
'''simple docstring'''





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   :		List[Any]					 =       True
						from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.getLogger(__name__)






@dataclass
class 					__lowercase		:
					__magic_name__						:							Dict     	=	field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
					__magic_name__						:							Tuple     	=	field(
					    default=A__							,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,       )
					__magic_name__						:							List[str]     	=	field(
					    default=A__							,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
					__magic_name__						:							int     	=	field(
					    default=A__							,       metadata={'''help''': '''Whether to log verbose messages or not.'''}							,       )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				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(__UpperCamelCase     )






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






@dataclass
class 					__lowercase		:
					__magic_name__						:							int     	=	False
					__magic_name__						:							Optional[Any]     	=	None
					__magic_name__						:							int     	=	'''|'''
					__magic_name__						:							Dict     	=	field(default_factory=A__       )
					__magic_name__						:							Union[str, Any]     	=	field(default_factory=A__       )
					__magic_name__						:							Dict     	=	None


					@classmethod
					def        lowerCAmelCase_    (	cls       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									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       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									# 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       ,       a__						)		->							List[Any]:





									'''simple docstring'''
									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 					__lowercase		:
					__magic_name__						:							List[Any]     	=	42
					__magic_name__						:							Any     	=	True
					__magic_name__						:							int     	=	None
					__magic_name__						:							Any     	=	None
					__magic_name__						:							int     	=	None
					__magic_name__						:							Optional[int]     	=	None


					def __call__(	self       ,       a__						)		->							List[str]:





									'''simple docstring'''
									# 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						)       ,       -1_0_0						)

									A_       	=  labels

									return batch






class 					__lowercase		(					A__       ):


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__						)		->							Union[str, Any]:





									'''simple docstring'''
									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        lowerCamelCase_  (   ):
				A_       	=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

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

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

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

				if data_args.max_duration_in_seconds is not None:

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

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

				logger.warning(F"Updated {len(__UpperCamelCase     )} 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(__UpperCamelCase     ):
								# 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(__UpperCamelCase     )
								return batch

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

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

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

								A_       	=  processor.tokenizer.pad_token_id

								A_       	=  processor.batch_decode(__UpperCamelCase     )
								# we do not want to group tokens when computing the metrics
								A_       	=  processor.batch_decode(pred.label_ids ,						group_tokens=__UpperCamelCase     )
								if logger.isEnabledFor(logging.DEBUG     ):
												for reference, predicted in zip(__UpperCamelCase ,						__UpperCamelCase     ):
																logger.debug(F"reference: \"{reference}\""     )
																logger.debug(F"predicted: \"{predicted}\""     )
																if orthography.untransliterator is not None:
																				logger.debug(F"reference (untransliterated): \"{orthography.untransliterator(__UpperCamelCase     )}\""     )
																				logger.debug(F"predicted (untransliterated): \"{orthography.untransliterator(__UpperCamelCase     )}\""     )

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

								return {"wer": wer}

				if model_args.freeze_feature_extractor:
								model.freeze_feature_extractor()

				A_       	=  CTCTrainer(
				    model=__UpperCamelCase ,						data_collator=__UpperCamelCase ,						args=__UpperCamelCase ,						compute_metrics=__UpperCamelCase ,						train_dataset=__UpperCamelCase ,						eval_dataset=__UpperCamelCase ,						tokenizer=processor.feature_extractor ,						)

				trainer.train()


if __name__ == "__main__":
						main()

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

				for i in range(__UpperCamelCase     ):
								for j in range(__UpperCamelCase     ):
												A_       	=  int(to_grayscale(*img[i][j]     )     )
												A_       	=  [
												    normalize(__UpperCamelCase     ),
												    normalize(greyscale + factor     ),
												    normalize(greyscale + 2 * factor     ),
												]

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f'''Original image with sepia (factor: {percentage})''', img)

						waitKey(0)
						destroyAllWindows()