
"""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'):
  SCREAMING_SNAKE_CASE_  					= True
  from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE_  					= logging.getLogger(__name__)






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



def       __UpperCAmelCase		(		a_      :		ModelArguments     ,	a_      :		TrainingArguments					)  ->							Tuple:
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,	datefmt="%m/%d/%Y %H:%M:%S"     ,	handlers=[logging.StreamHandler(sys.stdout					)]     ,	)
   _UpperCAmelCase 						=       logging.WARNING
   if model_args.verbose_logging:
      _UpperCAmelCase 						=       logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank					):
      _UpperCAmelCase 						=       logging.INFO
   logger.setLevel(a_					)






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






@dataclass
class      __a      :
       _lowerCamelCase				:				bool   			=      False
       _lowerCamelCase				:				Optional[str]   			=      None
       _lowerCamelCase				:				Optional[str]   			=      "|"
       _lowerCamelCase				:				Optional[Dict[str, str]]   			=      field(default_factory=lowerCAmelCase_      )
       _lowerCamelCase				:				Optional[Set[str]]   			=      field(default_factory=lowerCAmelCase_      )
       _lowerCamelCase				:				Optional[Callable[[str], str]]   			=      None







       @classmethod
       def  _snake_case      (       cls				,lowerCamelCase_		):



          '''simple docstring'''

          if name == "librispeech":
             return cls()
          if name == "timit":
             return cls(
                 do_lower_case=lowerCamelCase_				,translation_table=str.maketrans({"-": " "}		)				,)
          if name == "buckwalter":
             _UpperCAmelCase 						=       {
                 "-": " ",  # 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  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          if len(self.translation_table		) > 0:
             _UpperCAmelCase 						=       text.translate(self.translation_table		)
          if len(self.words_to_remove		) == 0:
             _UpperCAmelCase 						=       " ".join(text.split()		)  # clean up whitespaces
          else:
             _UpperCAmelCase 						=       " ".join(w for w in text.split() if w not in self.words_to_remove		)  # and clean up whilespaces
          return text







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       WavaVecaFeatureExtractor.from_pretrained(
              model_args.model_name_or_path				,cache_dir=model_args.cache_dir		)
          if self.vocab_file:
             _UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       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      __a      :
       _lowerCamelCase				:				WavaVecaProcessor
       _lowerCamelCase				:				Union[bool, str]   			=      True
       _lowerCamelCase				:				Optional[int]   			=      None
       _lowerCamelCase				:				Optional[int]   			=      None
       _lowerCamelCase				:				Optional[int]   			=      None
       _lowerCamelCase				:				Optional[int]   			=      None







       def __call__(       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       [{"input_values": feature["input_values"]} for feature in features]
          _UpperCAmelCase 						=       [{"input_ids": feature["labels"]} for feature in features]

          _UpperCAmelCase 						=       self.processor.pad(
              lowerCamelCase_				,padding=self.padding				,max_length=self.max_length				,pad_to_multiple_of=self.pad_to_multiple_of				,return_tensors="pt"				,)
          _UpperCAmelCase 						=       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
          _UpperCAmelCase 						=       labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1		)				,-100		)

          _UpperCAmelCase 						=       labels

          return batch






class      __a      (       lowerCAmelCase_      ):







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          model.train()
          _UpperCAmelCase 						=       self._prepare_inputs(lowerCamelCase_		)

          if self.use_amp:
             with autocast():
                _UpperCAmelCase 						=       self.compute_loss(lowerCamelCase_				,lowerCamelCase_		)
          else:
             _UpperCAmelCase 						=       self.compute_loss(lowerCamelCase_				,lowerCamelCase_		)

          if self.args.n_gpu > 1:
             if model.module.config.ctc_loss_reduction == "mean":
                _UpperCAmelCase 						=       loss.mean()
             elif model.module.config.ctc_loss_reduction == "sum":
                _UpperCAmelCase 						=       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:
             _UpperCAmelCase 						=       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       __UpperCAmelCase		(		)  ->							Dict:
   # 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.

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

   _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       parser.parse_args_into_dataclasses()
   configure_logger(a_     ,	a_					)

   _UpperCAmelCase 						=       Orthography.from_name(data_args.orthography.lower()					)
   _UpperCAmelCase 						=       orthography.create_processor(a_					)
   _UpperCAmelCase 						=       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					)     ,	)

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

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

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

   _UpperCAmelCase 						=       train_dataset.map(a_     ,	remove_columns=[data_args.speech_file_column]					)
   _UpperCAmelCase 						=       val_dataset.map(a_     ,	remove_columns=[data_args.speech_file_column]					)

   if data_args.max_duration_in_seconds is not None:

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

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

   logger.warning(F'''Updated {len(a_					)} 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}"'''					)
   _UpperCAmelCase 						=       None

   def prepare_dataset(a_      :		Optional[int]					):
      # 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}.'''

      _UpperCAmelCase 						=       processor(
          audio=batch["speech"]     ,	text=batch[data_args.target_text_column]     ,	sampling_rate=batch["sampling_rate"][0]					)
      batch.update(a_					)
      return batch

   _UpperCAmelCase 						=       train_dataset.map(
       a_     ,	batch_size=training_args.per_device_train_batch_size     ,	batched=a_     ,	num_proc=data_args.preprocessing_num_workers     ,	)
   _UpperCAmelCase 						=       val_dataset.map(
       a_     ,	batch_size=training_args.per_device_train_batch_size     ,	batched=a_     ,	num_proc=data_args.preprocessing_num_workers     ,	)

   _UpperCAmelCase 						=       DataCollatorCTCWithPadding(processor=a_     ,	padding=a_					)

   def compute_metrics(a_      :		int					):
      _UpperCAmelCase 						=       pred.predictions
      _UpperCAmelCase 						=       np.argmax(a_     ,	axis=-1					)

      _UpperCAmelCase 						=       processor.tokenizer.pad_token_id

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

      _UpperCAmelCase 						=       wer_metric.compute(predictions=a_     ,	references=a_					)

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   _UpperCAmelCase 						=       CTCTrainer(
       model=a_     ,	data_collator=a_     ,	args=a_     ,	compute_metrics=a_     ,	train_dataset=a_     ,	eval_dataset=a_     ,	tokenizer=processor.feature_extractor     ,	)

   trainer.train()


if __name__ == "__main__":
  main()



"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[list[int]]					)  ->							bool:
   # must be
   _UpperCAmelCase 						=       [list(a_					) for row in matrix]
   if matrix and isinstance(a_     ,	a_					):
      if isinstance(matrix[0]     ,	a_					):
         _UpperCAmelCase 						=       0
         for row in matrix:
            if prev_len == 0:
               _UpperCAmelCase 						=       len(a_					)
               _UpperCAmelCase 						=       True
            else:
               _UpperCAmelCase 						=       prev_len == len(a_					)
      else:
         _UpperCAmelCase 						=       True
   else:
      _UpperCAmelCase 						=       False

   return result
def       __UpperCAmelCase		(		a_      :		list[list[int]]					)  ->							None:
   if check_matrix(a_					) and len(a_					) > 0:
      _UpperCAmelCase 						=       [list(a_					) for row in a]
      _UpperCAmelCase 						=       len(a_					)
      if isinstance(a[0]     ,	a_					):
         _UpperCAmelCase 						=       len(a[0]					)
      else:
         for dat in a:
            print(a_					)
         return

      # horizotal printing increasing
      for i in range(0     ,	a_					):
         print(a[0][i]					)
      # vertical printing down
      for i in range(1     ,	a_					):
         print(a[i][mat_col - 1]					)
      # horizotal printing decreasing
      if mat_row > 1:
         for i in range(mat_col - 2     ,	-1     ,	-1					):
            print(a[mat_row - 1][i]					)
        # vertical printing up
      for i in range(mat_row - 2     ,	0     ,	-1					):
         print(a[i][0]					)
      _UpperCAmelCase 						=       [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
      if len(a_					) > 0:
         spiral_print_clockwise(a_					)
      else:
         return
   else:
      print("Not a valid matrix"					)
      return
def       __UpperCAmelCase		(		a_      :		list[list]					)  ->							list[int]:
   if matrix:
      return list(matrix.pop(0					)					) + spiral_traversal(list(zip(*a_					)					)[::-1]					)
   else:
      return []


# driver code
if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE_  					= [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
  spiral_print_clockwise(a)

