)



def 				snake_case_(_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							set()
  _snake_case						=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)					)
    _snake_case						=							char
  return pairs



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Dict										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		List[str]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Union[str, Any]										=    ["input_ids", "attention_mask"]



      def __init__(      self				:    Tuple      , A__				:    List[str]      , A__				:    Dict      , A__				:    Tuple="replace"      , A__				:    List[str]="<s>"      , A__				:    Union[str, Any]="</s>"      , A__				:    List[str]="</s>"      , A__				:    str="<s>"      , A__				:    Any="<unk>"      , A__				:    Dict="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Any=False      , **A__				:    str      , )   ->					int:
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else bos_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else eos_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else sep_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else cls_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else unk_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else mask_token

        super().__init__(
            errors=A__      , bos_token=A__      , eos_token=A__      , unk_token=A__      , sep_token=A__      , cls_token=A__      , pad_token=A__      , mask_token=A__      , add_prefix_space=A__      , **A__      , )

        with open(A__      , encoding='''utf-8'''		) as vocab_handle:
          _snake_case						=							json.load(A__		)
        _snake_case						=							{v: k for k, v in self.encoder.items()}
        _snake_case						=							errors  # how to handle errors in decoding
        _snake_case						=							bytes_to_unicode()
        _snake_case						=							{v: k for k, v in self.byte_encoder.items()}
        with open(A__      , encoding='''utf-8'''		) as merges_handle:
          _snake_case						=							merges_handle.read().split('''\n'''		)[1:-1]
        _snake_case						=							[tuple(merge.split()		) for merge in bpe_merges]
        _snake_case						=							dict(zip(A__      , range(len(A__		)		)		)		)
        _snake_case						=							{}
        _snake_case						=							add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _snake_case						=							re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''		)



      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					List[Any]:
        return len(self.encoder		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[Any]:
        return dict(self.encoder      , **self.added_tokens_encoder		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Optional[int]		)   ->					Tuple:
        if token in self.cache:
          return self.cache[token]
        _snake_case						=							tuple(A__		)
        _snake_case						=							get_pairs(A__		)

        if not pairs:
          return token

        while True:
          _snake_case						=							min(A__      , key=lambda A__		: self.bpe_ranks.get(A__      , float('''inf'''		)		)		)
          if bigram not in self.bpe_ranks:
            break
          _snake_case,					_snake_case						=							bigram
          _snake_case						=							[]
          _snake_case						=							0
          while i < len(A__		):
            try:
              _snake_case						=							word.index(A__      , A__		)
            except ValueError:
              new_word.extend(word[i:]		)
              break
            else:
              new_word.extend(word[i:j]		)
              _snake_case						=							j

            if word[i] == first and i < len(A__		) - 1 and word[i + 1] == second:
              new_word.append(first + second		)
              i += 2
            else:
              new_word.append(word[i]		)
              i += 1
          _snake_case						=							tuple(A__		)
          _snake_case						=							new_word
          if len(A__		) == 1:
            break
          else:
            _snake_case						=							get_pairs(A__		)
        _snake_case						=							''' '''.join(A__		)
        _snake_case						=							word
        return word



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    List[Any]		)   ->					Optional[Any]:
        _snake_case						=							[]
        for token in re.findall(self.pat      , A__		):
          _snake_case						=							''''''.join(
              self.byte_encoder[b] for b in token.encode('''utf-8'''		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A__		).split(''' '''		)		)
        return bpe_tokens



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Union[str, Any]		)   ->					Union[str, Any]:
        return self.encoder.get(A__      , self.encoder.get(self.unk_token		)		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    int		)   ->					Union[str, Any]:
        return self.decoder.get(A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Optional[Any]		)   ->					int:
        _snake_case						=							''''''.join(A__		)
        _snake_case						=							bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''      , errors=self.errors		)
        return text



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        if not os.path.isdir(A__		):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
          return
        _snake_case						=							os.path.join(
            A__      , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
        _snake_case						=							os.path.join(
            A__      , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

        with open(A__      , '''w'''      , encoding='''utf-8'''		) as f:
          f.write(json.dumps(self.encoder      , indent=2      , sort_keys=A__      , ensure_ascii=A__		) + '''\n'''		)

        _snake_case						=							0
        with open(A__      , '''w'''      , encoding='''utf-8'''		) as writer:
          writer.write('''#version: 0.2\n'''		)
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      , key=lambda A__		: kv[1]		):
            if index != token_index:
              logger.warning(
                  f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  ''' Please check that the tokenizer is not corrupted!'''		)
              _snake_case						=							token_index
            writer.write(''' '''.join(A__		) + '''\n'''		)
            index += 1

        return vocab_file, merge_file



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

        if token_ids_a is None:
          return [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1, 1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Dict      , A__				:    Optional[int]=False      , **A__				:    Tuple		)   ->					List[str]:
        _snake_case						=							kwargs.pop('''add_prefix_space'''      , self.add_prefix_space		)
        if (is_split_into_words or add_prefix_space) and (len(A__		) > 0 and not text[0].isspace()):
          _snake_case						=							''' ''' + text
        return (text, kwargs)







      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Union[Dict[str, EncodedInput], BatchEncoding]      , A__				:    Optional[int] = None      , A__				:    PaddingStrategy = PaddingStrategy.DO_NOT_PAD      , A__				:    Optional[int] = None      , A__				:    Optional[bool] = None      , )   ->					dict:
        _snake_case						=							super()._pad(
            encoded_inputs=A__      , max_length=A__      , padding_strategy=A__      , pad_to_multiple_of=A__      , return_attention_mask=A__      , )

        # Load from model defaults
        if return_attention_mask is None:
          _snake_case						=							'''attention_mask''' in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
          _snake_case						=							encoded_inputs[self.model_input_names[0]]
          # `global_attention_mask` need to have the same length as other (sequential) inputs.
          _snake_case						=							len(encoded_inputs['''global_attention_mask''']		) != len(A__		)

          if needs_to_be_padded:
            _snake_case						=							len(A__		) - len(encoded_inputs['''global_attention_mask''']		)

            if self.padding_side == "right":
              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
              _snake_case						=							(
                  encoded_inputs['''global_attention_mask'''] + [-1] * difference
              )
            elif self.padding_side == "left":
              _snake_case						=							[-1] * difference + encoded_inputs[
                  '''global_attention_mask'''
              ]
            else:
              raise ValueError('''Invalid padding strategy:''' + str(self.padding_side		)		)

        return encoded_inputs







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 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							torch.exp(_UpperCamelCase					)
  _snake_case						=							torch.sum(_UpperCamelCase			,	dim=1					)  # sum of exp(x_i)
  _snake_case						=							torch.sum(x * exp_x			,	dim=1					)  # sum of x_i * exp(x_i)
  return torch.log(_UpperCamelCase					) - B / A



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Tuple      , A__				:    int		)   ->					Tuple:
        super().__init__()
        _snake_case						=							config.output_attentions
        _snake_case						=							config.output_hidden_states
        _snake_case						=							nn.ModuleList([BertLayer(A__		) for _ in range(config.num_hidden_layers		)]		)
        _snake_case						=							nn.ModuleList([BertHighway(A__		) for _ in range(config.num_hidden_layers		)]		)

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



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any		)   ->					Any:
        if (type(A__		) is float) or (type(A__		) is int):
          for i in range(len(self.early_exit_entropy		)		):
            _snake_case						=							x
        else:
          _snake_case						=							x



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Tuple		)   ->					int:
        _snake_case						=							pooler.state_dict()
        for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
            param.copy_(loaded_model[name]		)







      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[int]      , A__				:    Dict=None      , A__				:    List[str]=None      , A__				:    Union[str, Any]=None      , A__				:    Dict=None      , )   ->					Dict:
        _snake_case						=							()
        _snake_case						=							()
        _snake_case						=							()
        for i, layer_module in enumerate(self.layer		):
          if self.output_hidden_states:
            _snake_case						=							all_hidden_states + (hidden_states,)

          _snake_case						=							layer_module(
              A__      , A__      , head_mask[i]      , A__      , A__		)
          _snake_case						=							layer_outputs[0]

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

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

          _snake_case						=							self.highway[i](A__		)
          # logits, pooled_output

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

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

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

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

        _snake_case						=							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). "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , A__				:    Any		)   ->					str:
        super().__init__(A__		)
        _snake_case						=							config

        _snake_case						=							BertEmbeddings(A__		)
        _snake_case						=							DeeBertEncoder(A__		)
        _snake_case						=							BertPooler(A__		)

        self.init_weights()



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[Any]:
        self.encoder.init_highway_pooler(self.pooler		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        return self.embeddings.word_embeddings



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					str:
        _snake_case						=							value



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







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

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

        if attention_mask is None:
          _snake_case						=							torch.ones(A__      , device=A__		)
        if encoder_attention_mask is None:
          _snake_case						=							torch.ones(A__      , device=A__		)
        if token_type_ids is None:
          _snake_case						=							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.
        _snake_case						=							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:
          _snake_case						=							encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
          _snake_case						=							encoder_attention_mask[:, None, None, :]

        _snake_case						=							encoder_extended_attention_mask.to(
            dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
        _snake_case						=							(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]
        _snake_case						=							self.get_head_mask(A__      , self.config.num_hidden_layers		)

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

        _snake_case						=							(
            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   lowercase_   (  __lowercase					):



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



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Any      , A__				:    int		)   ->					Optional[Any]:
        super().__init__()
        _snake_case						=							BertPooler(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , config.num_labels		)







      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					Optional[int]:
        # Pooler
        _snake_case						=							encoder_outputs[0]
        _snake_case						=							self.pooler(A__		)
        # "return" pooler_output

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

        # Dropout and classification
        _snake_case						=							bmodel_output[1]

        _snake_case						=							self.dropout(A__		)
        _snake_case						=							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. "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    List[str]      , A__				:    Optional[int]		)   ->					int:
        super().__init__(A__		)
        _snake_case						=							config.num_labels
        _snake_case						=							config.num_hidden_layers

        _snake_case						=							DeeBertModel(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , self.config.num_labels		)

        self.init_weights()







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[Any]=None      , A__				:    List[Any]=None      , A__				:    Optional[int]=None      , A__				:    List[Any]=None      , A__				:    List[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    List[Any]=-1      , A__				:    str=False      , )   ->					Dict:
        _snake_case						=							self.num_layers
        try:
          _snake_case						=							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

          _snake_case						=							outputs[1]

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

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

          # work with highway exits
          _snake_case						=							[]
          for highway_exit in outputs[-1]:
            _snake_case						=							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
              _snake_case						=							MSELoss()
              _snake_case						=							loss_fct(highway_logits.view(-1		)      , labels.view(-1		)		)
            else:
              _snake_case						=							CrossEntropyLoss()
              _snake_case						=							loss_fct(highway_logits.view(-1      , self.num_labels		)      , labels.view(-1		)		)
            highway_losses.append(A__		)

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

