





"""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:




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   List[str]:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						torchaudio.load(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.resampler(SCREAMING_SNAKE_CASE__							).squeeze().numpy()
             return speech




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						self.blank_id
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''|'''.join(item['''sent'''].split(''' '''							)							)
             if not os.path.isfile(item['''wav_path''']							):
                   print(item['''wav_path''']	,'''not found in wavs directory'''							)

             __SCREAMING_SNAKE_CASE     :List[str]	       =						self.speech_file_to_array_fn(item['''wav_path''']							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.processor(SCREAMING_SNAKE_CASE__	,sampling_rate=1_60_00	,return_tensors='''pt'''	,padding=SCREAMING_SNAKE_CASE__							)
             if self.cuda:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						inputs.to(device='''cuda'''							)

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

             # get the emission probability at frame level
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.log_softmax(SCREAMING_SNAKE_CASE__	,dim=-1							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						emissions[0].cpu().detach()

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

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

             def get_trellis(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=0							):
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						emission.size(0							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						len(SCREAMING_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.
                   __SCREAMING_SNAKE_CASE     :Any	       =						torch.full((num_frames + 1, num_tokens + 1)	,-float('''inf'''							)							)
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						0
                   for t in range(SCREAMING_SNAKE_CASE__							):
                         __SCREAMING_SNAKE_CASE     :Union[str, 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

             __SCREAMING_SNAKE_CASE     :Any	       =						get_trellis(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)






             @dataclass
             class       _SCREAMING_SNAKE_CASE:
                    SCREAMING_SNAKE_CASE_     :   int
                    SCREAMING_SNAKE_CASE_     :   int
                    SCREAMING_SNAKE_CASE_     :   float

             def backtrack(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=0							):
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						trellis.size(1							) - 1
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						torch.argmax(trellis[:, j]							).item()

                   __SCREAMING_SNAKE_CASE     :int	       =						[]
                   for t in range(SCREAMING_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.
                         __SCREAMING_SNAKE_CASE     :Any	       =						trellis[t - 1, j] + emission[t - 1, blank_id]
                         # Score for token changing from C-1 at T-1 to J at T.
                         __SCREAMING_SNAKE_CASE     :Any	       =						trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                         # 2. Store the path with frame-wise probability.
                         __SCREAMING_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	,SCREAMING_SNAKE_CASE__							)							)

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

             __SCREAMING_SNAKE_CASE     :str	       =						backtrack(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)






             @dataclass
             class       _SCREAMING_SNAKE_CASE:
                    SCREAMING_SNAKE_CASE_     :   str
                    SCREAMING_SNAKE_CASE_     :   int
                    SCREAMING_SNAKE_CASE_     :   int
                    SCREAMING_SNAKE_CASE_     :   float




                    def __repr__(		self							)    ->   Dict:





                          """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							)    ->   str:





                          """simple docstring"""






                          return self.end - self.start

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

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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Dict:





             """simple docstring"""






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

             # load text file
             __SCREAMING_SNAKE_CASE     :Any	       =						open(SCREAMING_SNAKE_CASE__	,encoding='''utf8'''							).readlines()

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

                   __SCREAMING_SNAKE_CASE     :Dict	       =						line.strip().split('''\t'''							)
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						os.path.join(SCREAMING_SNAKE_CASE__	,wav_name + '''.wav'''							)
                   __SCREAMING_SNAKE_CASE     :Any	       =						os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE__							)							)

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




def   __lowerCamelCase     (							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						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=1_60_00				,     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'''							)

      __SCREAMING_SNAKE_CASE     :Dict	       =						parser.parse_args()

      __SCREAMING_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()






"""simple docstring"""
def   __lowerCamelCase     (							a_  :	str							)    ->   int:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						0
      for plain_chr in plain_text:
            __SCREAMING_SNAKE_CASE     :List[str]	       =						(
                ord(a_							) + (hash_value << 6) + (hash_value << 16) - hash_value
            )
      return hash_value