





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			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Union[str, Any]:






        '''simple docstring'''






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







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Optional[int]:






        '''simple docstring'''






        __snake_case      ,				__snake_case				:			Optional[Any]            =    torchaudio.load(UpperCAmelCase				)
        __snake_case				:			Dict            =    self.resampler(UpperCAmelCase				).squeeze().numpy()
        return speech







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			List[str]            =    self.blank_id
        __snake_case				:			List[str]            =    "|".join(item["sent"].split(" "				)				)
        if not os.path.isfile(item["wav_path"]				):
          print(item["wav_path"]			,  "not found in wavs directory"				)

        __snake_case				:			Tuple            =    self.speech_file_to_array_fn(item["wav_path"]				)
        __snake_case				:			List[Any]            =    self.processor(UpperCAmelCase			,  sampling_rate=16000			,  return_tensors="pt"			,  padding=UpperCAmelCase				)
        if self.cuda:
          __snake_case				:			List[Any]            =    inputs.to(device="cuda"				)

        with torch.no_grad():
          __snake_case				:			Optional[int]            =    self.model(inputs.input_values				).logits

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

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

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

        def get_trellis(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=0				):
          __snake_case				:			Optional[Any]            =    emission.size(0				)
          __snake_case				:			int            =    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.
          __snake_case				:			Dict            =    torch.full((num_frames + 1, num_tokens + 1)			,  -float("inf"				)				)
          __snake_case				:			Optional[int]            =    0
          for t in range(UpperCAmelCase				):
            __snake_case				:			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

        __snake_case				:			int            =    get_trellis(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)




        @dataclass
        class 			_lowerCamelCase	:



              """simple docstring"""






              UpperCAmelCase_  : int
              UpperCAmelCase_  : int
              UpperCAmelCase_  : float

        def backtrack(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase=0				):
          __snake_case				:			Optional[int]            =    trellis.size(1				) - 1
          __snake_case				:			Tuple            =    torch.argmax(trellis[:, j]				).item()

          __snake_case				:			Dict            =    []
          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.
            __snake_case				:			Dict            =    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				:			List[Any]            =    trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

            # 2. Store the path with frame-wise probability.
            __snake_case				:			Tuple            =    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]

        __snake_case				:			Optional[Any]            =    backtrack(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)




        @dataclass
        class 			_lowerCamelCase	:



              """simple docstring"""






              UpperCAmelCase_  : str
              UpperCAmelCase_  : int
              UpperCAmelCase_  : int
              UpperCAmelCase_  : float







              def __repr__(	self				)    ->				List[Any]:






                '''simple docstring'''






                return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""







              @property
              def   UpperCAmelCase						(	self				)    ->				Any:






                '''simple docstring'''






                return self.end - self.start

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

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







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Tuple:






        '''simple docstring'''






        if not os.path.exists(UpperCAmelCase				):
          os.makedirs(UpperCAmelCase				)

        # load text file
        __snake_case				:			List[str]            =    open(UpperCAmelCase			,  encoding="utf8"				).readlines()

        __snake_case				:			int            =    []
        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				:			str            =    line.strip().split("\t"				)
          __snake_case				:			Tuple            =    os.path.join(UpperCAmelCase			,  wav_name + ".wav"				)
          __snake_case				:			Tuple            =    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  lowerCAmelCase__(		)	->      Dict:
  __snake_case				:			Any            =    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_6000				,       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"			)

  __snake_case				:			int            =    parser.parse_args()

  __snake_case				:			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()








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


_UpperCamelCase    =	logging.get_logger(__name__)




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  *UpperCAmelCase			,  **UpperCAmelCase				)    ->				None:






        '''simple docstring'''






        warnings.warn(
            "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use MaskFormerImageProcessor instead."			,  UpperCAmelCase			,  )
        super().__init__(*UpperCAmelCase			,  **UpperCAmelCase				)

