


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


_A									=		logging.getLogger(__name__)






@dataclass
class        UpperCAmelCase__			:



    """simple docstring"""






    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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict     ,	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							)]     ,	)
     __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(lowerCAmelCase_							)






@dataclass
class        UpperCAmelCase__			:



    """simple docstring"""






    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__			:



    """simple docstring"""






    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 _a   (     cls   ,  A_       )    ->			Dict:
         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 _a   (     self   ,  A_       )    ->			List[str]:
         # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
         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 _a   (     self   ,  A_       )    ->			Tuple:
         __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        UpperCAmelCase__			:



    """simple docstring"""






    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   ,  A_       )    ->			Optional[Any]:
         # split inputs and labels since they have to be of different lenghts and need
         # different padding methods
         __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        UpperCAmelCase__			(   _a						):



    """simple docstring"""






    def _a   (     self   ,  A_   ,  A_       )    ->			int:
         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							(     ):
     __UpperCamelCase       				=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)

     __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase       				=parser.parse_args_into_dataclasses()
     configure_logger(lowerCAmelCase_     ,	lowerCAmelCase_							)

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

     def prepare_example(SCREAMING_SNAKE_CASE__     :   List[str]							):  # TODO(elgeish) make use of multiprocessing?
          __UpperCamelCase				, __UpperCamelCase       				=librosa.load(example[data_args.speech_file_column]     ,	sr=lowerCAmelCase_							)
          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(''     ,	lowerCAmelCase_							)
          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(lowerCAmelCase_     ,	remove_columns=[data_args.speech_file_column]							)
     __UpperCamelCase       				=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(SCREAMING_SNAKE_CASE__     :   Dict							):
               return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

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

     def prepare_dataset(SCREAMING_SNAKE_CASE__     :   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}.'

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

     __UpperCamelCase       				=train_dataset.map(
         lowerCAmelCase_     ,	batch_size=training_args.per_device_train_batch_size     ,	batched=lowerCAmelCase_     ,	num_proc=data_args.preprocessing_num_workers     ,	)
     __UpperCamelCase       				=val_dataset.map(
         lowerCAmelCase_     ,	batch_size=training_args.per_device_train_batch_size     ,	batched=lowerCAmelCase_     ,	num_proc=data_args.preprocessing_num_workers     ,	)

     __UpperCamelCase       				=DataCollatorCTCWithPadding(processor=lowerCAmelCase_     ,	padding=lowerCAmelCase_							)

     def compute_metrics(SCREAMING_SNAKE_CASE__     :   Any							):
          __UpperCamelCase       				=pred.predictions
          __UpperCamelCase       				=np.argmax(lowerCAmelCase_     ,	axis=-1							)

          __UpperCamelCase       				=processor.tokenizer.pad_token_id

          __UpperCamelCase       				=processor.batch_decode(lowerCAmelCase_							)
          # we do not want to group tokens when computing the metrics
          __UpperCamelCase       				=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_							)}\"'							)

          __UpperCamelCase       				=wer_metric.compute(predictions=lowerCAmelCase_     ,	references=lowerCAmelCase_							)

          return {"wer": wer}

     if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()

     __UpperCamelCase       				=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()






import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')