





"""simple docstring"""




def   lowercase__     (			_UpperCAmelCase = 4_00_00_00				)     ->			int:






      '''simple docstring'''


      lowercase  :      Optional[Any]					=   1
      lowercase  :      int					=   2
      lowercase  :      Optional[Any]					=   0
      while j <= n:
            if j % 2 == 0:
                  total += j
            lowercase  :      Tuple					=   j, i + j

      return total


if __name__ == "__main__":
     print(f'''{solution() = }''')





"""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      :							Any,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Optional[Any]			)    ->       Optional[Any]:
         lowercase  :      Optional[Any]					=   cuda
         lowercase  :      Optional[Any]					=   AutoConfig.from_pretrained(lowerCAmelCase			)
         lowercase  :      str					=   AutoModelForCTC.from_pretrained(lowerCAmelCase			)
         self.model.eval()
         if self.cuda:
               self.model.to(device='cuda'			)
         lowercase  :      Tuple					=   AutoProcessor.from_pretrained(lowerCAmelCase			)
         lowercase  :      Tuple					=   torchaudio.transforms.Resample(lowerCAmelCase,     16000			)
         lowercase  :      Any					=   0
         lowercase  :      Tuple					=   list(self.processor.tokenizer.get_vocab().keys()			)
         for i in range(len(lowerCAmelCase			)			):
               if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                     lowercase  :      Dict					=   i
         print('Blank Token id [PAD]/<pad>',     lowerCAmelCase			)
         lowercase  :      List[str]					=   blank_id


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							Optional[int]			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      Any					=   torchaudio.load(lowerCAmelCase			)
         lowercase  :      List[Any]					=   self.resampler(lowerCAmelCase			).squeeze().numpy()
         return speech


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

         lowercase  :      Dict					=   self.speech_file_to_array_fn(item['wav_path']			)
         lowercase  :      List[str]					=   self.processor(lowerCAmelCase,     sampling_rate=16000,     return_tensors='pt',     padding=lowerCAmelCase			)
         if self.cuda:
               lowercase  :      Union[str, Any]					=   inputs.to(device='cuda'			)

         with torch.no_grad():
               lowercase  :      List[Any]					=   self.model(inputs.input_values			).logits

         # get the emission probability at frame level
         lowercase  :      Dict					=   torch.log_softmax(lowerCAmelCase,     dim=-1			)
         lowercase  :      Optional[int]					=   emissions[0].cpu().detach()

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

         lowercase  :      Optional[int]					=   {c: i for i, c in enumerate(lowerCAmelCase			)}
         lowercase  :      Union[str, Any]					=   []
         for c in transcript:
               if c in dictionary:
                     tokens.append(dictionary[c]			)

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

         lowercase  :      Any					=   get_trellis(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42

         def backtrack(lowerCAmelCase      :							Any,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							str,     lowerCAmelCase      :							Any=0			):
               lowercase  :      Tuple					=   trellis.size(1			) - 1
               lowercase  :      Optional[int]					=   torch.argmax(trellis[:, j]			).item()

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

                     # 2. Store the path with frame-wise probability.
                     lowercase  :      str					=   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]

         lowercase  :      int					=   backtrack(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42


            def __repr__(							self      :							Union[str, Any]			)    ->       Optional[Any]:
                  return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''






            @property
            def        lowercase				(							self      :							Tuple			)    ->       int:
                  return self.end - self.start

         def merge_repeats(lowerCAmelCase      :							Optional[Any]			):
               lowercase	,    lowercase  :      Tuple					=   0, 0
               lowercase  :      Union[str, Any]					=   []
               while ia < len(lowerCAmelCase			):
                     while ia < len(lowerCAmelCase			) and path[ia].token_index == path[ia].token_index:
                           ia += 1
                     lowercase  :      List[str]					=   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,     )			)
                     lowercase  :      List[Any]					=   ia
               return segments

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






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

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

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

               lowercase	,    lowercase  :      int					=   line.strip().split('\t'			)
               lowercase  :      List[Any]					=   os.path.join(lowerCAmelCase,     wav_name + '.wav'			)
               lowercase  :      List[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   lowercase__     (			)     ->			Dict:






      '''simple docstring'''


      lowercase  :      Union[str, 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_60_00	,      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'				)

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

      lowercase  :      Dict					=   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()
