'''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      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      torchaudio.load(snake_case_						)
       UpperCAmelCase_							:   Optional[int]   =      self.resampler(snake_case_						).squeeze().numpy()
       return speech


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.blank_id
       UpperCAmelCase_							:   List[Any]   =      '|'.join(item['sent'].split(' '						)						)
       if not os.path.isfile(item['wav_path']						):
            print(item['wav_path']      ,		'not found in wavs directory'						)

       UpperCAmelCase_							:   Tuple   =      self.speech_file_to_array_fn(item['wav_path']						)
       UpperCAmelCase_							:   Optional[int]   =      self.processor(snake_case_      ,		sampling_rate=1_6_0_0_0      ,		return_tensors='pt'      ,		padding=snake_case_						)
       if self.cuda:
            UpperCAmelCase_							:   Dict   =      inputs.to(device='cuda'						)

       with torch.no_grad():
            UpperCAmelCase_							:   Union[str, Any]   =      self.model(inputs.input_values						).logits

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

       # get labels from vocab
       UpperCAmelCase_							:   Dict   =      ([''] + 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(snake_case_						)}
       UpperCAmelCase_							:   Any   =      []
       for c in transcript:
            if c in dictionary:
                 tokens.append(dictionary[c]						)

       def get_trellis(snake_case_      ,		snake_case_      ,		snake_case_=0						):
            UpperCAmelCase_							:   List[Any]   =      emission.size(0						)
            UpperCAmelCase_							:   Optional[Any]   =      len(snake_case_						)

            # 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[str]   =      torch.full((num_frames + 1, num_tokens + 1)      ,		-float('inf'						)						)
            UpperCAmelCase_							:   int   =      0
            for t in range(snake_case_						):
                 UpperCAmelCase_							:   Optional[int]   =      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_							:   int   =      get_trellis(snake_case_      ,		snake_case_      ,		snake_case_						)






       @dataclass
       class      __SCREAMING_SNAKE_CASE						:







         '''simple docstring'''






         lowerCamelCase_		:int
         lowerCamelCase_		:int
         lowerCamelCase_		:float

       def backtrack(snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_=0						):
            UpperCAmelCase_							:   int   =      trellis.size(1						) - 1
            UpperCAmelCase_							:   List[str]   =      torch.argmax(trellis[:, j]						).item()

            UpperCAmelCase_							:   List[str]   =      []
            for t in range(snake_case_      ,		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_							:   str   =      trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                 # 2. Store the path with frame-wise probability.
                 UpperCAmelCase_							:   List[Any]   =      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      ,		snake_case_						)						)

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

       UpperCAmelCase_							:   Tuple   =      backtrack(snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						)






       @dataclass
       class      __SCREAMING_SNAKE_CASE						:







         '''simple docstring'''






         lowerCamelCase_		:str
         lowerCamelCase_		:int
         lowerCamelCase_		:int
         lowerCamelCase_		:float


         def __repr__( self						):




              '''simple docstring'''

              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						):




              '''simple docstring'''

              return self.end - self.start

       def merge_repeats(snake_case_						):
            UpperCAmelCase_   ,			UpperCAmelCase_							:   List[str]   =      0, 0
            UpperCAmelCase_							:   Optional[int]   =      []
            while ia < len(snake_case_						):
                 while ia < len(snake_case_						) and path[ia].token_index == path[ia].token_index:
                      ia += 1
                 UpperCAmelCase_							:   List[str]   =      sum(path[k].score for k in range(snake_case_      ,		snake_case_						)						) / (ia - ia)
                 segments.append(
                     Segment(
                         transcript[path[ia].token_index]      ,		path[ia].time_index      ,		path[ia - 1].time_index + 1      ,		snake_case_      ,		)						)
                 UpperCAmelCase_							:   Dict   =      ia
            return segments

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






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

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

       # load text file
       UpperCAmelCase_							:   int   =      open(snake_case_      ,		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_							:   Optional[int]   =      line.strip().split('\t'						)
            UpperCAmelCase_							:   Union[str, Any]   =      os.path.join(snake_case_      ,		wav_name + '.wav'						)
            UpperCAmelCase_							:   str   =      os.path.join(snake_case_      ,		wav_name + '.txt'						)

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

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







def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[str]   =      argparse.ArgumentParser()

     parser.add_argument(
         '--model_name'	,     type=lowerCamelCase_	,     default='arijitx/wav2vec2-xls-r-300m-bengali'	,     help='wav2vec model name'   )
     parser.add_argument('--wav_dir'	,     type=lowerCamelCase_	,     default='./wavs'	,     help='directory containing wavs'   )
     parser.add_argument('--text_file'	,     type=lowerCamelCase_	,     default='script.txt'	,     help='file containing text'   )
     parser.add_argument('--input_wavs_sr'	,     type=lowerCamelCase_	,     default=16000	,     help='sampling rate of input audios'   )
     parser.add_argument(
         '--output_dir'	,     type=lowerCamelCase_	,     default='./out_alignment'	,     help='output directory containing the alignment files'   )
     parser.add_argument('--cuda'	,     action='store_true'   )

     UpperCAmelCase_							:   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__     :			Optional[Any]      			=  {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Tuple      			=  [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 snake_case__     :			int      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






