





import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
class 				SCREAMING_SNAKE_CASE   :


   def __init__(    self			:				Dict			,     a			:				Tuple			,     a			:				Tuple			,     a			:				str       )->   Optional[Any]:





     """simple docstring"""







     lowercase__				=  cuda
     lowercase__				=  AutoConfig.from_pretrained(_lowerCamelCase       )
     lowercase__				=  AutoModelForCTC.from_pretrained(_lowerCamelCase       )
     self.model.eval()
     if self.cuda:
       self.model.to(device='cuda'       )
     lowercase__				=  AutoProcessor.from_pretrained(_lowerCamelCase       )
     lowercase__				=  torchaudio.transforms.Resample(_lowerCamelCase			,     16_000       )
     lowercase__				=  0
     lowercase__				=  list(self.processor.tokenizer.get_vocab().keys()       )
     for i in range(len(_lowerCamelCase       )       ):
       if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
         lowercase__				=  i
     print('Blank Token id [PAD]/<pad>'			,     _lowerCamelCase       )
     lowercase__				=  blank_id


   def        SCREAMING_SNAKE_CASE_							(    self			:				Dict			,     a			:				str       )->   Any:





     """simple docstring"""







     lowercase__			,    lowercase__				=  torchaudio.load(_lowerCamelCase       )
     lowercase__				=  self.resampler(_lowerCamelCase       ).squeeze().numpy()
     return speech


   def        SCREAMING_SNAKE_CASE_							(    self			:				Optional[Any]			,     a			:				List[str]       )->   List[Any]:





     """simple docstring"""







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

     lowercase__				=  self.speech_file_to_array_fn(item['wav_path']       )
     lowercase__				=  self.processor(_lowerCamelCase			,     sampling_rate=16_000			,     return_tensors='pt'			,     padding=_lowerCamelCase       )
     if self.cuda:
       lowercase__				=  inputs.to(device='cuda'       )

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

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

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

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

     def get_trellis(a			:				Union[str, Any]			,     a			:				int			,     a			:				Union[str, Any]=0       ):
       lowercase__				=  emission.size(0       )
       lowercase__				=  len(_lowerCamelCase       )

       # 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.
       lowercase__				=  torch.full((num_frames + 1, num_tokens + 1)			,     -float('inf'       )       )
       lowercase__				=  0
       for t in range(_lowerCamelCase       ):
         lowercase__				=  torch.maximum(
             # Score for staying at the same token
             trellis[t, 1:] + emission[t, blank_id]			,     trellis[t, :-1] + emission[t, tokens]			,     )
       return trellis

     lowercase__				=  get_trellis(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase       )
     @dataclass
     class 				SCREAMING_SNAKE_CASE   :
        _UpperCamelCase    :		int
        _UpperCamelCase    :		int
        _UpperCamelCase    :		float

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

       lowercase__				=  []
       for t in range(_lowerCamelCase			,     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.
         lowercase__				=  trellis[t - 1, j] + emission[t - 1, blank_id]
         # Score for token changing from C-1 at T-1 to J at T.
         lowercase__				=  trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

         # 2. Store the path with frame-wise probability.
         lowercase__				=  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			,     _lowerCamelCase       )       )

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

     lowercase__				=  backtrack(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase       )
     @dataclass
     class 				SCREAMING_SNAKE_CASE   :
        _UpperCamelCase    :		str
        _UpperCamelCase    :		int
        _UpperCamelCase    :		int
        _UpperCamelCase    :		float


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





          """simple docstring"""







          return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""


        @property
        def        SCREAMING_SNAKE_CASE_							(    self			:				Union[str, Any]       )->   Union[str, Any]:





          """simple docstring"""







          return self.end - self.start

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

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


   def        SCREAMING_SNAKE_CASE_							(    self			:				List[str]			,     a			:				Tuple			,     a			:				str			,     a			:				List[str]       )->   Union[str, Any]:





     """simple docstring"""







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

     # load text file
     lowercase__				=  open(_lowerCamelCase			,     encoding='utf8'       ).readlines()

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

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

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

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






def 			__UpperCamelCase  ()				->   List[str]:
  lowercase__				=  argparse.ArgumentParser()

  parser.add_argument(
      '--model_name'		,						type=lowerCamelCase_		,						default='arijitx/wav2vec2-xls-r-300m-bengali'		,						help='wav2vec model name'		)
  parser.add_argument('--wav_dir'		,						type=lowerCamelCase_		,						default='./wavs'		,						help='directory containing wavs'		)
  parser.add_argument('--text_file'		,						type=lowerCamelCase_		,						default='script.txt'		,						help='file containing text'		)
  parser.add_argument('--input_wavs_sr'		,						type=lowerCamelCase_		,						default=16000		,						help='sampling rate of input audios'		)
  parser.add_argument(
      '--output_dir'		,						type=lowerCamelCase_		,						default='./out_alignment'		,						help='output directory containing the alignment files'		)
  parser.add_argument('--cuda'		,						action='store_true'		)

  lowercase__				=  parser.parse_args()

  lowercase__				=  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()









'''simple docstring'''



import math




def       _lowerCAmelCase  ( lowerCamelCase_	:     int ):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,				int(math.sqrt(lowerCamelCase_ ) + 1 )						,				6 ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True




def       _lowerCAmelCase  ( lowerCamelCase_	:     int = 6_0_0_8_5_1_4_7_5_1_4_3 ):

			try:
						__lowercase   =							int(lowerCamelCase_ )
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.''' )
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.''' )
			__lowercase   =							0
			if is_prime(lowerCamelCase_ ):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(lowerCamelCase_ ):
						return n
			for i in range(3						,				int(math.sqrt(lowerCamelCase_ ) ) + 1						,				2 ):
						if n % i == 0:
									if is_prime(n // i ):
												__lowercase   =							n // i
												break
									elif is_prime(lowerCamelCase_ ):
												__lowercase   =							i
			return max_number


if __name__ == "__main__":
	print(f'''{solution() = }''')





