

import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__A      		=     '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__A      		=     direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__A      		=     re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__A      		=     re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A      		=     re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A      		=     [
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''			,	_UpperCamelCase					)
  return [m.group(0					) for m in matches]



def 				snake_case_()							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  _snake_case						=							{
      config.replace('''Config'''			,	''''''					): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  _snake_case						=							collections.defaultdict(_UpperCamelCase					)
  _snake_case						=							collections.defaultdict(_UpperCamelCase					)
  _snake_case						=							collections.defaultdict(_UpperCamelCase					)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_UpperCamelCase					):
    _snake_case						=							None
    if _re_tf_models.match(_UpperCamelCase					) is not None:
      _snake_case						=							tf_models
      _snake_case						=							_re_tf_models.match(_UpperCamelCase					).groups()[0]
    elif _re_flax_models.match(_UpperCamelCase					) is not None:
      _snake_case						=							flax_models
      _snake_case						=							_re_flax_models.match(_UpperCamelCase					).groups()[0]
    elif _re_pt_models.match(_UpperCamelCase					) is not None:
      _snake_case						=							pt_models
      _snake_case						=							_re_pt_models.match(_UpperCamelCase					).groups()[0]

    if lookup_dict is not None:
      while len(_UpperCamelCase					) > 0:
        if attr_name in model_prefix_to_model_type:
          _snake_case						=							True
          break
        # Try again after removing the last word in the name
        _snake_case						=							''''''.join(camel_case_split(_UpperCamelCase					)[:-1]					)

  _snake_case						=							set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
  _snake_case						=							list(_UpperCamelCase					)
  all_models.sort()

  _snake_case						=							{'''model_type''': all_models}
  _snake_case						=							[pt_models[t] for t in all_models]
  _snake_case						=							[tf_models[t] for t in all_models]
  _snake_case						=							[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  _snake_case						=							{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      _snake_case						=							'''AutoProcessor'''
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      _snake_case						=							'''AutoTokenizer'''
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      _snake_case						=							'''AutoFeatureExtractor'''
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      _snake_case						=							'''AutoTokenizer'''

  _snake_case						=							[processors[t] for t in all_models]

  return pd.DataFrame(_UpperCamelCase					)



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    _snake_case						=							[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
    _snake_case						=							[auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					):
      # The type of pipeline may not exist in this framework
      if not hasattr(_UpperCamelCase			,	_UpperCamelCase					):
        continue
      # First extract all model_names
      _snake_case						=							[]
      for name in getattr(_UpperCamelCase			,	_UpperCamelCase					).values():
        if isinstance(_UpperCamelCase			,	_UpperCamelCase					):
          model_names.append(_UpperCamelCase					)
        else:
          model_names.extend(list(_UpperCamelCase					)					)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}					)

  return table



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							get_frameworks_table()
  _snake_case						=							Dataset.from_pandas(_UpperCamelCase					)

  _snake_case						=							hf_hub_download(
      '''huggingface/transformers-metadata'''			,	'''pipeline_tags.json'''			,	repo_type='''dataset'''			,	token=_UpperCamelCase					)
  _snake_case						=							Dataset.from_json(_UpperCamelCase					)
  _snake_case						=							{
      tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
      for i in range(len(_UpperCamelCase					)					)
  }
  _snake_case						=							update_pipeline_and_auto_class_table(_UpperCamelCase					)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  _snake_case						=							sorted(table.keys()					)
  _snake_case						=							pd.DataFrame(
      {
          '''model_class''': model_classes,
          '''pipeline_tag''': [table[m][0] for m in model_classes],
          '''auto_class''': [table[m][1] for m in model_classes],
      }					)
  _snake_case						=							Dataset.from_pandas(_UpperCamelCase					)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_UpperCamelCase			,	'''frameworks.json'''					)					)
    tags_dataset.to_json(os.path.join(_UpperCamelCase			,	'''pipeline_tags.json'''					)					)

    if commit_sha is not None:
      _snake_case						=							(
          F"""Update with commit {commit_sha}\n\nSee: """
          F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      _snake_case						=							'''Update'''

    upload_folder(
        repo_id='''huggingface/transformers-metadata'''			,	folder_path=_UpperCamelCase			,	repo_type='''dataset'''			,	token=_UpperCamelCase			,	commit_message=_UpperCamelCase			,	)



def 				snake_case_()							->     List[Any]:



  """simple docstring"""






  _snake_case						=							{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  _snake_case						=							transformers_module.pipelines.SUPPORTED_TASKS
  _snake_case						=							[]
  for key in pipeline_tasks:
    if key not in in_table:
      _snake_case						=							pipeline_tasks[key]['''pt''']
      if isinstance(_UpperCamelCase			,	(list, tuple)					):
        _snake_case						=							model[0]
      _snake_case						=							model.__name__
      if model not in in_table.values():
        missing.append(_UpperCamelCase					)

  if len(_UpperCamelCase					) > 0:
    _snake_case						=							''', '''.join(_UpperCamelCase					)
    raise ValueError(
        '''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
        F"""`utils/update_metadata.py`: {msg}. Please add them!"""					)


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()
 parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
 parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
 parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
 __A      		=     parser.parse_args()

 if args.check_only:
  check_pipeline_tags()
 else:
  update_metadata(args.token, args.commit_sha)




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)

