
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()



class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Dict=None   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	data
    __magic_name__    					=	None





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







    """simple docstring"""






    __magic_name__    					=	""""""
    __magic_name__    					=	self
    while temp:
      string_rep += F'''<{temp.data}> ---> '''
      __magic_name__    					=	temp.next
    string_rep += "<END>"
    return string_rep







def a__   (	A_ ):






  '''simple docstring'''





  if not elements_list:
    raise Exception("""The Elements List is empty""" )

  # Set first element as Head
  __magic_name__    					=	Node(elements_list[0] )
  __magic_name__    					=	head
  # Loop through elements from position 1
  for data in elements_list[1:]:
    __magic_name__    					=	Node(A_ )
    __magic_name__    					=	current.next
  return head


__lowerCAmelCase      :					Union[str, Any]   	=       [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('Creating Linked List from List.')
__lowerCAmelCase      :					Optional[Any]   	=       make_linked_list(list_data)
print('Linked List:')





print(linked_list)

