

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__	:   str								=     True
       from torch.cuda.amp import autocast


UpperCamelCase__	:   Tuple								=     logging.getLogger(__name__)



@dataclass
class    __snake_case	:
  __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							(	_SCREAMING_SNAKE_CASE       :		ModelArguments			, _SCREAMING_SNAKE_CASE       :		TrainingArguments							):





    """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							)]			, )
    SCREAMING_SNAKE_CASE_						=    logging.WARNING
    if model_args.verbose_logging:
        SCREAMING_SNAKE_CASE_						=    logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank							):
        SCREAMING_SNAKE_CASE_						=    logging.INFO
    logger.setLevel(_SCREAMING_SNAKE_CASE							)



@dataclass
class    __snake_case	:
  __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    __snake_case	:
  __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     lowerCAmelCase__  (       cls    ,		_A):
      if name == "librispeech":
          return cls()
      if name == "timit":
          return cls(
              do_lower_case=_A    ,		translation_table=str.maketrans({'-': ' '})    ,		)
      if name == "buckwalter":
          SCREAMING_SNAKE_CASE_						=    {
              '-': ' ',  # sometimes used to represent pauses
              '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
          }
          return cls(
              vocab_file=pathlib.Path(_A).parent.joinpath('vocab/buckwalter.json')    ,		word_delimiter_token='/'    ,		translation_table=str.maketrans(_A)    ,		words_to_remove={'sil'}    ,		untransliterator=arabic.buckwalter.untransliterate    ,		)
      raise ValueError(f"""Unsupported orthography: '{name}'.""")

  def     lowerCAmelCase__  (       self    ,		_A):
      # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
      if len(self.translation_table) > 0:
          SCREAMING_SNAKE_CASE_						=    text.translate(self.translation_table)
      if len(self.words_to_remove) == 0:
          SCREAMING_SNAKE_CASE_						=    ' '.join(text.split())  # clean up whitespaces
      else:
          SCREAMING_SNAKE_CASE_						=    ' '.join(w for w in text.split() if w not in self.words_to_remove)  # and clean up whilespaces
      return text


  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    WavaVecaFeatureExtractor.from_pretrained(
          model_args.model_name_or_path    ,		cache_dir=model_args.cache_dir)
      if self.vocab_file:
          SCREAMING_SNAKE_CASE_						=    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:
          SCREAMING_SNAKE_CASE_						=    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(_A    ,		_A)






@dataclass
class    __snake_case	:
  __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    ,		_A):
      # split inputs and labels since they have to be of different lenghts and need
      # different padding methods
      SCREAMING_SNAKE_CASE_						=    [{'input_values': feature['input_values']} for feature in features]
      SCREAMING_SNAKE_CASE_						=    [{'input_ids': feature['labels']} for feature in features]

      SCREAMING_SNAKE_CASE_						=    self.processor.pad(
          _A    ,		padding=self.padding    ,		max_length=self.max_length    ,		pad_to_multiple_of=self.pad_to_multiple_of    ,		return_tensors='pt'    ,		)
      SCREAMING_SNAKE_CASE_						=    self.processor.pad(
          labels=_A    ,		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
      SCREAMING_SNAKE_CASE_						=    labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1)    ,		-100)

      SCREAMING_SNAKE_CASE_						=    labels

      return batch






class    __snake_case	(     lowerCAmelCase__							):

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A):
      model.train()
      SCREAMING_SNAKE_CASE_						=    self._prepare_inputs(_A)

      if self.use_amp:
          with autocast():
              SCREAMING_SNAKE_CASE_						=    self.compute_loss(_A    ,		_A)
      else:
          SCREAMING_SNAKE_CASE_						=    self.compute_loss(_A    ,		_A)

      if self.args.n_gpu > 1:
          if model.module.config.ctc_loss_reduction == "mean":
              SCREAMING_SNAKE_CASE_						=    loss.mean()
          elif model.module.config.ctc_loss_reduction == "sum":
              SCREAMING_SNAKE_CASE_						=    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:
          SCREAMING_SNAKE_CASE_						=    loss / self.args.gradient_accumulation_steps

      if self.use_amp:
          self.scaler.scale(_A).backward()
      elif self.use_apex:
          with amp.scale_loss(_A    ,		self.optimizer) as scaled_loss:
              scaled_loss.backward()
      elif self.deepspeed:
          self.deepspeed.backward(_A)
      else:
          loss.backward()

      return loss.detach()






def        _UpperCAmelCase							(	):





    """simple docstring"""


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

    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    parser.parse_args_into_dataclasses()
    configure_logger(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    Orthography.from_name(data_args.orthography.lower()							)
    SCREAMING_SNAKE_CASE_						=    orthography.create_processor(_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    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							)			, )

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

    SCREAMING_SNAKE_CASE_						=    datasets.load_metric('wer'							)
    SCREAMING_SNAKE_CASE_						=    processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
    SCREAMING_SNAKE_CASE_						=    ''.join(t for t in processor.tokenizer.get_vocab().keys() if len(_SCREAMING_SNAKE_CASE							) == 1							)
    SCREAMING_SNAKE_CASE_						=    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			, )
    SCREAMING_SNAKE_CASE_						=    []

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

    SCREAMING_SNAKE_CASE_						=    train_dataset.map(_SCREAMING_SNAKE_CASE			, remove_columns=[data_args.speech_file_column]							)
    SCREAMING_SNAKE_CASE_						=    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       :		Tuple							):
            return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

        SCREAMING_SNAKE_CASE_						=    len(_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    len(_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    train_dataset.filter(_SCREAMING_SNAKE_CASE			, remove_columns=['duration_in_seconds']							)
        SCREAMING_SNAKE_CASE_						=    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}\""""							)
    SCREAMING_SNAKE_CASE_						=    None

    def prepare_dataset(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        # 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}."""

        SCREAMING_SNAKE_CASE_						=    processor(
            audio=batch['speech']			, text=batch[data_args.target_text_column]			, sampling_rate=batch['sampling_rate'][0]							)
        batch.update(_SCREAMING_SNAKE_CASE							)
        return batch

    SCREAMING_SNAKE_CASE_						=    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			, )
    SCREAMING_SNAKE_CASE_						=    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			, )

    SCREAMING_SNAKE_CASE_						=    DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE			, padding=_SCREAMING_SNAKE_CASE							)

    def compute_metrics(_SCREAMING_SNAKE_CASE       :		Optional[int]							):
        SCREAMING_SNAKE_CASE_						=    pred.predictions
        SCREAMING_SNAKE_CASE_						=    np.argmax(_SCREAMING_SNAKE_CASE			, axis=-1							)

        SCREAMING_SNAKE_CASE_						=    processor.tokenizer.pad_token_id

        SCREAMING_SNAKE_CASE_						=    processor.batch_decode(_SCREAMING_SNAKE_CASE							)
        # we do not want to group tokens when computing the metrics
        SCREAMING_SNAKE_CASE_						=    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							)}\""""							)

        SCREAMING_SNAKE_CASE_						=    wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE			, references=_SCREAMING_SNAKE_CASE							)

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    SCREAMING_SNAKE_CASE_						=    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 typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						Tuple    			=	['pixel_values']

  def __init__(       self    ,		_A = True    ,		_A = None    ,		_A = PILImageResampling.BILINEAR    ,		_A = True    ,		_A = 1 / 255    ,		_A = True    ,		_A = None    ,		_A = None    ,		**_A    ,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_						=    size if size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      SCREAMING_SNAKE_CASE_						=    do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize
      SCREAMING_SNAKE_CASE_						=    size
      SCREAMING_SNAKE_CASE_						=    resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else IMAGENET_STANDARD_STD

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = PILImageResampling.BILINEAR    ,		_A = None    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)
      if "height" not in size or "width" not in size:
          raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
      return resize(
          _A    ,		size=(size['height'], size['width'])    ,		resample=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A = None    ,		**_A):
      return rescale(_A    ,		scale=_A    ,		data_format=_A    ,		**_A)

  def     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A    ,		_A = None    ,		**_A    ,		):
      return normalize(_A    ,		mean=_A    ,		std=_A    ,		data_format=_A    ,		**_A)


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = None    ,		_A = ChannelDimension.FIRST    ,		**_A    ,		):
      SCREAMING_SNAKE_CASE_						=    do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_						=    do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_						=    do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_						=    resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_						=    rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_						=    image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_						=    image_std if image_std is not None else self.image_std

      SCREAMING_SNAKE_CASE_						=    size if size is not None else self.size
      SCREAMING_SNAKE_CASE_						=    get_size_dict(_A)

      SCREAMING_SNAKE_CASE_						=    make_list_of_images(_A)

      if not valid_images(_A):
          raise ValueError(
              'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
              'torch.Tensor, tf.Tensor or jax.ndarray.')

      if do_resize and size is None:
          raise ValueError('Size must be specified if do_resize is True.')

      if do_rescale and rescale_factor is None:
          raise ValueError('Rescale factor must be specified if do_rescale is True.')

      # All transformations expect numpy arrays.
      SCREAMING_SNAKE_CASE_						=    [to_numpy_array(_A) for image in images]

      if do_resize:
          SCREAMING_SNAKE_CASE_						=    [self.resize(image=_A    ,		size=_A    ,		resample=_A) for image in images]

      if do_rescale:
          SCREAMING_SNAKE_CASE_						=    [self.rescale(image=_A    ,		scale=_A) for image in images]

      if do_normalize:
          SCREAMING_SNAKE_CASE_						=    [self.normalize(image=_A    ,		mean=_A    ,		std=_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    [to_channel_dimension_format(_A    ,		_A) for image in images]

      SCREAMING_SNAKE_CASE_						=    {'pixel_values': images}
      return BatchFeature(data=_A    ,		tensor_type=_A)


