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



def       a			(   a		,	a  )    ->Tuple:




			'''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(a  )





@dataclass
class 						lowerCamelCase			:
	UpperCamelCase_     :				str								=  field(
	    default=__lowerCamelCase   ,		metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
	UpperCamelCase_     :				Optional[str]								=  field(
	    default=__lowerCamelCase   ,		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=__lowerCamelCase   ,		metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}   ,		)
	UpperCamelCase_     :				Optional[float]								=  field(
	    default=__lowerCamelCase   ,		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=__lowerCamelCase   ,		metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}				)
	UpperCamelCase_     :				Optional[int]								=  field(
	    default=__lowerCamelCase   ,		metadata={'help': 'The number of processes to use for the preprocessing.'}   ,		)





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







	@classmethod
	def    snake_case__    (    cls			:Tuple      ,    lowercase			:str )		->			int:







				"""simple docstring"""






				if name == "librispeech":
							return cls()
				if name == "timit":
							return cls(
							    do_lower_case=lowercase      ,    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(lowercase ).parent.joinpath('''vocab/buckwalter.json''' )      ,    word_delimiter_token='''/'''      ,    translation_table=str.maketrans(lowercase )      ,    words_to_remove={'''sil'''}      ,    untransliterator=arabic.buckwalter.untransliterate      ,    )
				raise ValueError(f"""Unsupported orthography: '{name}'.""" )







	def    snake_case__    (    self			:str      ,    lowercase			:str )		->			str:







				"""simple docstring"""






				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    snake_case__    (    self			:Any      ,    lowercase			:ModelArguments )		->			WavaVecaProcessor:







				"""simple docstring"""






				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(lowercase      ,    lowercase )





@dataclass
class 						lowerCamelCase			:
	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			:Any      ,    lowercase			:List[Dict[str, Union[List[int], torch.Tensor]]] )		->			Dict[str, torch.Tensor]:







				"""simple docstring"""






				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(
				    lowercase      ,    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=lowercase      ,    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 )      ,    -1_0_0 )

				SCREAMING_SNAKE_CASE    	=   labels

				return batch





class 						lowerCamelCase			(					__lowerCamelCase				):







	def    snake_case__    (    self			:Tuple      ,    lowercase			:nn.Module      ,    lowercase			:Dict[str, Union[torch.Tensor, Any]] )		->			torch.Tensor:







				"""simple docstring"""






				model.train()
				SCREAMING_SNAKE_CASE    	=   self._prepare_inputs(lowercase )

				if self.use_amp:
							with autocast():
										SCREAMING_SNAKE_CASE    	=   self.compute_loss(lowercase      ,    lowercase )
				else:
							SCREAMING_SNAKE_CASE    	=   self.compute_loss(lowercase      ,    lowercase )

				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(lowercase ).backward()
				elif self.use_apex:
							with amp.scale_loss(lowercase      ,    self.optimizer ) as scaled_loss:
										scaled_loss.backward()
				elif self.deepspeed:
							self.deepspeed.backward(lowercase )
				else:
							loss.backward()

				return loss.detach()



def       a			(   )    ->Tuple:




			'''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(a		,	a  )

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

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

			if data_args.max_duration_in_seconds is not None:

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

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

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

			SCREAMING_SNAKE_CASE    	=   train_dataset.map(
			    a		,	batch_size=training_args.per_device_train_batch_size		,	batched=a		,	num_proc=data_args.preprocessing_num_workers		,	)
			SCREAMING_SNAKE_CASE    	=   val_dataset.map(
			    a		,	batch_size=training_args.per_device_train_batch_size		,	batched=a		,	num_proc=data_args.preprocessing_num_workers		,	)

			SCREAMING_SNAKE_CASE    	=   DataCollatorCTCWithPadding(processor=a		,	padding=a  )

			def compute_metrics(a  ):
						SCREAMING_SNAKE_CASE    	=   pred.predictions
						SCREAMING_SNAKE_CASE    	=   np.argmax(a		,	axis=-1  )

						SCREAMING_SNAKE_CASE    	=   processor.tokenizer.pad_token_id

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

						SCREAMING_SNAKE_CASE    	=   wer_metric.compute(predictions=a		,	references=a  )

						return {"wer": wer}

			if model_args.freeze_feature_extractor:
						model.freeze_feature_extractor()

			SCREAMING_SNAKE_CASE    	=   CTCTrainer(
			    model=a		,	data_collator=a		,	args=a		,	compute_metrics=a		,	train_dataset=a		,	eval_dataset=a		,	tokenizer=processor.feature_extractor		,	)

			trainer.train()


if __name__ == "__main__":
							main()

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor





class 						lowerCamelCase			:







	def __init__(    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   cuda
				SCREAMING_SNAKE_CASE    	=   AutoConfig.from_pretrained(lowercase )
				SCREAMING_SNAKE_CASE    	=   AutoModelForCTC.from_pretrained(lowercase )
				self.model.eval()
				if self.cuda:
							self.model.to(device='''cuda''' )
				SCREAMING_SNAKE_CASE    	=   AutoProcessor.from_pretrained(lowercase )
				SCREAMING_SNAKE_CASE    	=   torchaudio.transforms.Resample(lowercase      ,    1_6_0_0_0 )
				SCREAMING_SNAKE_CASE    	=   0
				SCREAMING_SNAKE_CASE    	=   list(self.processor.tokenizer.get_vocab().keys() )
				for i in range(len(lowercase ) ):
							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										SCREAMING_SNAKE_CASE    	=   i
				print('''Blank Token id [PAD]/<pad>'''      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   blank_id







	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
				return speech







	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.blank_id
				SCREAMING_SNAKE_CASE    	=   '''|'''.join(item['''sent'''].split(''' ''' ) )
				if not os.path.isfile(item['''wav_path'''] ):
							print(item['''wav_path''']      ,    '''not found in wavs directory''' )

				SCREAMING_SNAKE_CASE    	=   self.speech_file_to_array_fn(item['''wav_path'''] )
				SCREAMING_SNAKE_CASE    	=   self.processor(lowercase      ,    sampling_rate=1_6_0_0_0      ,    return_tensors='''pt'''      ,    padding=lowercase )
				if self.cuda:
							SCREAMING_SNAKE_CASE    	=   inputs.to(device='''cuda''' )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE    	=   self.model(inputs.input_values ).logits

				# get the emission probability at frame level
				SCREAMING_SNAKE_CASE    	=   torch.log_softmax(lowercase      ,    dim=-1 )
				SCREAMING_SNAKE_CASE    	=   emissions[0].cpu().detach()

				# get labels from vocab
				SCREAMING_SNAKE_CASE    	=   ([''''''] + list(self.processor.tokenizer.get_vocab().keys() ))[
				    :-1
				]  # logits don't align with the tokenizer's vocab

				SCREAMING_SNAKE_CASE    	=   {c: i for i, c in enumerate(lowercase )}
				SCREAMING_SNAKE_CASE    	=   []
				for c in transcript:
							if c in dictionary:
										tokens.append(dictionary[c] )

				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
							SCREAMING_SNAKE_CASE    	=   len(lowercase )

							# Trellis has extra diemsions for both time axis and tokens.
							# The extra dim for tokens represents <SoS> (start-of-sentence)
							# The extra dim for time axis is for simplification of the code.
							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
							SCREAMING_SNAKE_CASE    	=   0
							for t in range(lowercase ):
										SCREAMING_SNAKE_CASE    	=   torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]      ,    trellis[t, :-1] + emission[t, tokens]      ,    )
							return trellis

				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float

				def backtrack(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0 ):
							SCREAMING_SNAKE_CASE    	=   trellis.size(1 ) - 1
							SCREAMING_SNAKE_CASE    	=   torch.argmax(trellis[:, j] ).item()

							SCREAMING_SNAKE_CASE    	=   []
							for t in range(lowercase      ,    0      ,    -1 ):
										# 1. Figure out if the current position was stay or change
										# Note (again):
										# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
										# Score for token staying the same from time frame J-1 to T.
										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j] + emission[t - 1, blank_id]
										# Score for token changing from C-1 at T-1 to J at T.
										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										SCREAMING_SNAKE_CASE    	=   emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
										# Return token index and time index in non-trellis coordinate.
										path.append(Point(j - 1      ,    t - 1      ,    lowercase ) )

										# 3. Update the token
										if changed > stayed:
													j -= 1
													if j == 0:
																break
							else:
										raise ValueError('''Failed to align''' )
							return path[::-1]

				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				str
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float







					def __repr__(    self			:Optional[Any] )		->			str:







								"""simple docstring"""






								return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""







					@property
					def    snake_case__    (    self			:List[str] )		->			Any:







								"""simple docstring"""






								return self.end - self.start

				def merge_repeats(lowercase			:List[str] ):
							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   0, 0
							SCREAMING_SNAKE_CASE    	=   []
							while ia < len(lowercase ):
										while ia < len(lowercase ) and path[ia].token_index == path[ia].token_index:
													ia += 1
										SCREAMING_SNAKE_CASE    	=   sum(path[k].score for k in range(lowercase      ,    lowercase ) ) / (ia - ia)
										segments.append(
										    Segment(
										        transcript[path[ia].token_index]      ,    path[ia].time_index      ,    path[ia - 1].time_index + 1      ,    lowercase      ,    ) )
										SCREAMING_SNAKE_CASE    	=   ia
							return segments

				SCREAMING_SNAKE_CASE    	=   merge_repeats(lowercase )
				with open(item['''out_path''']      ,    '''w''' ) as out_align:
							for seg in segments:
										out_align.write(str(lowercase ) + '''\n''' )







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:







				"""simple docstring"""






				if not os.path.exists(lowercase ):
							os.makedirs(lowercase )

				# load text file
				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()

				SCREAMING_SNAKE_CASE    	=   []
				for line in lines:
							if len(line.strip().split('''\t''' ) ) != 2:
										print('''Script must be in format: 00001  this is my sentence''' )
										exit()

							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.txt''' )

							items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path} )
				print('''Number of samples found in script file'''      ,    len(lowercase ) )

				for item in tqdm(lowercase ):
							self.align_single_sample(lowercase )



def       a			(   )    ->Optional[int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name'''		,	type=a		,	default='''arijitx/wav2vec2-xls-r-300m-bengali'''		,	help='''wav2vec model name'''  )
			parser.add_argument('''--wav_dir'''		,	type=a		,	default='''./wavs'''		,	help='''directory containing wavs'''  )
			parser.add_argument('''--text_file'''		,	type=a		,	default='''script.txt'''		,	help='''file containing text'''  )
			parser.add_argument('''--input_wavs_sr'''		,	type=a		,	default=1_6000		,	help='''sampling rate of input audios'''  )
			parser.add_argument(
			    '''--output_dir'''		,	type=a		,	default='''./out_alignment'''		,	help='''output directory containing the alignment files'''  )
			parser.add_argument('''--cuda'''		,	action='''store_true'''  )

			SCREAMING_SNAKE_CASE    	=   parser.parse_args()

			SCREAMING_SNAKE_CASE    	=   WavaVecaAligner(args.model_name		,	args.input_wavs_sr		,	args.cuda  )
			aligner.align_data(args.wav_dir		,	args.text_file		,	args.output_dir  )


if __name__ == "__main__":
							main()