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

def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[Any]:

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







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




    @classmethod
    def     __lowerCamelCase			(       cls	: Optional[Any]   ,   lowerCAmelCase	: str )						->			int:
      if name == "librispeech":
        return cls()
      if name == "timit":
        return cls(
            do_lower_case=lowerCAmelCase   ,   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(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     __lowerCamelCase			(       self	: Any   ,   lowerCAmelCase	: 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	: int   ,   lowerCAmelCase	: 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(lowerCAmelCase   ,   lowerCAmelCase )







@dataclass
class        UpperCAmelCase_					:
    UpperCAmelCase_      :							WavaVecaProcessor
    UpperCAmelCase_      :							Union[bool, str]    			=				True
    UpperCAmelCase_      :							Optional[int]    			=				None
    UpperCAmelCase_      :							Optional[int]    			=				None
    UpperCAmelCase_      :							Optional[int]    			=				None
    UpperCAmelCase_      :							Optional[int]    			=				None




    def __call__(       self	: Optional[int]   ,   lowerCAmelCase	: 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(
          lowerCAmelCase   ,   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=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
      a__  		=  labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1 )   ,   -100 )

      a__  		=  labels

      return batch







class        UpperCAmelCase_					(				_a     ):




    def     __lowerCamelCase			(       self	: Tuple   ,   lowerCAmelCase	: nn.Module   ,   lowerCAmelCase	: Dict[str, Union[torch.Tensor, Any]] )						->			torch.Tensor:
      model.train()
      a__  		=  self._prepare_inputs(lowerCAmelCase )

      if self.use_amp:
        with autocast():
          a__  		=  self.compute_loss(lowerCAmelCase   ,   lowerCAmelCase )
      else:
        a__  		=  self.compute_loss(lowerCAmelCase   ,   lowerCAmelCase )

      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(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 					__lowercase						(    )					->Optional[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__       ):  # 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__       ):
      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__       ):
    # 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__       ):
    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 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  if not isinstance(lowerCamelCase__			,					lowerCamelCase__       ):
    raise ValueError("double_factorial() only accepts integral values"       )
  if n < 0:
    raise ValueError("double_factorial() not defined for negative values"       )
  return 1 if n <= 1 else n * double_factorial(n - 2       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

