code: 
  import sys
  
  sys.set_int_max_str_digits(0)  # type: ignore
  
  
  def       a			(   a  )    ->bool:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   [0] * 11
  			SCREAMING_SNAKE_CASE    	=   [0] * 11
  
  			# mark last 9 numbers
  			for _ in range(9  ):
  						SCREAMING_SNAKE_CASE    	=   1
  						SCREAMING_SNAKE_CASE    	=   number // 10
  			# flag
  			SCREAMING_SNAKE_CASE    	=   True
  
  			# check last 9 numbers for pandigitality
  
  			for x in range(9  ):
  						if not check_last[x + 1]:
  									SCREAMING_SNAKE_CASE    	=   False
  			if not f:
  						return f
  
  			# mark first 9 numbers
  			SCREAMING_SNAKE_CASE    	=   int(str(a  )[:9]  )
  
  			for _ in range(9  ):
  						SCREAMING_SNAKE_CASE    	=   1
  						SCREAMING_SNAKE_CASE    	=   number // 10
  
  			# check first 9 numbers for pandigitality
  
  			for x in range(9  ):
  						if not check_front[x + 1]:
  									SCREAMING_SNAKE_CASE    	=   False
  			return f
  
  
  def       a			(   a  )    ->bool:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   [0] * 11
  
  			# mark last 9 numbers
  			for _ in range(9  ):
  						SCREAMING_SNAKE_CASE    	=   1
  						SCREAMING_SNAKE_CASE    	=   number // 10
  			# flag
  			SCREAMING_SNAKE_CASE    	=   True
  
  			# check last 9 numbers for pandigitality
  
  			for x in range(9  ):
  						if not check_last[x + 1]:
  									SCREAMING_SNAKE_CASE    	=   False
  			return f
  
  
  def       a			(   )    ->int:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   1
  			SCREAMING_SNAKE_CASE    	=   1
  			SCREAMING_SNAKE_CASE    	=   2
  			# temporary Fibonacci numbers
  
  			SCREAMING_SNAKE_CASE    	=   1
  			SCREAMING_SNAKE_CASE    	=   1
  			SCREAMING_SNAKE_CASE    	=   2
  			# temporary Fibonacci numbers mod 1e9
  
  			# mod m=1e9, done for fast optimisation
  			SCREAMING_SNAKE_CASE    	=   [0] * 100_0000
  			SCREAMING_SNAKE_CASE    	=   10_0000_0000
  
  			for x in range(100_0000  ):
  						SCREAMING_SNAKE_CASE    	=   (aa + ba) % m
  						SCREAMING_SNAKE_CASE    	=   ba % m
  						SCREAMING_SNAKE_CASE    	=   ca % m
  						if checka(a  ):
  									SCREAMING_SNAKE_CASE    	=   1
  
  			for x in range(100_0000  ):
  						SCREAMING_SNAKE_CASE    	=   a + b
  						SCREAMING_SNAKE_CASE    	=   b
  						SCREAMING_SNAKE_CASE    	=   c
  						# perform check only if in tocheck
  						if tocheck[x + 3] and check(a  ):
  									return x + 3  # first 2 already done
  			return -1
  
  
  if __name__ == "__main__":
  							print(F'''{solution() = }''')
style_context: 
  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()