





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,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    cuda
            _UpperCamelCase														=    AutoConfig.from_pretrained(A   )
            _UpperCamelCase														=    AutoModelForCTC.from_pretrained(A   )
            self.model.eval()
            if self.cuda:
                   self.model.to(device='''cuda'''   )
            _UpperCamelCase														=    AutoProcessor.from_pretrained(A   )
            _UpperCamelCase														=    torchaudio.transforms.Resample(A,							1_6000   )
            _UpperCamelCase														=    0
            _UpperCamelCase														=    list(self.processor.tokenizer.get_vocab().keys()   )
            for i in range(len(A   )   ):
                   if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                          _UpperCamelCase														=    i
            print('''Blank Token id [PAD]/<pad>''',							A   )
            _UpperCamelCase														=    blank_id

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    torchaudio.load(A   )
            _UpperCamelCase														=    self.resampler(A   ).squeeze().numpy()
            return speech

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

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

            _UpperCamelCase														=    self.speech_file_to_array_fn(item['''wav_path''']   )
            _UpperCamelCase														=    self.processor(A,							sampling_rate=1_6000,							return_tensors='''pt''',							padding=A   )
            if self.cuda:
                   _UpperCamelCase														=    inputs.to(device='''cuda'''   )

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

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

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

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

            def get_trellis(A,							A,							A=0   ):
                   _UpperCamelCase														=    emission.size(0   )
                   _UpperCamelCase														=    len(A   )

                   # 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														=    torch.full((num_frames + 1, num_tokens + 1),							-float('''inf'''   )   )
                   _UpperCamelCase														=    0
                   for t in range(A   ):
                          _UpperCamelCase														=    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														=    get_trellis(A,							A,							A   )

            @dataclass
            class 			__A:
                 __A							=							42
                 __A							=							42
                 __A							=							42

            def backtrack(A,							A,							A,							A=0   ):
                   _UpperCamelCase														=    trellis.size(1   ) - 1
                   _UpperCamelCase														=    torch.argmax(trellis[:, j]   ).item()

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

                          # 2. Store the path with frame-wise probability.
                          _UpperCamelCase														=    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,							A   )   )

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

            _UpperCamelCase														=    backtrack(A,							A,							A,							A   )

            @dataclass
            class 			__A:
                 __A							=							42
                 __A							=							42
                 __A							=							42
                 __A							=							42

                 def __repr__(   self   ):





                        """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   ):





                        """simple docstring"""

                        return self.end - self.start

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

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



     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """simple docstring"""

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

            # load text file
            _UpperCamelCase														=    open(A,							encoding='''utf8'''   ).readlines()

            _UpperCamelCase														=    []
            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														=    line.strip().split('''\t'''   )
                   _UpperCamelCase														=    os.path.join(A,							wav_name + '''.wav'''   )
                   _UpperCamelCase														=    os.path.join(A,							wav_name + '''.txt'''   )

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

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






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name'''     ,							type=_SCREAMING_SNAKE_CASE     ,							default='''arijitx/wav2vec2-xls-r-300m-bengali'''     ,							help='''wav2vec model name'''       )
       parser.add_argument('''--wav_dir'''     ,							type=_SCREAMING_SNAKE_CASE     ,							default='''./wavs'''     ,							help='''directory containing wavs'''       )
       parser.add_argument('''--text_file'''     ,							type=_SCREAMING_SNAKE_CASE     ,							default='''script.txt'''     ,							help='''file containing text'''       )
       parser.add_argument('''--input_wavs_sr'''     ,							type=_SCREAMING_SNAKE_CASE     ,							default=16_000     ,							help='''sampling rate of input audios'''       )
       parser.add_argument(
           '''--output_dir'''     ,							type=_SCREAMING_SNAKE_CASE     ,							default='''./out_alignment'''     ,							help='''output directory containing the alignment files'''       )
       parser.add_argument('''--cuda'''     ,							action='''store_true'''       )

       _UpperCamelCase														=    parser.parse_args()

       _UpperCamelCase														=    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'''







class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase=None   ):
								_SCREAMING_SNAKE_CASE					=			data
								_SCREAMING_SNAKE_CASE					=			None




		def __repr__(					self   ):
								_SCREAMING_SNAKE_CASE					=			""
								_SCREAMING_SNAKE_CASE					=			self
								while temp:
														string_rep += F'<{temp.data}> ---> '
														_SCREAMING_SNAKE_CASE					=			temp.next
								string_rep += "<END>"
								return string_rep



def       _a		(						_SCREAMING_SNAKE_CASE		: Any	):

						# if elements_list is empty
						if not elements_list:
												raise Exception("The Elements List is empty"	)

						# Set first element as Head
						_SCREAMING_SNAKE_CASE					=			Node(elements_list[0]	)
						_SCREAMING_SNAKE_CASE					=			head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_SCREAMING_SNAKE_CASE					=			Node(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			current.next
						return head


_snake_case					: List[Any]     			=					[1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
_snake_case					: List[Any]     			=					make_linked_list(list_data)
print("""Linked List:""")




print(linked_list)