




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__ :   Union[str, Any]						=     True
       from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE__ :   Any						=     logging.getLogger(__name__)




@dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



    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   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Dict:
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,datefmt="%m/%d/%Y %H:%M:%S"				,handlers=[logging.StreamHandler(sys.stdout					)]				,)
       lowerCamelCase     :   str    				=      logging.WARNING
       if model_args.verbose_logging:
              lowerCamelCase     :   int    				=      logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank					):
              lowerCamelCase     :   Union[str, Any]    				=      logging.INFO
       logger.setLevel(_SCREAMING_SNAKE_CASE					)




@dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



    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 	UpperCamelCase__			:

    '''simple docstring'''



    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 					_lowercase  (					cls  ,						UpperCamelCase__  )  ->   str:
           if name == "librispeech":
                  return cls()
           if name == "timit":
                  return cls(
                      do_lower_case=UpperCamelCase__  ,						translation_table=str.maketrans({"-": " "}  )  ,						)
           if name == "buckwalter":
                  lowerCamelCase     :   List[str]    				=      {
                      "-": " ",  # 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 					_lowercase  (					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:
                  lowerCamelCase     :   Any    				=      text.translate(self.translation_table  )
           if len(self.words_to_remove  ) == 0:
                  lowerCamelCase     :   List[Any]    				=      " ".join(text.split()  )  # clean up whitespaces
           else:
                  lowerCamelCase     :   List[Any]    				=      " ".join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
           return text







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   WavaVecaProcessor:
           lowerCamelCase     :   Union[str, Any]    				=      WavaVecaFeatureExtractor.from_pretrained(
               model_args.model_name_or_path  ,						cache_dir=model_args.cache_dir  )
           if self.vocab_file:
                  lowerCamelCase     :   str    				=      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:
                  lowerCamelCase     :   List[str]    				=      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 	UpperCamelCase__			:

    '''simple docstring'''



    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  ,						UpperCamelCase__  )  ->   Dict[str, torch.Tensor]:
           # split inputs and labels since they have to be of different lenghts and need
           # different padding methods
           lowerCamelCase     :   Dict    				=      [{"input_values": feature["input_values"]} for feature in features]
           lowerCamelCase     :   Tuple    				=      [{"input_ids": feature["labels"]} for feature in features]

           lowerCamelCase     :   str    				=      self.processor.pad(
               UpperCamelCase__  ,						padding=self.padding  ,						max_length=self.max_length  ,						pad_to_multiple_of=self.pad_to_multiple_of  ,						return_tensors="pt"  ,						)
           lowerCamelCase     :   Tuple    				=      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
           lowerCamelCase     :   int    				=      labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1  )  ,						-100  )

           lowerCamelCase     :   str    				=      labels

           return batch




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   torch.Tensor:
           model.train()
           lowerCamelCase     :   Tuple    				=      self._prepare_inputs(UpperCamelCase__  )

           if self.use_amp:
                  with autocast():
                         lowerCamelCase     :   Optional[int]    				=      self.compute_loss(UpperCamelCase__  ,						UpperCamelCase__  )
           else:
                  lowerCamelCase     :   int    				=      self.compute_loss(UpperCamelCase__  ,						UpperCamelCase__  )

           if self.args.n_gpu > 1:
                  if model.module.config.ctc_loss_reduction == "mean":
                         lowerCamelCase     :   List[str]    				=      loss.mean()
                  elif model.module.config.ctc_loss_reduction == "sum":
                         lowerCamelCase     :   List[str]    				=      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:
                  lowerCamelCase     :   Tuple    				=      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   (   )			->				List[Any]:
       # 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.

       lowerCamelCase     :   str    				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

       lowerCamelCase     :   Union[str, Any]    				=      parser.parse_args_into_dataclasses()
       configure_logger(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)

       lowerCamelCase     :   Optional[int]    				=      Orthography.from_name(data_args.orthography.lower()					)
       lowerCamelCase     :   int    				=      orthography.create_processor(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Optional[int]    				=      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					)				,)

       lowerCamelCase     :   Tuple    				=      datasets.load_dataset(
           data_args.dataset_name				,data_args.dataset_config_name				,split=data_args.train_split_name					)
       lowerCamelCase     :   List[Any]    				=      datasets.load_dataset(
           data_args.dataset_name				,data_args.dataset_config_name				,split=data_args.validation_split_name					)

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

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

       lowerCamelCase     :   Optional[int]    				=      train_dataset.map(_SCREAMING_SNAKE_CASE				,remove_columns=[data_args.speech_file_column]					)
       lowerCamelCase     :   Tuple    				=      val_dataset.map(_SCREAMING_SNAKE_CASE				,remove_columns=[data_args.speech_file_column]					)

       if data_args.max_duration_in_seconds is not None:

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

              lowerCamelCase     :   Optional[int]    				=      len(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Dict    				=      len(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Dict    				=      train_dataset.filter(_SCREAMING_SNAKE_CASE				,remove_columns=["duration_in_seconds"]					)
              lowerCamelCase     :   Dict    				=      val_dataset.filter(_SCREAMING_SNAKE_CASE				,remove_columns=["duration_in_seconds"]					)
              if len(_SCREAMING_SNAKE_CASE					) > old_train_size:
                     logger.warning(
                         f'''Filtered out {len(_SCREAMING_SNAKE_CASE					) - old_train_size} train example(s) longer than'''
                         f''' {data_args.max_duration_in_seconds} second(s).'''					)
              if len(_SCREAMING_SNAKE_CASE					) > old_val_size:
                     logger.warning(
                         f'''Filtered out {len(_SCREAMING_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(_SCREAMING_SNAKE_CASE					)} train and {len(_SCREAMING_SNAKE_CASE					)} validation.'''					)

       logger.warning(f'''Updated {len(_SCREAMING_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}"'''					)
       lowerCamelCase     :   Optional[int]    				=      None

       def prepare_dataset(_SCREAMING_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}.'''

              lowerCamelCase     :   Optional[int]    				=      processor(
                  audio=batch["speech"]				,text=batch[data_args.target_text_column]				,sampling_rate=batch["sampling_rate"][0]					)
              batch.update(_SCREAMING_SNAKE_CASE					)
              return batch

       lowerCamelCase     :   Optional[Any]    				=      train_dataset.map(
           _SCREAMING_SNAKE_CASE				,batch_size=training_args.per_device_train_batch_size				,batched=_SCREAMING_SNAKE_CASE				,num_proc=data_args.preprocessing_num_workers				,)
       lowerCamelCase     :   Optional[Any]    				=      val_dataset.map(
           _SCREAMING_SNAKE_CASE				,batch_size=training_args.per_device_train_batch_size				,batched=_SCREAMING_SNAKE_CASE				,num_proc=data_args.preprocessing_num_workers				,)

       lowerCamelCase     :   Optional[Any]    				=      DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE				,padding=_SCREAMING_SNAKE_CASE					)

       def compute_metrics(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   Dict    				=      pred.predictions
              lowerCamelCase     :   int    				=      np.argmax(_SCREAMING_SNAKE_CASE				,axis=-1					)

              lowerCamelCase     :   Any    				=      processor.tokenizer.pad_token_id

              lowerCamelCase     :   Tuple    				=      processor.batch_decode(_SCREAMING_SNAKE_CASE					)
              # we do not want to group tokens when computing the metrics
              lowerCamelCase     :   Optional[int]    				=      processor.batch_decode(pred.label_ids				,group_tokens=_SCREAMING_SNAKE_CASE					)
              if logger.isEnabledFor(logging.DEBUG					):
                     for reference, predicted in zip(_SCREAMING_SNAKE_CASE				,_SCREAMING_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(_SCREAMING_SNAKE_CASE					)}"'''					)
                                   logger.debug(f'''predicted (untransliterated): "{orthography.untransliterator(_SCREAMING_SNAKE_CASE					)}"'''					)

              lowerCamelCase     :   Any    				=      wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE				,references=_SCREAMING_SNAKE_CASE					)

              return {"wer": wer}

       if model_args.freeze_feature_extractor:
              model.freeze_feature_extractor()

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

       trainer.train()


if __name__ == "__main__":
       main()








from __future__ import annotations

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				list:
       lowerCamelCase     :   int    				=      [None] * (len(_SCREAMING_SNAKE_CASE					) + len(_SCREAMING_SNAKE_CASE					))

       lowerCamelCase     :   Any    				=      0  # pointer to current index for left Half
       lowerCamelCase     :   int    				=      0  # pointer to current index for the right Half
       lowerCamelCase     :   Optional[int]    				=      0  # pointer to current index for the sorted array Half

       while pointera < len(_SCREAMING_SNAKE_CASE					) and pointera < len(_SCREAMING_SNAKE_CASE					):
              if left_half[pointera] < right_half[pointera]:
                     lowerCamelCase     :   Any    				=      left_half[pointera]
                     pointera += 1
                     index += 1
              else:
                     lowerCamelCase     :   Any    				=      right_half[pointera]
                     pointera += 1
                     index += 1
       while pointera < len(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   Optional[Any]    				=      left_half[pointera]
              pointera += 1
              index += 1

       while pointera < len(_SCREAMING_SNAKE_CASE					):
              lowerCamelCase     :   str    				=      right_half[pointera]
              pointera += 1
              index += 1

       return sorted_array

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list:
       if len(_SCREAMING_SNAKE_CASE					) <= 1:
              return array
       # the actual formula to calculate the middle element = left + (right - left) // 2
       # this avoids integer overflow in case of large N
       lowerCamelCase     :   int    				=      0 + (len(_SCREAMING_SNAKE_CASE					) - 0) // 2

       # Split the array into halves till the array length becomes equal to One
       # merge the arrays of single length returned by mergeSort function and
       # pass them into the merge arrays function which merges the array
       lowerCamelCase     :   Optional[Any]    				=      array[:middle]
       lowerCamelCase     :   Optional[Any]    				=      array[middle:]

       return merge(merge_sort(_SCREAMING_SNAKE_CASE					)				,merge_sort(_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






