


import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
  from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
  SCREAMING_SNAKE_CASE							    =							True
  from torch.cuda.amp import autocast

SCREAMING_SNAKE_CASE							    =							logging.getLogger(__name__)




def  a   (lowerCAmelCase__=None		,				lowerCAmelCase__=None   ):
   return field(default_factory=lambda: default		,				metadata=lowerCAmelCase__   )


@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}							)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={"""help""": """The dropout ratio for the attention probabilities."""}							)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""}							)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={
             """help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
         } ,					)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} ,					)
     _lowerCamelCase   =      field(
         default=0.05 ,					metadata={
             """help""": (
                 """Propability of each feature vector along the time axis to be chosen as the start of the vector"""
                 """span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
                 """vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
             )
         } ,					)
     _lowerCamelCase   =      field(default=0.0 ,					metadata={"""help""": """The LayerDrop probability."""}							)







@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
     _lowerCamelCase   =      field(
         default="""train+validation""" ,					metadata={
             """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
         } ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}							)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """The number of processes to use for the preprocessing."""} ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         } ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of validation examples to this """
                 """value if set."""
             )
         } ,					)
     _lowerCamelCase   =      list_field(
         default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] ,					metadata={"""help""": """A list of characters to remove from the transcripts."""} ,					)







@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      True
     _lowerCamelCase   =      None
     _lowerCamelCase   =      None
     _lowerCamelCase   =      None
     _lowerCamelCase   =      None


     def __call__(	self  ,       __A    ):
        # split inputs and labels since they have to be of different lenghts and need
        # different padding methods
        __a					=  [{"""input_values""": feature["""input_values"""]} for feature in features]
        __a					=  [{"""input_ids""": feature["""labels"""]} for feature in features]

        __a					=  self.processor.pad(
            __A  ,       padding=self.padding  ,       max_length=self.max_length  ,       pad_to_multiple_of=self.pad_to_multiple_of  ,       return_tensors="""pt"""  ,       )
        __a					=  self.processor.pad(
            labels=__A  ,       padding=self.padding  ,       max_length=self.max_length_labels  ,       pad_to_multiple_of=self.pad_to_multiple_of_labels  ,       return_tensors="""pt"""  ,       )

        # replace padding with -100 to ignore loss correctly
        __a					=  labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )  ,       -100    )

        __a					=  labels

        return batch







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        model.train()
        __a					=  self._prepare_inputs(__A    )

        if self.use_amp:
           with autocast():
              __a					=  self.compute_loss(__A  ,       __A    )
        else:
           __a					=  self.compute_loss(__A  ,       __A    )

        if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
              __a					=  loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
              __a					=  loss.sum() / (inputs["""labels"""] >= 0).sum()
           else:
              raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''    )

        if self.args.gradient_accumulation_steps > 1:
           __a					=  loss / self.args.gradient_accumulation_steps

        if self.use_amp:
           self.scaler.scale(__A    ).backward()
        elif self.use_apex:
           with amp.scale_loss(__A  ,       self.optimizer    ) as scaled_loss:
              scaled_loss.backward()
        elif self.deepspeed:
           self.deepspeed.backward(__A    )
        else:
           loss.backward()

        return loss.detach()







def  a   ():
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   __a					=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
   if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      __a	,    __a	,    __a					=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      __a	,    __a	,    __a					=  parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   __a					=  None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      __a					=  get_last_checkpoint(training_args.output_dir   )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
         raise ValueError(
             f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
             """Use --overwrite_output_dir to overcome."""   )
      elif last_checkpoint is not None:
         logger.info(
             f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""   )

    # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,				datefmt="""%m/%d/%Y %H:%M:%S"""		,				handlers=[logging.StreamHandler(sys.stdout   )]		,				)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank   ) else logging.WARN   )

   # Log on each process the small summary:
   logger.warning(
       f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
       + f'''distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'''   )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank   ):
      transformers.utils.logging.set_verbosity_info()
   logger.info("""Training/evaluation parameters %s"""		,				lowerCAmelCase__   )

   # Set seed before initializing model.
   set_seed(training_args.seed   )

   # Get the datasets:
   __a					=  datasets.load_dataset(
       """common_voice"""		,				data_args.dataset_config_name		,				split=data_args.train_split_name   )
   __a					=  datasets.load_dataset("""common_voice"""		,				data_args.dataset_config_name		,				split="""test"""   )

   # Create and save tokenizer
   __a					=  f'''[{''.join(data_args.chars_to_ignore   )}]'''

   def remove_special_characters(lowerCAmelCase__   ):
      __a					=  re.sub(lowerCAmelCase__		,				""""""		,				batch["""sentence"""]   ).lower() + """ """
      return batch

   __a					=  train_dataset.map(lowerCAmelCase__		,				remove_columns=["""sentence"""]   )
   __a					=  eval_dataset.map(lowerCAmelCase__		,				remove_columns=["""sentence"""]   )

   def extract_all_chars(lowerCAmelCase__   ):
      __a					=  """ """.join(batch["""text"""]   )
      __a					=  list(set(lowerCAmelCase__   )   )
      return {"vocab": [vocab], "all_text": [all_text]}

   __a					=  train_dataset.map(
       lowerCAmelCase__		,				batched=lowerCAmelCase__		,				batch_size=-1		,				keep_in_memory=lowerCAmelCase__		,				remove_columns=train_dataset.column_names		,				)
   __a					=  train_dataset.map(
       lowerCAmelCase__		,				batched=lowerCAmelCase__		,				batch_size=-1		,				keep_in_memory=lowerCAmelCase__		,				remove_columns=eval_dataset.column_names		,				)

   __a					=  list(set(vocab_train["""vocab"""][0]   ) | set(vocab_test["""vocab"""][0]   )   )
   __a					=  {v: k for k, v in enumerate(lowerCAmelCase__   )}
   __a					=  vocab_dict[""" """]
   del vocab_dict[" "]
   __a					=  len(lowerCAmelCase__   )
   __a					=  len(lowerCAmelCase__   )

   with open("""vocab.json"""		,				"""w"""   ) as vocab_file:
      json.dump(lowerCAmelCase__		,				lowerCAmelCase__   )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   __a					=  WavaVecaCTCTokenizer(
       """vocab.json"""		,				unk_token="""[UNK]"""		,				pad_token="""[PAD]"""		,				word_delimiter_token="""|"""		,				)
   __a					=  WavaVecaFeatureExtractor(
       feature_size=1		,				sampling_rate=16_000		,				padding_value=0.0		,				do_normalize=lowerCAmelCase__		,				return_attention_mask=lowerCAmelCase__   )
   __a					=  WavaVecaProcessor(feature_extractor=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__   )
   __a					=  WavaVecaForCTC.from_pretrained(
       model_args.model_name_or_path		,				cache_dir=model_args.cache_dir		,				activation_dropout=model_args.activation_dropout		,				attention_dropout=model_args.attention_dropout		,				hidden_dropout=model_args.hidden_dropout		,				feat_proj_dropout=model_args.feat_proj_dropout		,				mask_time_prob=model_args.mask_time_prob		,				gradient_checkpointing=training_args.gradient_checkpointing		,				layerdrop=model_args.layerdrop		,				ctc_loss_reduction="""mean"""		,				pad_token_id=processor.tokenizer.pad_token_id		,				vocab_size=len(processor.tokenizer   )		,				)

   if data_args.max_train_samples is not None:
      __a					=  min(len(lowerCAmelCase__   )		,				data_args.max_train_samples   )
      __a					=  train_dataset.select(range(lowerCAmelCase__   )   )

   if data_args.max_val_samples is not None:
      __a					=  eval_dataset.select(range(data_args.max_val_samples   )   )

   __a					=  torchaudio.transforms.Resample(48_000		,				16_000   )

   # Preprocessing the datasets.
   # We need to read the aduio files as arrays and tokenize the targets.
   def speech_file_to_array_fn(lowerCAmelCase__   ):
      __a	,    __a					=  torchaudio.load(batch["""path"""]   )
      __a					=  resampler(lowerCAmelCase__   ).squeeze().numpy()
      __a					=  16_000
      __a					=  batch["""text"""]
      return batch

   __a					=  train_dataset.map(
       lowerCAmelCase__		,				remove_columns=train_dataset.column_names		,				num_proc=data_args.preprocessing_num_workers		,				)
   __a					=  eval_dataset.map(
       lowerCAmelCase__		,				remove_columns=eval_dataset.column_names		,				num_proc=data_args.preprocessing_num_workers		,				)

   def prepare_dataset(lowerCAmelCase__   ):
      # check that all files have the correct sampling rate
      assert (
          len(set(batch["""sampling_rate"""]   )   ) == 1
      ), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

      __a					=  processor(
          audio=batch["""speech"""]		,				text=batch["""target_text"""]		,				sampling_rate=batch["""sampling_rate"""][0]   )
      batch.update(lowerCAmelCase__   )
      return batch

   __a					=  train_dataset.map(
       lowerCAmelCase__		,				remove_columns=train_dataset.column_names		,				batch_size=training_args.per_device_train_batch_size		,				batched=lowerCAmelCase__		,				num_proc=data_args.preprocessing_num_workers		,				)
   __a					=  eval_dataset.map(
       lowerCAmelCase__		,				remove_columns=eval_dataset.column_names		,				batch_size=training_args.per_device_train_batch_size		,				batched=lowerCAmelCase__		,				num_proc=data_args.preprocessing_num_workers		,				)

   # Metric
   __a					=  datasets.load_metric("""wer"""   )

   def compute_metrics(lowerCAmelCase__   ):
      __a					=  pred.predictions
      __a					=  np.argmax(lowerCAmelCase__		,				axis=-1   )

      __a					=  processor.tokenizer.pad_token_id

      __a					=  processor.batch_decode(lowerCAmelCase__   )
      # we do not want to group tokens when computing the metrics
      __a					=  processor.batch_decode(pred.label_ids		,				group_tokens=lowerCAmelCase__   )

      __a					=  wer_metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   # Data collator
   __a					=  DataCollatorCTCWithPadding(processor=lowerCAmelCase__		,				padding=lowerCAmelCase__   )

   # Initialize our Trainer
   __a					=  CTCTrainer(
       model=lowerCAmelCase__		,				data_collator=lowerCAmelCase__		,				args=lowerCAmelCase__		,				compute_metrics=lowerCAmelCase__		,				train_dataset=train_dataset if training_args.do_train else None		,				eval_dataset=eval_dataset if training_args.do_eval else None		,				tokenizer=processor.feature_extractor		,				)

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         __a					=  last_checkpoint
      elif os.path.isdir(model_args.model_name_or_path   ):
         __a					=  model_args.model_name_or_path
      else:
         __a					=  None

      # Save the feature_extractor and the tokenizer
      if is_main_process(training_args.local_rank   ):
         processor.save_pretrained(training_args.output_dir   )

      __a					=  trainer.train(resume_from_checkpoint=lowerCAmelCase__   )
      trainer.save_model()

      __a					=  train_result.metrics
      __a					=  (
          data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__   )
      )
      __a					=  min(lowerCAmelCase__		,				len(lowerCAmelCase__   )   )

      trainer.log_metrics("""train"""		,				lowerCAmelCase__   )
      trainer.save_metrics("""train"""		,				lowerCAmelCase__   )
      trainer.save_state()

   # Evaluation
   __a					=  {}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""   )
      __a					=  trainer.evaluate()
      __a					=  data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCAmelCase__   )
      __a					=  min(lowerCAmelCase__		,				len(lowerCAmelCase__   )   )

      trainer.log_metrics("""eval"""		,				lowerCAmelCase__   )
      trainer.save_metrics("""eval"""		,				lowerCAmelCase__   )

   return results


if __name__ == "__main__":
  main()











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 		lowercase__  (				_UpperCamelCase) ->			List[str]:
      """simple docstring"""

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





class 					A__     (     nn.Module ):

     '''simple docstring'''






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






           """simple docstring"""




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

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    List[Any] ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    int=None   ,						_SCREAMING_SNAKE_CASE      :    int=None   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=None   ,						):






           """simple docstring"""




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

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

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

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

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

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

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

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

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

           UpperCamelCase					       =  outputs + (all_highway_exits,)
           return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """   ,    __snake_case   ,    )
class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    Any   ,						_SCREAMING_SNAKE_CASE      :    int ):






           """simple docstring"""




           super().__init__(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  config

           UpperCamelCase					       =  BertEmbeddings(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  DeeBertEncoder(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  BertPooler(_SCREAMING_SNAKE_CASE )

           self.init_weights()







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




           self.encoder.init_highway_pooler(self.pooler )







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           return self.embeddings.word_embeddings







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  value







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Tuple ):






           """simple docstring"""




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







     @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE )
     def   _SCREAMING_SNAKE_CASE     ( self      :    Union[str, Any]   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    Tuple=None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						):






           """simple docstring"""




           if input_ids is not None and inputs_embeds is not None:
                 raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
           elif input_ids is not None:
                 UpperCamelCase					       =  input_ids.size()
           elif inputs_embeds is not None:
                 UpperCamelCase					       =  inputs_embeds.size()[:-1]
           else:
                 raise ValueError('You have to specify either input_ids or inputs_embeds' )

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

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

           # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
           # ourselves in which case we just need to make it broadcastable to all heads.
           UpperCamelCase					       =  self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           # If a 2D ou 3D attention mask is provided for the cross-attention
           # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
           if encoder_attention_mask.dim() == 3:
                 UpperCamelCase					       =  encoder_attention_mask[:, None, :, :]
           if encoder_attention_mask.dim() == 2:
                 UpperCamelCase					       =  encoder_attention_mask[:, None, None, :]

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

           # Prepare head mask if needed
           # 1.0 in head_mask indicate we keep the head
           # attention_probs has shape bsz x n_heads x N x N
           # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
           # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
           UpperCamelCase					       =  self.get_head_mask(_SCREAMING_SNAKE_CASE   ,						self.config.num_hidden_layers )

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

           UpperCamelCase					       =  (
               sequence_output,
               pooled_output,
           ) + encoder_outputs[
               1:
           ]  # add hidden_states and attentions if they are here
           return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class 					A__     (     __snake_case ):

     '''simple docstring'''






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






           """simple docstring"""




           UpperCamelCase					       =  message
           UpperCamelCase					       =  exit_layer  # start from 1!





class 					A__     (     nn.Module ):

     '''simple docstring'''






     def __init__( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Optional[Any] ):






           """simple docstring"""




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

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

           # Dropout and classification
           UpperCamelCase					       =  bmodel_output[1]

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

           return logits, pooled_output





@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """   ,    __snake_case   ,    )
class 					A__     (     __snake_case ):

     '''simple docstring'''






     def __init__( self      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Dict ):






           """simple docstring"""




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

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

           self.init_weights()







     @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE )
     def   _SCREAMING_SNAKE_CASE     ( self      :    str   ,						_SCREAMING_SNAKE_CASE      :    Any=None   ,						_SCREAMING_SNAKE_CASE      :    int=None   ,						_SCREAMING_SNAKE_CASE      :    Tuple=None   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=None   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						_SCREAMING_SNAKE_CASE      :    List[str]=None   ,						_SCREAMING_SNAKE_CASE      :    str=-1   ,						_SCREAMING_SNAKE_CASE      :    str=False   ,						):






           """simple docstring"""




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

                 UpperCamelCase					       =  outputs[1]

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

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

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

                 if train_highway:
                       UpperCamelCase					       =  (sum(highway_losses[:-1] ),) + outputs
                       # exclude the final highway, of course
                 else:
                       UpperCamelCase					       =  (loss,) + outputs
           if not self.training:
                 UpperCamelCase					       =  outputs + ((original_entropy, highway_entropy), exit_layer)
                 if output_layer >= 0:
                       UpperCamelCase					       =  (
                           (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
                       )  # use the highway of the last layer

           return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)



