

from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						seed(1 )
						return [gauss(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )]


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return [k for k in range(SCREAMING_SNAKE_CASE__ ) for _ in range(instance_count[k] )]


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return sum(SCREAMING_SNAKE_CASE__ ) / instance_count


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						return instance_count / total_count


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			[]  # An empty list to store all squared differences
						# iterate over number of elements in items
						for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
												# for loop iterates over number of elements in inner layer of items
												for j in range(len(items[i] ) ):
																		# appending squared differences to 'squared_diff' list
																		squared_diff.append((items[i][j] - means[i]) ** 2 )

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
						snake_case_     =			len(SCREAMING_SNAKE_CASE__ )  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__ )


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			[]
						# for loop iterates over number of elements in list
						for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
												# for loop iterates over number of inner items of each element
												for j in range(len(x_items[i] ) ):
																		snake_case_     =			[]  # to store all discriminant values of each item as a list
																		# for loop iterates over number of classes we have in our dataset
																		for k in range(len(SCREAMING_SNAKE_CASE__ ) ):
																								# appending values of discriminants for each class to 'temp' list
																								temp.append(
																								    x_items[i][j] * (means[k] / variance)
																								    - (means[k] ** 2 / (2 * variance))
																								    + log(probabilities[k] ) )
																		# appending discriminant values of each item to 'results' list
																		results.append(SCREAMING_SNAKE_CASE__ )

						return [result.index(max(SCREAMING_SNAKE_CASE__ ) ) for result in results]


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			sum(1 for i, j in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ) if i == j )
						# percentage of accuracy equals to number of correct predictions divided by number
						# of all data and multiplied by 100
						return (correct / len(SCREAMING_SNAKE_CASE__ )) * 100


lowerCAmelCase_ 			=			TypeVar('''num''')


def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = lambda SCREAMING_SNAKE_CASE__ : True						,	SCREAMING_SNAKE_CASE__ = None						,	):
						while True:
												try:
																		snake_case_     =			input_type(input(SCREAMING_SNAKE_CASE__ ).strip() or default )
																		if condition(SCREAMING_SNAKE_CASE__ ):
																								return user_input
																		else:
																								print(F'''{user_input}: {err_msg}''' )
																								continue
												except ValueError:
																		print(
																		    F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}''' )


def 						__SCREAMING_SNAKE_CASE      ():
						while True:
												print(''' Linear Discriminant Analysis '''.center(50						,	'''*''' ) )
												print('''*''' * 50						,	'''\n''' )
												print('''First of all we should specify the number of classes that''' )
												print('''we want to generate as training dataset''' )
												# Trying to get number of classes
												snake_case_     =			valid_input(
												    input_type=SCREAMING_SNAKE_CASE__						,	condition=lambda SCREAMING_SNAKE_CASE__ : x > 0						,	input_msg='''Enter the number of classes (Data Groupings): '''						,	err_msg='''Number of classes should be positive!'''						,	)

												print('''-''' * 100 )

												# Trying to get the value of standard deviation
												snake_case_     =			valid_input(
												    input_type=SCREAMING_SNAKE_CASE__						,	condition=lambda SCREAMING_SNAKE_CASE__ : x >= 0						,	input_msg=(
												        '''Enter the value of standard deviation'''
												        '''(Default value is 1.0 for all classes): '''
												    )						,	err_msg='''Standard deviation should not be negative!'''						,	default='''1.0'''						,	)

												print('''-''' * 100 )

												# Trying to get number of instances in classes and theirs means to generate
												# dataset
												snake_case_     =			[]  # An empty list to store instance counts of classes in dataset
												for i in range(SCREAMING_SNAKE_CASE__ ):
																		snake_case_     =			valid_input(
																		    input_type=SCREAMING_SNAKE_CASE__						,	condition=lambda SCREAMING_SNAKE_CASE__ : x > 0						,	input_msg=(F'''Enter The number of instances for class_{i+1}: ''')						,	err_msg='''Number of instances should be positive!'''						,	)
																		counts.append(SCREAMING_SNAKE_CASE__ )
												print('''-''' * 100 )

												# An empty list to store values of user-entered means of classes
												snake_case_     =			[]
												for a in range(SCREAMING_SNAKE_CASE__ ):
																		snake_case_     =			valid_input(
																		    input_type=SCREAMING_SNAKE_CASE__						,	input_msg=(F'''Enter the value of mean for class_{a+1}: ''')						,	err_msg='''This is an invalid value.'''						,	)
																		user_means.append(SCREAMING_SNAKE_CASE__ )
												print('''-''' * 100 )

												print('''Standard deviation: '''						,	SCREAMING_SNAKE_CASE__ )
												# print out the number of instances in classes in separated line
												for i, count in enumerate(SCREAMING_SNAKE_CASE__						,	1 ):
																		print(F'''Number of instances in class_{i} is: {count}''' )
												print('''-''' * 100 )

												# print out mean values of classes separated line
												for i, user_mean in enumerate(SCREAMING_SNAKE_CASE__						,	1 ):
																		print(F'''Mean of class_{i} is: {user_mean}''' )
												print('''-''' * 100 )

												# Generating training dataset drawn from gaussian distribution
												snake_case_     =			[
												    gaussian_distribution(user_means[j]						,	SCREAMING_SNAKE_CASE__						,	counts[j] )
												    for j in range(SCREAMING_SNAKE_CASE__ )
												]
												print('''Generated Normal Distribution: \n'''						,	SCREAMING_SNAKE_CASE__ )
												print('''-''' * 100 )

												# Generating Ys to detecting corresponding classes
												snake_case_     =			y_generator(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ )
												print('''Generated Corresponding Ys: \n'''						,	SCREAMING_SNAKE_CASE__ )
												print('''-''' * 100 )

												# Calculating the value of actual mean for each class
												snake_case_     =			[calculate_mean(counts[k]						,	x[k] ) for k in range(SCREAMING_SNAKE_CASE__ )]
												# for loop iterates over number of elements in 'actual_means' list and print
												# out them in separated line
												for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE__						,	1 ):
																		print(F'''Actual(Real) mean of class_{i} is: {actual_mean}''' )
												print('''-''' * 100 )

												# Calculating the value of probabilities for each class
												snake_case_     =			[
												    calculate_probabilities(counts[i]						,	sum(SCREAMING_SNAKE_CASE__ ) ) for i in range(SCREAMING_SNAKE_CASE__ )
												]

												# for loop iterates over number of elements in 'probabilities' list and print
												# out them in separated line
												for i, probability in enumerate(SCREAMING_SNAKE_CASE__						,	1 ):
																		print(F'''Probability of class_{i} is: {probability}''' )
												print('''-''' * 100 )

												# Calculating the values of variance for each class
												snake_case_     =			calculate_variance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	sum(SCREAMING_SNAKE_CASE__ ) )
												print('''Variance: '''						,	SCREAMING_SNAKE_CASE__ )
												print('''-''' * 100 )

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												snake_case_     =			predict_y_values(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ )
												print('''-''' * 100 )

												# Calculating Accuracy of the model
												print(F'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ )}''' )
												print('''-''' * 100 )
												print(''' DONE '''.center(100						,	'''+''' ) )

												if input('''Press any key to restart or \'q\' for quit: ''' ).strip().lower() == "q":
																		print('''\n''' + '''GoodBye!'''.center(100						,	'''-''' ) + '''\n''' )
																		break
												system('''cls''' if name == '''nt''' else '''clear''' )  # noqa: S605


if __name__ == "__main__":
					main()


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


lowerCAmelCase_ 			=			logging.getLogger(__name__)





@dataclass
class 			snake_case_     :







			'''simple docstring'''

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






def 						__SCREAMING_SNAKE_CASE      (SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,	datefmt='''%m/%d/%Y %H:%M:%S'''						,	handlers=[logging.StreamHandler(sys.stdout )]						,	)
						snake_case_     =			logging.WARNING
						if model_args.verbose_logging:
												snake_case_     =			logging.DEBUG
						elif trainer_utils.is_main_process(training_args.local_rank ):
												snake_case_     =			logging.INFO
						logger.setLevel(SCREAMING_SNAKE_CASE__ )





@dataclass
class 			snake_case_     :







			'''simple docstring'''

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






@dataclass
class 			snake_case_     :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     bool		    =       False
			SCREAMING_SNAKE_CASE  :     Optional[str]		    =       None
			SCREAMING_SNAKE_CASE  :     Optional[str]		    =       "|"
			SCREAMING_SNAKE_CASE  :     Optional[Dict[str, str]]		    =       field(default_factory=__A      )
			SCREAMING_SNAKE_CASE  :     Optional[Set[str]]		    =       field(default_factory=__A      )
			SCREAMING_SNAKE_CASE  :     Optional[Callable[[str], str]]		    =       None







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







			def        snake_case__(  self				:       Tuple							,     _UpperCamelCase				:       ModelArguments							)   ->WavaVecaProcessor:
									snake_case_     =			WavaVecaFeatureExtractor.from_pretrained(
									    model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							)
									if self.vocab_file:
															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:
															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(_UpperCamelCase							,     _UpperCamelCase							)






@dataclass
class 			snake_case_     :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     WavaVecaProcessor
			SCREAMING_SNAKE_CASE  :     Union[bool, str]		    =       True
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       None
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       None
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       None
			SCREAMING_SNAKE_CASE  :     Optional[int]		    =       None







			def __call__(  self				:       Optional[Any]							,     _UpperCamelCase				:       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
									snake_case_     =			[{'''input_values''': feature['''input_values''']} for feature in features]
									snake_case_     =			[{'''input_ids''': feature['''labels''']} for feature in features]

									snake_case_     =			self.processor.pad(
									    _UpperCamelCase							,     padding=self.padding							,     max_length=self.max_length							,     pad_to_multiple_of=self.pad_to_multiple_of							,     return_tensors='''pt'''							,     )
									snake_case_     =			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
									snake_case_     =			labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1							)							,     -1_0_0							)

									snake_case_     =			labels

									return batch






class 			snake_case_     (	__A      ):







			'''simple docstring'''

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

									if self.use_amp:
															with autocast():
																					snake_case_     =			self.compute_loss(_UpperCamelCase							,     _UpperCamelCase							)
									else:
															snake_case_     =			self.compute_loss(_UpperCamelCase							,     _UpperCamelCase							)

									if self.args.n_gpu > 1:
															if model.module.config.ctc_loss_reduction == "mean":
																					snake_case_     =			loss.mean()
															elif model.module.config.ctc_loss_reduction == "sum":
																					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:
															snake_case_     =			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 						__SCREAMING_SNAKE_CASE      ():
						# 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.

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

						snake_case_,					snake_case_,					snake_case_     =			parser.parse_args_into_dataclasses()
						configure_logger(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ )

						snake_case_     =			Orthography.from_name(data_args.orthography.lower() )
						snake_case_     =			orthography.create_processor(SCREAMING_SNAKE_CASE__ )
						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 )						,	)

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

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

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

						snake_case_     =			train_dataset.map(SCREAMING_SNAKE_CASE__						,	remove_columns=[data_args.speech_file_column] )
						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__ ):
																		return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

												snake_case_     =			len(SCREAMING_SNAKE_CASE__ )
												snake_case_     =			len(SCREAMING_SNAKE_CASE__ )
												snake_case_     =			train_dataset.filter(SCREAMING_SNAKE_CASE__						,	remove_columns=['''duration_in_seconds'''] )
												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}"''' )
						snake_case_     =			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}.'''

												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

						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						,	)
						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						,	)

						snake_case_     =			DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__ )

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

												snake_case_     =			processor.tokenizer.pad_token_id

												snake_case_     =			processor.batch_decode(SCREAMING_SNAKE_CASE__ )
												# we do not want to group tokens when computing the metrics
												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__ )}"''' )

												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()

						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()