


'''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 :


 def __init__(			self       :  List[str] ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Optional[int] ,					__lowerCamelCase       :  Optional[int]   ):
    UpperCAmelCase__  :Tuple            =			cuda
    UpperCAmelCase__  :List[Any]            =			AutoConfig.from_pretrained(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[int]            =			AutoModelForCTC.from_pretrained(__lowerCamelCase   )
    self.model.eval()
    if self.cuda:
       self.model.to(device='''cuda'''   )
    UpperCAmelCase__  :List[Any]            =			AutoProcessor.from_pretrained(__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			torchaudio.transforms.Resample(__lowerCamelCase ,					1_6_0_0_0   )
    UpperCAmelCase__  :str            =			0
    UpperCAmelCase__  :int            =			list(self.processor.tokenizer.get_vocab().keys()   )
    for i in range(len(__lowerCamelCase   )   ):
       if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
          UpperCAmelCase__  :str            =			i
    print('''Blank Token id [PAD]/<pad>''' ,					__lowerCamelCase   )
    UpperCAmelCase__  :Optional[Any]            =			blank_id


 def 					__SCREAMING_SNAKE_CASE				(			self       :  Optional[Any] ,					__lowerCamelCase       :  Union[str, Any]   ):
    UpperCAmelCase__      ,	UpperCAmelCase__  :Dict            =			torchaudio.load(__lowerCamelCase   )
    UpperCAmelCase__  :Union[str, Any]            =			self.resampler(__lowerCamelCase   ).squeeze().numpy()
    return speech


 def 					__SCREAMING_SNAKE_CASE				(			self       :  List[Any] ,					__lowerCamelCase       :  Optional[int]   ):
    UpperCAmelCase__  :Dict            =			self.blank_id
    UpperCAmelCase__  :Union[str, Any]            =			'''|'''.join(item['''sent'''].split(''' '''   )   )
    if not os.path.isfile(item['''wav_path''']   ):
       print(item['''wav_path'''] ,					'''not found in wavs directory'''   )

    UpperCAmelCase__  :Optional[Any]            =			self.speech_file_to_array_fn(item['''wav_path''']   )
    UpperCAmelCase__  :Any            =			self.processor(__lowerCamelCase ,					sampling_rate=1_6_0_0_0 ,					return_tensors='''pt''' ,					padding=__lowerCamelCase   )
    if self.cuda:
       UpperCAmelCase__  :Union[str, Any]            =			inputs.to(device='''cuda'''   )

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

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

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

    UpperCAmelCase__  :List[str]            =			{c: i for i, c in enumerate(__lowerCamelCase   )}
    UpperCAmelCase__  :Any            =			[]
    for c in transcript:
       if c in dictionary:
          tokens.append(dictionary[c]   )

    def get_trellis(__lowerCamelCase       :  int ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  str=0   ):
       UpperCAmelCase__  :int            =			emission.size(0   )
       UpperCAmelCase__  :Optional[int]            =			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.
       UpperCAmelCase__  :List[Any]            =			torch.full((num_frames + 1, num_tokens + 1) ,					-float('''inf'''   )   )
       UpperCAmelCase__  :List[Any]            =			0
       for t in range(__lowerCamelCase   ):
          UpperCAmelCase__  :Optional[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

    UpperCAmelCase__  :Tuple            =			get_trellis(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )




    @dataclass
    class 							UpperCAmelCase :
     UpperCAmelCase				=				42
     UpperCAmelCase				=				42
     UpperCAmelCase				=				42

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

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

          # 2. Store the path with frame-wise probability.
          UpperCAmelCase__  :Optional[int]            =			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]

    UpperCAmelCase__  :Optional[Any]            =			backtrack(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase   )




    @dataclass
    class 							UpperCAmelCase :
     UpperCAmelCase				=				42
     UpperCAmelCase				=				42
     UpperCAmelCase				=				42
     UpperCAmelCase				=				42


     def __repr__(			self       :  int   ):
        return f'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''





     @property
     def 					__SCREAMING_SNAKE_CASE				(			self       :  List[str]   ):
        return self.end - self.start

    def merge_repeats(__lowerCamelCase       :  Any   ):
       UpperCAmelCase__      ,	UpperCAmelCase__  :Tuple            =			0, 0
       UpperCAmelCase__  :Optional[int]            =			[]
       while ia < len(__lowerCamelCase   ):
          while ia < len(__lowerCamelCase   ) and path[ia].token_index == path[ia].token_index:
             ia += 1
          UpperCAmelCase__  :Any            =			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 ,					)   )
          UpperCAmelCase__  :int            =			ia
       return segments

    UpperCAmelCase__  :int            =			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       :  Union[str, Any] ,					__lowerCamelCase       :  int ,					__lowerCamelCase       :  Optional[Any] ,					__lowerCamelCase       :  Any   ):
    if not os.path.exists(__lowerCamelCase   ):
       os.makedirs(__lowerCamelCase   )

    # load text file
    UpperCAmelCase__  :Dict            =			open(__lowerCamelCase ,					encoding='''utf8'''   ).readlines()

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

       UpperCAmelCase__      ,	UpperCAmelCase__  :Union[str, Any]            =			line.strip().split('''\t'''   )
       UpperCAmelCase__  :Any            =			os.path.join(__lowerCamelCase ,					wav_name + '''.wav'''   )
       UpperCAmelCase__  :Union[str, Any]            =			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 					a__    (				):
   UpperCAmelCase__  :Optional[int]            =			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=16_000,      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'''	)

   UpperCAmelCase__  :List[Any]            =			parser.parse_args()

   UpperCAmelCase__  :Tuple            =			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'''





from scipy.constants import g


def 					a__    (				UpperCamelCase_	:			float,      UpperCamelCase_	:			float	):
   if mass < 0:
      # handling of negative values of mass
      raise ValueError('''The mass of a body cannot be negative'''	)
   if height < 0:
      # handling of negative values of height
      raise ValueError('''The height above the ground cannot be negative'''	)
   return mass * g * height


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='''potential_energy''')



