code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  import os
  from dataclasses import dataclass
  
  import torch
  import torchaudio
  from tqdm import tqdm
  
  from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
           __lowerCamelCase     :			Tuple              =	cuda
           __lowerCamelCase     :			int              =	AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[Any]              =	AutoModelForCTC.from_pretrained(SCREAMING_SNAKE_CASE_			)
           self.model.eval()
           if self.cuda:
                self.model.to(device='cuda'			)
           __lowerCamelCase     :			Tuple              =	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	torchaudio.transforms.Resample(SCREAMING_SNAKE_CASE_    ,  1_60_00			)
           __lowerCamelCase     :			Tuple              =	0
           __lowerCamelCase     :			Optional[int]              =	list(self.processor.tokenizer.get_vocab().keys()			)
           for i in range(len(SCREAMING_SNAKE_CASE_			)			):
                if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                     __lowerCamelCase     :			Optional[int]              =	i
           print('Blank Token id [PAD]/<pad>'    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Optional[int]              =	blank_id
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	torchaudio.load(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Dict              =	self.resampler(SCREAMING_SNAKE_CASE_			).squeeze().numpy()
           return speech
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Dict:
           __lowerCamelCase     :			int              =	self.blank_id
           __lowerCamelCase     :			List[str]              =	'|'.join(item['sent'].split(' '			)			)
           if not os.path.isfile(item['wav_path']			):
                print(item['wav_path']    ,  'not found in wavs directory'			)
  
           __lowerCamelCase     :			Tuple              =	self.speech_file_to_array_fn(item['wav_path']			)
           __lowerCamelCase     :			int              =	self.processor(SCREAMING_SNAKE_CASE_    ,  sampling_rate=1_60_00    ,  return_tensors='pt'    ,  padding=SCREAMING_SNAKE_CASE_			)
           if self.cuda:
                __lowerCamelCase     :			Dict              =	inputs.to(device='cuda'			)
  
           with torch.no_grad():
                __lowerCamelCase     :			Optional[int]              =	self.model(inputs.input_values			).logits
  
           # get the emission probability at frame level
           __lowerCamelCase     :			Optional[int]              =	torch.log_softmax(SCREAMING_SNAKE_CASE_    ,  dim=-1			)
           __lowerCamelCase     :			Union[str, Any]              =	emissions[0].cpu().detach()
  
           # get labels from vocab
           __lowerCamelCase     :			str              =	([''] + list(self.processor.tokenizer.get_vocab().keys()			))[
               :-1
           ]  # logits don't align with the tokenizer's vocab
  
           __lowerCamelCase     :			Union[str, Any]              =	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_			)}
           __lowerCamelCase     :			List[str]              =	[]
           for c in transcript:
                if c in dictionary:
                     tokens.append(dictionary[c]			)
  
           def get_trellis(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=0			):
                __lowerCamelCase     :			Optional[Any]              =	emission.size(0			)
                __lowerCamelCase     :			List[str]              =	len(SCREAMING_SNAKE_CASE_			)
  
                # 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.
                __lowerCamelCase     :			List[str]              =	torch.full((num_frames + 1, num_tokens + 1)    ,  -float('inf'			)			)
                __lowerCamelCase     :			str              =	0
                for t in range(SCREAMING_SNAKE_CASE_			):
                     __lowerCamelCase     :			Union[str, Any]              =	torch.maximum(
                         # Score for staying at the same token
                         trellis[t, 1:] + emission[t, blank_id]    ,  trellis[t, :-1] + emission[t, tokens]    ,  )
                return trellis
  
           __lowerCamelCase     :			Dict              =	get_trellis(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
           @dataclass
           class      UpperCAmelCase_						:
  
  
  
  
  
               """simple docstring"""
  
  
  
  
  
  
               lowerCamelCase						:      int
               lowerCamelCase						:      int
               lowerCamelCase						:      float
  
           def backtrack(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=0			):
                __lowerCamelCase     :			Dict              =	trellis.size(1			) - 1
                __lowerCamelCase     :			str              =	torch.argmax(trellis[:, j]			).item()
  
                __lowerCamelCase     :			int              =	[]
                for t in range(SCREAMING_SNAKE_CASE_    ,  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.
                     __lowerCamelCase     :			Tuple              =	trellis[t - 1, j] + emission[t - 1, blank_id]
                     # Score for token changing from C-1 at T-1 to J at T.
                     __lowerCamelCase     :			Tuple              =	trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
  
                     # 2. Store the path with frame-wise probability.
                     __lowerCamelCase     :			List[str]              =	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    ,  SCREAMING_SNAKE_CASE_			)			)
  
                     # 3. Update the token
                     if changed > stayed:
                          j -= 1
                          if j == 0:
                               break
                else:
                     raise ValueError('Failed to align'			)
                return path[::-1]
  
           __lowerCamelCase     :			Union[str, Any]              =	backtrack(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
           @dataclass
           class      UpperCAmelCase_						:
  
  
  
  
  
               """simple docstring"""
  
  
  
  
  
  
               lowerCamelCase						:      str
               lowerCamelCase						:      int
               lowerCamelCase						:      int
               lowerCamelCase						:      float
  
  
  
  
  
  
  
               def __repr__(							self			)       ->  Dict:
                    return f'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'
  
  
  
  
  
  
  
               @property
               def    lowercase_       (							self			)       ->  Optional[int]:
                    return self.end - self.start
  
           def merge_repeats(SCREAMING_SNAKE_CASE_			):
                __lowerCamelCase       , __lowerCamelCase     :			Dict              =	0, 0
                __lowerCamelCase     :			Optional[int]              =	[]
                while ia < len(SCREAMING_SNAKE_CASE_			):
                     while ia < len(SCREAMING_SNAKE_CASE_			) and path[ia].token_index == path[ia].token_index:
                          ia += 1
                     __lowerCamelCase     :			Any              =	sum(path[k].score for k in range(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			) / (ia - ia)
                     segments.append(
                         Segment(
                             transcript[path[ia].token_index]    ,  path[ia].time_index    ,  path[ia - 1].time_index + 1    ,  SCREAMING_SNAKE_CASE_    ,  )			)
                     __lowerCamelCase     :			List[Any]              =	ia
                return segments
  
           __lowerCamelCase     :			List[str]              =	merge_repeats(SCREAMING_SNAKE_CASE_			)
           with open(item['out_path']    ,  'w'			) as out_align:
                for seg in segments:
                     out_align.write(str(SCREAMING_SNAKE_CASE_			) + '\n'			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  List[Any]:
           if not os.path.exists(SCREAMING_SNAKE_CASE_			):
                os.makedirs(SCREAMING_SNAKE_CASE_			)
  
           # load text file
           __lowerCamelCase     :			List[Any]              =	open(SCREAMING_SNAKE_CASE_    ,  encoding='utf8'			).readlines()
  
           __lowerCamelCase     :			Any              =	[]
           for line in lines:
                if len(line.strip().split('\t'			)			) != 2:
                     print('Script must be in format: 00001  this is my sentence'			)
                     exit()
  
                __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	line.strip().split('\t'			)
                __lowerCamelCase     :			Optional[int]              =	os.path.join(SCREAMING_SNAKE_CASE_    ,  wav_name + '.wav'			)
                __lowerCamelCase     :			Any              =	os.path.join(SCREAMING_SNAKE_CASE_    ,  wav_name + '.txt'			)
  
                items.append({'sent': sentence, 'wav_path': wav_path, 'out_path': out_path}			)
           print('Number of samples found in script file'    ,  len(SCREAMING_SNAKE_CASE_			)			)
  
           for item in tqdm(SCREAMING_SNAKE_CASE_			):
                self.align_single_sample(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
  def     UpperCAmelCase__      (			)  ->	Tuple:
       __lowerCamelCase     :			Dict              =	argparse.ArgumentParser()
  
       parser.add_argument(
           '--model_name'   ,					type=UpperCAmelCase_   ,					default='arijitx/wav2vec2-xls-r-300m-bengali'   ,					help='wav2vec model name'							)
       parser.add_argument('--wav_dir'   ,					type=UpperCAmelCase_   ,					default='./wavs'   ,					help='directory containing wavs'							)
       parser.add_argument('--text_file'   ,					type=UpperCAmelCase_   ,					default='script.txt'   ,					help='file containing text'							)
       parser.add_argument('--input_wavs_sr'   ,					type=UpperCAmelCase_   ,					default=1_60_00   ,					help='sampling rate of input audios'							)
       parser.add_argument(
           '--output_dir'   ,					type=UpperCAmelCase_   ,					default='./out_alignment'   ,					help='output directory containing the alignment files'							)
       parser.add_argument('--cuda'   ,					action='store_true'							)
  
       __lowerCamelCase     :			List[Any]              =	parser.parse_args()
  
       __lowerCamelCase     :			int              =	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()
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  