






"""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				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			cuda
      lowercase				       =			AutoConfig.from_pretrained(__A			)
      lowercase				       =			AutoModelForCTC.from_pretrained(__A			)
      self.model.eval()
      if self.cuda:
        self.model.to(device='cuda'			)
      lowercase				       =			AutoProcessor.from_pretrained(__A			)
      lowercase				       =			torchaudio.transforms.Resample(__A				,						1_6_0_0_0			)
      lowercase				       =			0
      lowercase				       =			list(self.processor.tokenizer.get_vocab().keys()			)
      for i in range(len(__A			)			):
        if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
          lowercase				       =			i
      print('Blank Token id [PAD]/<pad>'				,						__A			)
      lowercase				       =			blank_id







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			torchaudio.load(__A			)
      lowercase				       =			self.resampler(__A			).squeeze().numpy()
      return speech







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      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(__A				,						sampling_rate=1_6_0_0_0				,						return_tensors='pt'				,						padding=__A			)
      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(__A				,						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(__A			)}
      lowercase				       =			[]
      for c in transcript:
        if c in dictionary:
          tokens.append(dictionary[c]			)

      def get_trellis(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=0			):
        lowercase				       =			emission.size(0			)
        lowercase				       =			len(__A			)

        # 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(__A			):
          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(__A				,						__A				,						__A			)

      @dataclass
      class 			a	:
          UpperCAmelCase_					:	int
          UpperCAmelCase_					:	int
          UpperCAmelCase_					:	float

      def backtrack(_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase=0			):
        lowercase				       =			trellis.size(1			) - 1
        lowercase				       =			torch.argmax(trellis[:, j]			).item()

        lowercase				       =			[]
        for t in range(__A				,						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				,						__A			)			)

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

      lowercase				       =			backtrack(__A				,						__A				,						__A				,						__A			)

      @dataclass
      class 			a	:
          UpperCAmelCase_					:	str
          UpperCAmelCase_					:	int
          UpperCAmelCase_					:	int
          UpperCAmelCase_					:	float







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







          @property
          def      UpperCamelCase_ (     self			):
            return self.end - self.start

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

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







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      if not os.path.exists(__A			):
        os.makedirs(__A			)

      # load text file
      lowercase				       =			open(__A				,						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				       =			line.strip().split('\t'			)
        lowercase				       =			os.path.join(__A				,						wav_name + '.wav'			)
        lowercase				       =			os.path.join(__A				,						wav_name + '.txt'			)

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

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


def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			argparse.ArgumentParser()

  parser.add_argument(
      '--model_name'						,   type=lowercase__						,   default='arijitx/wav2vec2-xls-r-300m-bengali'						,   help='wav2vec model name'       )
  parser.add_argument('--wav_dir'						,   type=lowercase__						,   default='./wavs'						,   help='directory containing wavs'       )
  parser.add_argument('--text_file'						,   type=lowercase__						,   default='script.txt'						,   help='file containing text'       )
  parser.add_argument('--input_wavs_sr'						,   type=lowercase__						,   default=1_60_00						,   help='sampling rate of input audios'       )
  parser.add_argument(
      '--output_dir'						,   type=lowercase__						,   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 		_snake_case			(      lowercase__						:    int						)    ->							int:


			'''simple docstring'''





			return sum(int(lowercase__						) for digit in str(lowercase__						)						)






def 		_snake_case			(      lowercase__						:    int = 3_0						)    ->							int:


			'''simple docstring'''





			lowerCAmelCase_       :Union[str, Any]     	=    []
			for digit in range(2 ,	1_0_0						):
						for power in range(2 ,	1_0_0						):
									lowerCAmelCase_       :Tuple     	=    int(math.pow(lowercase__ ,	lowercase__						)						)
									if digit == digit_sum(lowercase__						):
												digit_to_powers.append(lowercase__						)

			digit_to_powers.sort()
			return digit_to_powers[n - 1]


if __name__ == "__main__":
				print(solution())






