





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'):
       snake_case__   :				Dict						=	True
       from torch.cuda.amp import autocast


snake_case__   :				List[str]						=	logging.getLogger(__name__)





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





def _a							(      lowerCamelCase:  ModelArguments   ,							lowerCamelCase:  TrainingArguments					)      ->		str:







      '''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(lowerCamelCase					)





@dataclass
class A_   :
   lowerCAmelCase__  	=					field(
       default=_lowerCamelCase					, metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
   lowerCAmelCase__  	=					field(
       default=_lowerCamelCase					, 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=_lowerCamelCase					, metadata={"""help""": """Resample loaded audio to target feature extractor's sampling rate or not."""}					, )
   lowerCAmelCase__  	=					field(
       default=_lowerCamelCase					, 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=_lowerCamelCase					, metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}  )
   lowerCAmelCase__  	=					field(
       default=_lowerCamelCase					, metadata={"""help""": """The number of processes to use for the preprocessing."""}					, )





@dataclass
class A_   :
   lowerCAmelCase__  	=					False
   lowerCAmelCase__  	=					None
   lowerCAmelCase__  	=					"|"
   lowerCAmelCase__  	=					field(default_factory=_lowerCamelCase  )
   lowerCAmelCase__  	=					field(default_factory=_lowerCamelCase  )
   lowerCAmelCase__  	=					None




   @classmethod
   def _lowerCAmelCase (cls      :Any       ,  _UpperCamelCase      :str					)->   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 _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :str					)->   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 _lowerCAmelCase (self      :List[Any]       ,  _UpperCamelCase      :ModelArguments					)->   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 A_   :
   lowerCAmelCase__  	=					42
   lowerCAmelCase__  	=					True
   lowerCAmelCase__  	=					None
   lowerCAmelCase__  	=					None
   lowerCAmelCase__  	=					None
   lowerCAmelCase__  	=					None




   def __call__(self      :List[str]       ,  _UpperCamelCase      :List[Dict[str, Union[List[int], torch.Tensor]]]					)->   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					)       ,  -100					)

         __A			  =							labels

         return batch





class A_   (     _lowerCamelCase  ):




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :nn.Module       ,  _UpperCamelCase      :Dict[str, Union[torch.Tensor, Any]]					)->   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 _a							(      )      ->		Union[str, Any]:







      '''simple docstring'''






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

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

      __A			  =							Orthography.from_name(data_args.orthography.lower()					)
      __A			  =							orthography.create_processor(lowerCamelCase					)
      __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(lowerCamelCase					) == 1					)
      __A			  =							re.compile(  # remove characters not in vocabulary
          rF"""[^\s{re.escape(lowerCamelCase					)}]"""   ,							flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0   ,							)
      __A			  =							[]

      def prepare_example(lowerCamelCase:  int					):  # TODO(elgeish) make use of multiprocessing?
            __A  ,		__A			  =							librosa.load(example[data_args.speech_file_column]   ,							sr=lowerCamelCase					)
            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(''''''   ,							lowerCamelCase					)
            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(lowerCamelCase   ,							remove_columns=[data_args.speech_file_column]					)
      __A			  =							val_dataset.map(lowerCamelCase   ,							remove_columns=[data_args.speech_file_column]					)

      if data_args.max_duration_in_seconds is not None:

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

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

      logger.warning(F"""Updated {len(lowerCamelCase					)} 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(lowerCamelCase:  List[str]					):
            # 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(lowerCamelCase					)
            return batch

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

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

      def compute_metrics(lowerCamelCase:  Union[str, Any]					):
            __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					)
            if logger.isEnabledFor(logging.DEBUG					):
                  for reference, predicted in zip(lowerCamelCase   ,							lowerCamelCase					):
                        logger.debug(F"""reference: \"{reference}\""""					)
                        logger.debug(F"""predicted: \"{predicted}\""""					)
                        if orthography.untransliterator is not None:
                              logger.debug(F"""reference (untransliterated): \"{orthography.untransliterator(lowerCamelCase					)}\""""					)
                              logger.debug(F"""predicted (untransliterated): \"{orthography.untransliterator(lowerCamelCase					)}\""""					)

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

            return {"wer": wer}

      if model_args.freeze_feature_extractor:
            model.freeze_feature_extractor()

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

      trainer.train()


if __name__ == "__main__":
       main()












def _a							(      lowerCamelCase:  List[Any]					)      ->		Optional[Any]:







      '''simple docstring'''






      __A			  =							[7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowerCamelCase   ,							len(lowerCamelCase					)					):
                  __A			  =							collection[i]
                  __A			  =							i
                  while j >= gap and collection[j - gap] > insert_value:
                        __A			  =							collection[j - gap]
                        j -= gap
                  if j != i:
                        __A			  =							insert_value
      return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       snake_case__   :				Optional[int]						=	input('Enter numbers separated by a comma:\n').strip()
       snake_case__   :				int						=	[int(item) for item in user_input.split(',')]
       print(shell_sort(unsorted))





