
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      :			Any      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    __magic_name__    					=	cuda
    __magic_name__    					=	AutoConfig.from_pretrained(UpperCamelCase__   )
    __magic_name__    					=	AutoModelForCTC.from_pretrained(UpperCamelCase__   )
    self.model.eval()
    if self.cuda:
      self.model.to(device="""cuda"""   )
    __magic_name__    					=	AutoProcessor.from_pretrained(UpperCamelCase__   )
    __magic_name__    					=	torchaudio.transforms.Resample(UpperCamelCase__      ,							1_6000   )
    __magic_name__    					=	0
    __magic_name__    					=	list(self.processor.tokenizer.get_vocab().keys()   )
    for i in range(len(UpperCamelCase__   )   ):
      if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
        __magic_name__    					=	i
    print("""Blank Token id [PAD]/<pad>"""      ,							UpperCamelCase__   )
    __magic_name__    					=	blank_id
  def 							_lowercase							(			self      :			List[Any]      ,							UpperCamelCase__      :			Union[str, Any]   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	torchaudio.load(UpperCamelCase__   )
    __magic_name__    					=	self.resampler(UpperCamelCase__   ).squeeze().numpy()
    return speech
  def 							_lowercase							(			self      :			Dict      ,							UpperCamelCase__      :			Optional[int]   )      ->     List[str]:







    """simple docstring"""






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

    __magic_name__    					=	self.speech_file_to_array_fn(item["""wav_path"""]   )
    __magic_name__    					=	self.processor(UpperCamelCase__      ,							sampling_rate=1_6000      ,							return_tensors="""pt"""      ,							padding=UpperCamelCase__   )
    if self.cuda:
      __magic_name__    					=	inputs.to(device="""cuda"""   )

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

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

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

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

    def get_trellis(UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Dict      ,							UpperCamelCase__      :			Optional[Any]=0   ):
      __magic_name__    					=	emission.size(0   )
      __magic_name__    					=	len(UpperCamelCase__   )

      # 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.
      __magic_name__    					=	torch.full((num_frames + 1, num_tokens + 1)      ,							-float("""inf"""   )   )
      __magic_name__    					=	0
      for t in range(UpperCamelCase__   ):
        __magic_name__    					=	torch.maximum(
            # Score for staying at the same token
            trellis[t, 1:] + emission[t, blank_id]      ,							trellis[t, :-1] + emission[t, tokens]      ,							)
      return trellis

    __magic_name__    					=	get_trellis(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )




    @dataclass
    class 	UpperCAmelCase_      :


      '''simple docstring'''







      a__   	=		42
      a__   	=		42
      a__   	=		42

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

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

        # 2. Store the path with frame-wise probability.
        __magic_name__    					=	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      ,							UpperCamelCase__   )   )

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

    __magic_name__    					=	backtrack(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )




    @dataclass
    class 	UpperCAmelCase_      :


      '''simple docstring'''







      a__   	=		42
      a__   	=		42
      a__   	=		42
      a__   	=		42
      def __repr__(			self      :			Tuple   )      ->     Any:







        """simple docstring"""






        return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''





      @property
      def 							_lowercase							(			self      :			Tuple   )      ->     Optional[Any]:







        """simple docstring"""






        return self.end - self.start

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

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





  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Any   )      ->     Union[str, Any]:







    """simple docstring"""






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

    # load text file
    __magic_name__    					=	open(UpperCamelCase__      ,							encoding="""utf8"""   ).readlines()

    __magic_name__    					=	[]
    for line in lines:
      if len(line.strip().split("""\t"""   )   ) != 2:
        print("""Script must be in format: 00001  this is my sentence"""   )
        exit()

      __magic_name__					,   __magic_name__    					=	line.strip().split("""\t"""   )
      __magic_name__    					=	os.path.join(UpperCamelCase__      ,							wav_name + """.wav"""   )
      __magic_name__    					=	os.path.join(UpperCamelCase__      ,							wav_name + """.txt"""   )

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

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







def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	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=16000,					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""" )

  __magic_name__    					=	parser.parse_args()

  __magic_name__    					=	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()



import sys

sys.set_int_max_str_digits(0)  # type: ignore

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11
  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  if not f:
    return f

  # mark first 9 numbers
  __magic_name__    					=	int(str(A_ )[:9] )

  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10

  # check first 9 numbers for pandigitality

  for x in range(9 ):
    if not check_front[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers

  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers mod 1e9

  # mod m=1e9, done for fast optimisation
  __magic_name__    					=	[0] * 1000000
  __magic_name__    					=	1000000000

  for x in range(1000000 ):
    __magic_name__    					=	(aa + ba) % m
    __magic_name__    					=	ba % m
    __magic_name__    					=	ca % m
    if checka(A_ ):
      __magic_name__    					=	1

  for x in range(1000000 ):
    __magic_name__    					=	a + b
    __magic_name__    					=	b
    __magic_name__    					=	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() = }''')

