





def  __UpperCamelCase    (						snake_case							,			snake_case = 1_0_0_0      )    ->		str:


   '''simple docstring'''







   if (not isinstance(snake_case							,			snake_case      )) or (digit_position <= 0):
      raise ValueError('''Digit position must be a positive integer'''      )
   elif (not isinstance(snake_case							,			snake_case      )) or (precision < 0):
      raise ValueError('''Precision must be a nonnegative integer'''      )

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   __A    =  (
       4 * _subsum(snake_case							,			1							,			snake_case      )
       - 2 * _subsum(snake_case							,			4							,			snake_case      )
       - _subsum(snake_case							,			5							,			snake_case      )
       - _subsum(snake_case							,			6							,			snake_case      )
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 1_6      )      )[2:]
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case      )    ->		float:


   '''simple docstring'''







   __A    =  0.0
   for sum_index in range(digit_pos_to_extract + precision      ):
      __A    =  8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         __A    =  pow(
             1_6							,			digit_pos_to_extract - 1 - sum_index							,			snake_case      )
      else:
         __A    =  pow(1_6							,			digit_pos_to_extract - 1 - sum_index      )
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
    import doctest

    doctest.testmod()








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"""):
    _UpperCamelCase       :				List[Any]           =			True
    from torch.cuda.amp import autocast


_UpperCamelCase       :				Optional[Any]           =			logging.getLogger(__name__)







@dataclass
class 		_lowerCAmelCase:

  """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=_a						,       metadata={'''help''': '''Whether to log verbose messages or not.'''}						,       )
def  __UpperCamelCase    (						snake_case							,			snake_case      )    ->		int:


   '''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      )]							,			)
   __A    =  logging.WARNING
   if model_args.verbose_logging:
      __A    =  logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank      ):
      __A    =  logging.INFO
   logger.setLevel(snake_case      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       field(
      default=_a						,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''})
  lowerCamelCase__     	=       field(
      default=_a						,       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=_a						,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}						,       )
  lowerCamelCase__     	=       field(
      default=_a						,       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=_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.'''}						,       )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       False
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       "|"
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       field(default_factory=_a)
  lowerCamelCase__     	=       None

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

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   str:
     # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
     if len(self.translation_table      ) > 0:
        __A    =  text.translate(self.translation_table      )
     if len(self.words_to_remove      ) == 0:
        __A    =  ''' '''.join(text.split()      )  # clean up whitespaces
     else:
        __A    =  ''' '''.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    ,							UpperCAmelCase      )->   WavaVecaProcessor:
     __A    =  WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path    ,							cache_dir=model_args.cache_dir      )
     if self.vocab_file:
        __A    =  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:
        __A    =  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(UpperCAmelCase    ,							UpperCAmelCase      )







@dataclass
class 		_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__     	=       42
  lowerCamelCase__     	=       True
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None
  lowerCamelCase__     	=       None

  def __call__(   self    ,							UpperCAmelCase      )->   Dict[str, torch.Tensor]:
     # 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(
         UpperCAmelCase    ,							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=UpperCAmelCase    ,							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      )    ,							-1_00      )

     __A    =  labels

     return batch







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   torch.Tensor:
     model.train()
     __A    =  self._prepare_inputs(UpperCAmelCase      )

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

     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(UpperCAmelCase      ).backward()
     elif self.use_apex:
        with amp.scale_loss(UpperCAmelCase    ,							self.optimizer      ) as scaled_loss:
           scaled_loss.backward()
     elif self.deepspeed:
        self.deepspeed.backward(UpperCAmelCase      )
     else:
        loss.backward()

     return loss.detach()
def  __UpperCamelCase    (						)    ->		Any:


   '''simple docstring'''







   __A    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

   __A   ,		__A   ,		__A    =  parser.parse_args_into_dataclasses()
   configure_logger(snake_case							,			snake_case      )

   __A    =  Orthography.from_name(data_args.orthography.lower()      )
   __A    =  orthography.create_processor(snake_case      )
   __A    =  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      )							,			)

   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.train_split_name      )
   __A    =  datasets.load_dataset(
       data_args.dataset_name							,			data_args.dataset_config_name							,			split=data_args.validation_split_name      )

   __A    =  datasets.load_metric('''wer'''      )
   __A    =  processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
   __A    =  ''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(snake_case      ) == 1      )
   __A    =  re.compile(  # remove characters not in vocabulary
       RF"[^\s{re.escape(snake_case      )}]"							,			flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,			)
   __A    =  []

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

   __A    =  train_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )
   __A    =  val_dataset.map(snake_case							,			remove_columns=[data_args.speech_file_column]      )

   if data_args.max_duration_in_seconds is not None:

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

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

   logger.warning(F"Updated {len(snake_case      )} 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}\""      )
   __A    =  None

   def prepare_dataset(snake_case      ):
      # 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[data_args.target_text_column]							,			sampling_rate=batch['''sampling_rate'''][0]      )
      batch.update(snake_case      )
      return batch

   __A    =  train_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)
   __A    =  val_dataset.map(
       snake_case							,			batch_size=training_args.per_device_train_batch_size							,			batched=snake_case							,			num_proc=data_args.preprocessing_num_workers							,			)

   __A    =  DataCollatorCTCWithPadding(processor=snake_case							,			padding=snake_case      )

   def compute_metrics(snake_case      ):
      __A    =  pred.predictions
      __A    =  np.argmax(snake_case							,			axis=-1      )

      __A    =  processor.tokenizer.pad_token_id

      __A    =  processor.batch_decode(snake_case      )
      # we do not want to group tokens when computing the metrics
      __A    =  processor.batch_decode(pred.label_ids							,			group_tokens=snake_case      )
      if logger.isEnabledFor(logging.DEBUG      ):
         for reference, predicted in zip(snake_case							,			snake_case      ):
            logger.debug(F"reference: \"{reference}\""      )
            logger.debug(F"predicted: \"{predicted}\""      )
            if orthography.untransliterator is not None:
               logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )
               logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(snake_case      )}\""      )

      __A    =  wer_metric.compute(predictions=snake_case							,			references=snake_case      )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   __A    =  CTCTrainer(
       model=snake_case							,			data_collator=snake_case							,			args=snake_case							,			compute_metrics=snake_case							,			train_dataset=snake_case							,			eval_dataset=snake_case							,			tokenizer=processor.feature_extractor							,			)

   trainer.train()


if __name__ == "__main__":
    main()

