






"""simple docstring"""


def 				a (     UpperCamelCase_							:				list[list[int]]		)  ->		str:
 if len(UpperCamelCase_		) == 0 or not all(len(UpperCamelCase_		) == 2 for pair in coordinates		):
  raise ValueError('The program cannot work out a fitting polynomial.'		)

 if len({tuple(UpperCamelCase_		) for pair in coordinates}		) != len(UpperCamelCase_		):
  raise ValueError('The program cannot work out a fitting polynomial.'		)

 __A	:       Tuple       =	{x for x, _ in coordinates}
 if len(UpperCamelCase_		) == 1:
  return F"x={coordinates[0][0]}"

 if len(UpperCamelCase_		) != len(UpperCamelCase_		):
  raise ValueError('The program cannot work out a fitting polynomial.'		)

 __A	:       str       =	len(UpperCamelCase_		)

 __A	:       Any       =	0
 __A	:       list[list[float]]       =	[]
 # put the x and x to the power values in a matrix
 while count_of_line < x:
  __A	:       Optional[Any]       =	0
  __A	:       Any       =	coordinates[count_of_line][0]
  __A	:       list[float]       =	[]
  while count_in_line < x:
   count_line.append(a ** (x - (count_in_line + 1))		)
   count_in_line += 1
  matrix.append(UpperCamelCase_		)
  count_of_line += 1

 __A	:       List[Any]       =	0
 # put the y values into a vector
 __A	:       list[float]       =	[]
 while count_of_line < x:
  vector.append(coordinates[count_of_line][1]		)
  count_of_line += 1

 __A	:       Optional[Any]       =	0

 while count < x:
  __A	:       Dict       =	0
  while zahlen < x:
   if count == zahlen:
    zahlen += 1
   if zahlen == x:
    break
   __A	:       List[str]       =	matrix[zahlen][count] / matrix[count][count]
   for counting_columns, item in enumerate(matrix[count]		):
    # manipulating all the values in the matrix
    matrix[zahlen][counting_columns] -= item * bruch
   # manipulating the values in the vector
   vector[zahlen] -= vector[count] * bruch
   zahlen += 1
  count += 1

 __A	:       List[Any]       =	0
 # make solutions
 __A	:       list[str]       =	[]
 while count < x:
  solution.append(str(vector[count] / matrix[count][count]		)		)
  count += 1

 __A	:       Dict       =	0
 __A	:       List[Any]       =	'f(x)='

 while count < x:
  __A	:       list[str]       =	solution[count].split('E'		)
  if len(UpperCamelCase_		) > 1:
   __A	:       Tuple       =	F"{remove_e[0]}*10^{remove_e[1]}"
  solved += F"x^{x - (count + 1)}*{solution[count]}"
  if count + 1 != x:
   solved += "+"
  count += 1

 return solved


if __name__ == "__main__":
   print(points_to_polynomial([]))
   print(points_to_polynomial([[]]))
   print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
   print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
   print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
   print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
   print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
   print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
   print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
   print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))













"""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 __a   :



  def __init__(self  :		Dict				,			lowercase_  :		str				,			lowercase_  :		Optional[Any]				,			lowercase_  :		Tuple   )		->							int:
   __A	:       Union[str, Any]       =	cuda
   __A	:       List[Any]       =	AutoConfig.from_pretrained(lowercase_   )
   __A	:       Any       =	AutoModelForCTC.from_pretrained(lowercase_   )
   self.model.eval()
   if self.cuda:
    self.model.to(device='cuda'   )
   __A	:       Union[str, Any]       =	AutoProcessor.from_pretrained(lowercase_   )
   __A	:       List[str]       =	torchaudio.transforms.Resample(lowercase_				,			16_000   )
   __A	:       Dict       =	0
   __A	:       Optional[int]       =	list(self.processor.tokenizer.get_vocab().keys()   )
   for i in range(len(lowercase_   )   ):
    if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
     __A	:       List[str]       =	i
   print('Blank Token id [PAD]/<pad>'				,			lowercase_   )
   __A	:       List[str]       =	blank_id



  def 						A_				(self  :		List[str]				,			lowercase_  :		Union[str, Any]   )		->							Optional[int]:
   __A    ,					__A	:       Tuple       =	torchaudio.load(lowercase_   )
   __A	:       int       =	self.resampler(lowercase_   ).squeeze().numpy()
   return speech



  def 						A_				(self  :		Any				,			lowercase_  :		Any   )		->							List[Any]:
   __A	:       int       =	self.blank_id
   __A	:       List[str]       =	'|'.join(item['sent'].split(' '   )   )
   if not os.path.isfile(item['wav_path']   ):
    print(item['wav_path']				,			'not found in wavs directory'   )

   __A	:       List[Any]       =	self.speech_file_to_array_fn(item['wav_path']   )
   __A	:       Any       =	self.processor(lowercase_				,			sampling_rate=16_000				,			return_tensors='pt'				,			padding=lowercase_   )
   if self.cuda:
    __A	:       int       =	inputs.to(device='cuda'   )

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

   # get the emission probability at frame level
   __A	:       Any       =	torch.log_softmax(lowercase_				,			dim=-1   )
   __A	:       Tuple       =	emissions[0].cpu().detach()

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

   __A	:       int       =	{c: i for i, c in enumerate(lowercase_   )}
   __A	:       str       =	[]
   for c in transcript:
    if c in dictionary:
     tokens.append(dictionary[c]   )

   def get_trellis(lowercase_  :		int				,			lowercase_  :		List[Any]				,			lowercase_  :		str=0   ):
    __A	:       List[str]       =	emission.size(0   )
    __A	:       List[Any]       =	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.
    __A	:       int       =	torch.full((num_frames + 1, num_tokens + 1)				,			-float('inf'   )   )
    __A	:       str       =	0
    for t in range(lowercase_   ):
     __A	:       List[str]       =	torch.maximum(
         # Score for staying at the same token
         trellis[t, 1:] + emission[t, blank_id]				,			trellis[t, :-1] + emission[t, tokens]				,			)
    return trellis

   __A	:       Dict       =	get_trellis(lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float

   def backtrack(lowercase_  :		Any				,			lowercase_  :		str				,			lowercase_  :		Dict				,			lowercase_  :		int=0   ):
    __A	:       str       =	trellis.size(1   ) - 1
    __A	:       int       =	torch.argmax(trellis[:, j]   ).item()

    __A	:       int       =	[]
    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.
     __A	:       List[Any]       =	trellis[t - 1, j] + emission[t - 1, blank_id]
     # Score for token changing from C-1 at T-1 to J at T.
     __A	:       Tuple       =	trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

     # 2. Store the path with frame-wise probability.
     __A	:       Tuple       =	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]

   __A	:       Any       =	backtrack(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    str
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float



     def __repr__(self  :		Optional[Any]   )		->							Union[str, Any]:
      return f"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"



     @property
     def 						A_				(self  :		int   )		->							Dict:
      return self.end - self.start

   def merge_repeats(lowercase_  :		Dict   ):
    __A    ,					__A	:       List[str]       =	0, 0
    __A	:       Optional[int]       =	[]
    while ia < len(lowercase_   ):
     while ia < len(lowercase_   ) and path[ia].token_index == path[ia].token_index:
      ia += 1
     __A	:       Optional[Any]       =	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_				,			)   )
     __A	:       Any       =	ia
    return segments

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



  def 						A_				(self  :		int				,			lowercase_  :		Optional[int]				,			lowercase_  :		List[str]				,			lowercase_  :		str   )		->							Optional[Any]:
   if not os.path.exists(lowercase_   ):
    os.makedirs(lowercase_   )

   # load text file
   __A	:       Optional[int]       =	open(lowercase_				,			encoding='utf8'   ).readlines()

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

    __A    ,					__A	:       Union[str, Any]       =	line.strip().split('\t'   )
    __A	:       List[Any]       =	os.path.join(lowercase_				,			wav_name + '.wav'   )
    __A	:       Optional[Any]       =	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 (     )  ->		Any:
 __A	:       Any       =	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_6_0_0_0     ,	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'		)

 __A	:       Tuple       =	parser.parse_args()

 __A	:       Optional[Any]       =	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()





