
'''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 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			int						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			str     ) ->							List[Any]:
        snake_case_    							=				cuda
        snake_case_    							=				AutoConfig.from_pretrained(lowerCAmelCase_     )
        snake_case_    							=				AutoModelForCTC.from_pretrained(lowerCAmelCase_     )
        self.model.eval()
        if self.cuda:
              self.model.to(device="cuda"     )
        snake_case_    							=				AutoProcessor.from_pretrained(lowerCAmelCase_     )
        snake_case_    							=				torchaudio.transforms.Resample(lowerCAmelCase_						,			1_60_00     )
        snake_case_    							=				0
        snake_case_    							=				list(self.processor.tokenizer.get_vocab().keys()     )
        for i in range(len(lowerCAmelCase_     )     ):
              if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                    snake_case_    							=				i
        print("Blank Token id [PAD]/<pad>"						,			lowerCAmelCase_     )
        snake_case_    							=				blank_id

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Optional[int]     ) ->							Tuple:
        snake_case_							,						snake_case_    							=				torchaudio.load(lowerCAmelCase_     )
        snake_case_    							=				self.resampler(lowerCAmelCase_     ).squeeze().numpy()
        return speech

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Tuple     ) ->							Optional[Any]:
        snake_case_    							=				self.blank_id
        snake_case_    							=				"|".join(item["sent"].split(" "     )     )
        if not os.path.isfile(item["wav_path"]     ):
              print(item["wav_path"]						,			"not found in wavs directory"     )

        snake_case_    							=				self.speech_file_to_array_fn(item["wav_path"]     )
        snake_case_    							=				self.processor(lowerCAmelCase_						,			sampling_rate=1_60_00						,			return_tensors="pt"						,			padding=lowerCAmelCase_     )
        if self.cuda:
              snake_case_    							=				inputs.to(device="cuda"     )

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

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

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

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

        def get_trellis(lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Tuple=0     ):
              snake_case_    							=				emission.size(0     )
              snake_case_    							=				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.
              snake_case_    							=				torch.full((num_frames + 1, num_tokens + 1)						,			-float("inf"     )     )
              snake_case_    							=				0
              for t in range(lowerCAmelCase_     ):
                    snake_case_    							=				torch.maximum(
                        # Score for staying at the same token
                        trellis[t, 1:] + emission[t, blank_id]						,			trellis[t, :-1] + emission[t, tokens]						,			)
              return trellis

        snake_case_    							=				get_trellis(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )



        @dataclass
        class 			lowerCAmelCase__ :



          '''simple docstring'''






          _UpperCamelCase							=			42
          _UpperCamelCase							=			42
          _UpperCamelCase							=			42

        def backtrack(lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Any=0     ):
              snake_case_    							=				trellis.size(1     ) - 1
              snake_case_    							=				torch.argmax(trellis[:, j]     ).item()

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

                    # 2. Store the path with frame-wise probability.
                    snake_case_    							=				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]

        snake_case_    							=				backtrack(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )



        @dataclass
        class 			lowerCAmelCase__ :



          '''simple docstring'''






          _UpperCamelCase							=			42
          _UpperCamelCase							=			42
          _UpperCamelCase							=			42
          _UpperCamelCase							=			42

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


          @property
          def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Optional[Any]:
                return self.end - self.start

        def merge_repeats(lowerCAmelCase_	:			Dict     ):
              snake_case_							,						snake_case_    							=				0, 0
              snake_case_    							=				[]
              while ia < len(lowerCAmelCase_     ):
                    while ia < len(lowerCAmelCase_     ) and path[ia].token_index == path[ia].token_index:
                          ia += 1
                    snake_case_    							=				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_						,			)     )
                    snake_case_    							=				ia
              return segments

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


  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Dict     ) ->							Union[str, Any]:
        if not os.path.exists(lowerCAmelCase_     ):
              os.makedirs(lowerCAmelCase_     )

        # load text file
        snake_case_    							=				open(lowerCAmelCase_						,			encoding="utf8"     ).readlines()

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

              snake_case_							,						snake_case_    							=				line.strip().split("\t"     )
              snake_case_    							=				os.path.join(lowerCAmelCase_						,			wav_name + ".wav"     )
              snake_case_    							=				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 	snake_case_							(				):
      snake_case_    							=				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=16_000			,       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"      )

      snake_case_    							=				parser.parse_args()

      snake_case_    							=				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 math import gcd




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 1_20_00	)     ->			int:







   """simple docstring"""
   __snake_case					:   Dict  				=			0
   for d in range(max_d + 1	):
      for n in range(d // 3 + 1	,		(d + 1) // 2	):
         if gcd(A	,		A	) == 1:
            fractions_number += 1
   return fractions_number


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