




import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


a_      						=   logging.get_logger(__name__)  # pylint: disable=invalid-name


a_      						=   "lfs-multipart-upload"

class        _lowerCamelCase  (	__UpperCAmelCase      ):

      """simple docstring"""

      @staticmethod
      def 					snake_case		(   snake_case    :       ArgumentParser ):
          __UpperCamelCase     						=  parser.add_parser(
              '''lfs-enable-largefiles'''       ,				help=(
                  '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
              )       ,				)
          enable_parser.add_argument('''path'''       ,				type=_lowerCamelCase       ,				help='''Local path to repository you want to configure.''' )
          enable_parser.set_defaults(func=lambda snake_case : LfsEnableCommand(_lowerCamelCase ) )

          __UpperCamelCase     						=  parser.add_parser(
              _lowerCamelCase       ,				help=(
                  '''Deprecated: use `huggingface-cli` instead. '''
                  '''Command will get called by git-lfs, do not call it directly.'''
              )       ,				)
          upload_parser.set_defaults(func=lambda snake_case : LfsUploadCommand(_lowerCamelCase ) )







class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       Any ):
          __UpperCamelCase     						=  args







      def 					snake_case		(   self    :       Optional[int] ):
          warnings.warn(
              '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.''' )
          __UpperCamelCase     						=  os.path.abspath(self.args.path )
          if not os.path.isdir(_lowerCamelCase ):
              print('''This does not look like a valid git repo.''' )
              exit(1 )
          subprocess.run(
              '''git config lfs.customtransfer.multipart.path transformers-cli'''.split()       ,				check=_lowerCamelCase       ,				cwd=_lowerCamelCase )
          subprocess.run(
              F"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split()       ,				check=_lowerCamelCase       ,				cwd=_lowerCamelCase       ,				)
          print('''Local repo set up for largefiles''' )







def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Optional[int]:

    """simple docstring"""


    __UpperCamelCase     						=  json.dumps(lowercase_					) + '''\n'''
    sys.stdout.write(lowercase_					)
    sys.stdout.flush()




def     __SCREAMING_SNAKE_CASE      (     )				->		Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase     						=  json.loads(sys.stdin.readline().strip()					)

    if "terminate" in (msg.get('''type'''					), msg.get('''event'''					)):
        # terminate message received
        return None

    if msg.get('''event'''					) not in ("download", "upload"):
        logger.critical('''Received unexpected message'''					)
        sys.exit(1					)

    return msg

class        _lowerCamelCase  (	__UpperCAmelCase      ):

      """simple docstring"""

      def __init__(   self    :       int       ,				snake_case    :       str       ,				snake_case    :       int       ,				snake_case    :       int ):
          __UpperCamelCase     						=  filepath
          __UpperCamelCase     						=  seek_from
          __UpperCamelCase     						=  read_limit
          __UpperCamelCase     						=  0







      def __enter__(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  open(self.filepath       ,				'''rb''' )
          self.f.seek(self.seek_from )
          return self







      def __len__(   self    :       List[str] ):
          __UpperCamelCase     						=  os.fstat(self.f.fileno() ).st_size
          return min(self.read_limit       ,				total_length - self.seek_from )







      def 					snake_case		(   self    :       str       ,				snake_case    :       Dict=-1 ):
          if self.n_seen >= self.read_limit:
              return b""
          __UpperCamelCase     						=  self.read_limit - self.n_seen
          __UpperCamelCase     						=  self.f.read(remaining_amount if n < 0 else min(_lowerCamelCase       ,				_lowerCamelCase ) )
          self.n_seen += len(_lowerCamelCase )
          return data







      def __iter__(   self    :       List[Any] ):
          yield self.read(n=4 * 1024 * 1024 )







      def __exit__(   self    :       List[str]       ,				*snake_case    :       Tuple ):
          self.f.close()







class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       int       ,				snake_case    :       str ):
          __UpperCamelCase     						=  args







      def 					snake_case		(   self    :       Tuple ):
          # Immediately after invoking a custom transfer process, git-lfs
          # sends initiation data to the process over stdin.
          # This tells the process useful information about the configuration.
          __UpperCamelCase     						=  json.loads(sys.stdin.readline().strip() )
          if not (init_msg.get('''event''' ) == "init" and init_msg.get('''operation''' ) == "upload"):
              write_msg({'''error''': {'''code''': 32, '''message''': '''Wrong lfs init operation'''}} )
              sys.exit(1 )

          # The transfer process should use the information it needs from the
          # initiation structure, and also perform any one-off setup tasks it
          # needs to do. It should then respond on stdout with a simple empty
          # confirmation structure, as follows:
          write_msg({} )

          # After the initiation exchange, git-lfs will send any number of
          # transfer requests to the stdin of the transfer process, in a serial sequence.
          while True:
              __UpperCamelCase     						=  read_msg()
              if msg is None:
                  # When all transfers have been processed, git-lfs will send
                  # a terminate event to the stdin of the transfer process.
                  # On receiving this message the transfer process should
                  # clean up and terminate. No response is expected.
                  sys.exit(0 )

              __UpperCamelCase     						=  msg['''oid''']
              __UpperCamelCase     						=  msg['''path''']
              __UpperCamelCase     						=  msg['''action''']['''href''']
              __UpperCamelCase     						=  msg['''action''']['''header''']
              __UpperCamelCase     						=  int(header.pop('''chunk_size''' ) )
              __UpperCamelCase     						=  list(header.values() )

              __UpperCamelCase     						=  []
              for i, presigned_url in enumerate(_lowerCamelCase ):
                  with FileSlice(_lowerCamelCase       ,				seek_from=i * chunk_size       ,				read_limit=_lowerCamelCase ) as data:
                      __UpperCamelCase     						=  requests.put(_lowerCamelCase       ,				data=_lowerCamelCase )
                      r.raise_for_status()
                      parts.append(
                          {
                              '''etag''': r.headers.get('''etag''' ),
                              '''partNumber''': i + 1,
                          } )
                      # In order to support progress reporting while data is uploading / downloading,
                      # the transfer process should post messages to stdout
                      write_msg(
                          {
                              '''event''': '''progress''',
                              '''oid''': oid,
                              '''bytesSoFar''': (i + 1) * chunk_size,
                              '''bytesSinceLast''': chunk_size,
                          } )
                      # Not precise but that's ok.

              __UpperCamelCase     						=  requests.post(
                  _lowerCamelCase       ,				json={
                      '''oid''': oid,
                      '''parts''': parts,
                  }       ,				)
              r.raise_for_status()

              write_msg({'''event''': '''complete''', '''oid''': oid} )













"""simple docstring"""





import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
    from apex import amp

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


_lowerCamelCase       =	logging.getLogger(__name__)





@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Whether to log verbose messages or not.'''} ,     )







def 		lowerCAmelCase_	(  lowercase_   :							ModelArguments ,      lowercase_   :							TrainingArguments		):

 '''simple docstring'''






 logging.basicConfig(
     format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,      datefmt='''%m/%d/%Y %H:%M:%S''' ,      handlers=[logging.StreamHandler(sys.stdout		)] ,      )
 __SCREAMING_SNAKE_CASE :			Dict												=       logging.WARNING
 if model_args.verbose_logging:
  __SCREAMING_SNAKE_CASE :			List[Any]												=       logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank		):
  __SCREAMING_SNAKE_CASE :			Any												=       logging.INFO
 logger.setLevel(lowercase_		)





@dataclass
class 			snake_case		:
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
      lowerCamelCase__						= field(
          default='''train''' ,     metadata={
              '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
          } ,     )
      lowerCamelCase__						= field(
          default='''validation''' ,     metadata={
              '''help''': (
                  '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
              )
          } ,     )
      lowerCamelCase__						= field(
          default='''text''' ,     metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''} ,     )
      lowerCamelCase__						= field(
          default='''file''' ,     metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''} ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''} ,     )
      lowerCamelCase__						= field(
          default='''librispeech''' ,     metadata={
              '''help''': (
                  '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
                  ''' \'buckwalter\'.'''
              )
          } ,     )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}   )
      lowerCamelCase__						= field(
          default=__UpperCAmelCase ,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''} ,     )







@dataclass
class 			snake_case		:
      lowerCamelCase__						= False
      lowerCamelCase__						= None
      lowerCamelCase__						= "|"
      lowerCamelCase__						= field(default_factory=__UpperCAmelCase   )
      lowerCamelCase__						= field(default_factory=__UpperCAmelCase   )
      lowerCamelCase__						= None

      @classmethod
      def 						SCREAMING_SNAKE_CASE_			(			cls   :Any						,							_lowerCamelCase   :str						):
       if name == "librispeech":
        return cls()
       if name == "timit":
        return cls(
            do_lower_case=_lowerCamelCase						,							translation_table=str.maketrans({'''-''': ''' '''}						)						,							)
       if name == "buckwalter":
        __SCREAMING_SNAKE_CASE :			int												=       {
            '''-''': ''' ''',  # sometimes used to represent pauses
            '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(_lowerCamelCase						).parent.joinpath('''vocab/buckwalter.json'''						)						,							word_delimiter_token='''/'''						,							translation_table=str.maketrans(_lowerCamelCase						)						,							words_to_remove={'''sil'''}						,							untransliterator=arabic.buckwalter.untransliterate						,							)
       raise ValueError(f'''Unsupported orthography: \'{name}\'.'''						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :str						):
       # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
       if len(self.translation_table						) > 0:
        __SCREAMING_SNAKE_CASE :			int												=       text.translate(self.translation_table						)
       if len(self.words_to_remove						) == 0:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       ''' '''.join(text.split()						)  # clean up whitespaces
       else:
        __SCREAMING_SNAKE_CASE :			List[str]												=       ''' '''.join(w for w in text.split() if w not in self.words_to_remove						)  # and clean up whilespaces
       return text



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						,							_lowerCamelCase   :ModelArguments						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						)
       if self.vocab_file:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       WavaVecaCTCTokenizer(
            self.vocab_file						,							cache_dir=model_args.cache_dir						,							do_lower_case=self.do_lower_case						,							word_delimiter_token=self.word_delimiter_token						,							)
       else:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       WavaVecaCTCTokenizer.from_pretrained(
            model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							do_lower_case=self.do_lower_case						,							word_delimiter_token=self.word_delimiter_token						,							)
       return WavaVecaProcessor(_lowerCamelCase						,							_lowerCamelCase						)







@dataclass
class 			snake_case		:
      lowerCamelCase__						= 42
      lowerCamelCase__						= True
      lowerCamelCase__						= None
      lowerCamelCase__						= None
      lowerCamelCase__						= None
      lowerCamelCase__						= None

      def __call__(			self   :Tuple						,							_lowerCamelCase   :List[Dict[str, Union[List[int], torch.Tensor]]]						):
       # split inputs and labels since they have to be of different lenghts and need
       # different padding methods
       __SCREAMING_SNAKE_CASE :			int												=       [{'''input_values''': feature['''input_values''']} for feature in features]
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       [{'''input_ids''': feature['''labels''']} for feature in features]

       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.processor.pad(
           _lowerCamelCase						,							padding=self.padding						,							max_length=self.max_length						,							pad_to_multiple_of=self.pad_to_multiple_of						,							return_tensors='''pt'''						,							)
       __SCREAMING_SNAKE_CASE :			int												=       self.processor.pad(
           labels=_lowerCamelCase						,							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
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1						)						,							-1_0_0						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       labels

       return batch







class 			snake_case		(   __UpperCAmelCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :nn.Module						,							_lowerCamelCase   :Dict[str, Union[torch.Tensor, Any]]						):
       model.train()
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self._prepare_inputs(_lowerCamelCase						)

       if self.use_amp:
        with autocast():
         __SCREAMING_SNAKE_CASE :			Dict												=       self.compute_loss(_lowerCamelCase						,							_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.compute_loss(_lowerCamelCase						,							_lowerCamelCase						)

       if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
         __SCREAMING_SNAKE_CASE :			str												=       loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
         __SCREAMING_SNAKE_CASE :			List[Any]												=       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:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       loss / self.args.gradient_accumulation_steps

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

       return loss.detach()







def 		lowerCAmelCase_	(  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       parser.parse_args_into_dataclasses()
 configure_logger(lowercase_ ,      lowercase_		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       Orthography.from_name(data_args.orthography.lower()		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       orthography.create_processor(lowercase_		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       WavaVecaForCTC.from_pretrained(
     model_args.model_name_or_path ,      cache_dir=model_args.cache_dir ,      gradient_checkpointing=training_args.gradient_checkpointing ,      vocab_size=len(processor.tokenizer		) ,      )

 __SCREAMING_SNAKE_CASE :			Dict												=       datasets.load_dataset(
     data_args.dataset_name ,      data_args.dataset_config_name ,      split=data_args.train_split_name		)
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       datasets.load_dataset(
     data_args.dataset_name ,      data_args.dataset_config_name ,      split=data_args.validation_split_name		)

 __SCREAMING_SNAKE_CASE :			Tuple												=       datasets.load_metric('''wer'''		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(lowercase_		) == 1		)
 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       re.compile(  # remove characters not in vocabulary
     rF'''[^\s{re.escape(lowercase_		)}]''' ,      flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0 ,      )
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       []

 def prepare_example(lowercase_   :							List[str]		):  # TODO(elgeish) make use of multiprocessing?
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       librosa.load(example[data_args.speech_file_column] ,      sr=lowercase_		)
  if data_args.max_duration_in_seconds is not None:
   __SCREAMING_SNAKE_CASE :			List[Any]												=       len(example['''speech''']		) / example['''sampling_rate''']
  # Normalize and clean up text; order matters!
  __SCREAMING_SNAKE_CASE :			List[Any]												=       orthography.preprocess_for_training(example[data_args.target_text_column]		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       vocabulary_text_cleaner.sub('''''' ,      lowercase_		)
  if updated_text != example[data_args.target_text_column]:
   text_updates.append((example[data_args.target_text_column], updated_text)		)
   __SCREAMING_SNAKE_CASE :			Any												=       updated_text
  return example

 __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       train_dataset.map(lowercase_ ,      remove_columns=[data_args.speech_file_column]		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       val_dataset.map(lowercase_ ,      remove_columns=[data_args.speech_file_column]		)

 if data_args.max_duration_in_seconds is not None:

  def filter_by_max_duration(lowercase_   :							Optional[Any]		):
   return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

  __SCREAMING_SNAKE_CASE :			List[Any]												=       len(lowercase_		)
  __SCREAMING_SNAKE_CASE :			str												=       len(lowercase_		)
  __SCREAMING_SNAKE_CASE :			Any												=       train_dataset.filter(lowercase_ ,      remove_columns=['''duration_in_seconds''']		)
  __SCREAMING_SNAKE_CASE :			List[str]												=       val_dataset.filter(lowercase_ ,      remove_columns=['''duration_in_seconds''']		)
  if len(lowercase_		) > old_train_size:
   logger.warning(
       F'''Filtered out {len(lowercase_		) - old_train_size} train example(s) longer than'''
       F''' {data_args.max_duration_in_seconds} second(s).'''		)
  if len(lowercase_		) > old_val_size:
   logger.warning(
       F'''Filtered out {len(lowercase_		) - old_val_size} validation example(s) longer than'''
       F''' {data_args.max_duration_in_seconds} second(s).'''		)
 logger.info(F'''Split sizes: {len(lowercase_		)} train and {len(lowercase_		)} validation.'''		)

 logger.warning(F'''Updated {len(lowercase_		)} transcript(s) using \'{data_args.orthography}\' orthography rules.'''		)
 if logger.isEnabledFor(logging.DEBUG		):
  for original_text, updated_text in text_updates:
   logger.debug(F'''Updated text: "{original_text}" -> "{updated_text}"'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       None

 def prepare_dataset(lowercase_   :							Tuple		):
  # 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}.'''

  __SCREAMING_SNAKE_CASE :			List[Any]												=       processor(
      audio=batch['''speech'''] ,      text=batch[data_args.target_text_column] ,      sampling_rate=batch['''sampling_rate'''][0]		)
  batch.update(lowercase_		)
  return batch

 __SCREAMING_SNAKE_CASE :			Any												=       train_dataset.map(
     lowercase_ ,      batch_size=training_args.per_device_train_batch_size ,      batched=lowercase_ ,      num_proc=data_args.preprocessing_num_workers ,      )
 __SCREAMING_SNAKE_CASE :			Dict												=       val_dataset.map(
     lowercase_ ,      batch_size=training_args.per_device_train_batch_size ,      batched=lowercase_ ,      num_proc=data_args.preprocessing_num_workers ,      )

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       DataCollatorCTCWithPadding(processor=lowercase_ ,      padding=lowercase_		)

 def compute_metrics(lowercase_   :							int		):
  __SCREAMING_SNAKE_CASE :			List[Any]												=       pred.predictions
  __SCREAMING_SNAKE_CASE :			Any												=       np.argmax(lowercase_ ,      axis=-1		)

  __SCREAMING_SNAKE_CASE :			Tuple												=       processor.tokenizer.pad_token_id

  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       processor.batch_decode(lowercase_		)
  # we do not want to group tokens when computing the metrics
  __SCREAMING_SNAKE_CASE :			Dict												=       processor.batch_decode(pred.label_ids ,      group_tokens=lowercase_		)
  if logger.isEnabledFor(logging.DEBUG		):
   for reference, predicted in zip(lowercase_ ,      lowercase_		):
    logger.debug(F'''reference: "{reference}"'''		)
    logger.debug(F'''predicted: "{predicted}"'''		)
    if orthography.untransliterator is not None:
     logger.debug(F'''reference (untransliterated): "{orthography.untransliterator(lowercase_		)}"'''		)
     logger.debug(F'''predicted (untransliterated): "{orthography.untransliterator(lowercase_		)}"'''		)

  __SCREAMING_SNAKE_CASE :			str												=       wer_metric.compute(predictions=lowercase_ ,      references=lowercase_		)

  return {"wer": wer}

 if model_args.freeze_feature_extractor:
  model.freeze_feature_extractor()

 __SCREAMING_SNAKE_CASE :			str												=       CTCTrainer(
     model=lowercase_ ,      data_collator=lowercase_ ,      args=lowercase_ ,      compute_metrics=lowercase_ ,      train_dataset=lowercase_ ,      eval_dataset=lowercase_ ,      tokenizer=processor.feature_extractor ,      )

 trainer.train()


if __name__ == "__main__":
    main()

