



import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class       _snake_case				:






   '''simple docstring'''






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


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     Any							)	->      Union[str, Any]:
       UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					torchaudio.load(lowerCAmelCase_							)
       UpperCAmelCase_     :       int  		=					self.resampler(lowerCAmelCase_							).squeeze().numpy()
       return speech


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     List[str]							)	->      Dict:
       UpperCAmelCase_     :       List[str]  		=					self.blank_id
       UpperCAmelCase_     :       Optional[Any]  		=					"""|""".join(item["""sent"""].split(""" """							)							)
       if not os.path.isfile(item["""wav_path"""]							):
           print(item["""wav_path"""]  ,"""not found in wavs directory"""							)

       UpperCAmelCase_     :       Optional[int]  		=					self.speech_file_to_array_fn(item["""wav_path"""]							)
       UpperCAmelCase_     :       Union[str, Any]  		=					self.processor(lowerCAmelCase_  ,sampling_rate=16000  ,return_tensors="""pt"""  ,padding=lowerCAmelCase_							)
       if self.cuda:
           UpperCAmelCase_     :       int  		=					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_     :       int  		=					torch.log_softmax(lowerCAmelCase_  ,dim=-1							)
       UpperCAmelCase_     :       Any  		=					emissions[0].cpu().detach()

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

       UpperCAmelCase_     :       List[Any]  		=					{c: i for i, c in enumerate(lowerCAmelCase_							)}
       UpperCAmelCase_     :       Optional[int]  		=					[]
       for c in transcript:
           if c in dictionary:
               tokens.append(dictionary[c]							)

       def get_trellis(lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     str=0							):
           UpperCAmelCase_     :       List[str]  		=					emission.size(0							)
           UpperCAmelCase_     :       str  		=					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.
           UpperCAmelCase_     :       int  		=					torch.full((num_frames + 1, num_tokens + 1)  ,-float("""inf"""							)							)
           UpperCAmelCase_     :       Optional[Any]  		=					0
           for t in range(lowerCAmelCase_							):
               UpperCAmelCase_     :       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

       UpperCAmelCase_     :       Any  		=					get_trellis(lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_							)







       @dataclass
       class       _snake_case				:






          '''simple docstring'''






          A__      : int       =						42
          A__      : List[Any]       =						42
          A__      : Optional[int]       =						42

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

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

               # 2. Store the path with frame-wise probability.
               UpperCAmelCase_     :       Optional[int]  		=					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]

       UpperCAmelCase_     :       List[str]  		=					backtrack(lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_							)







       @dataclass
       class       _snake_case				:






          '''simple docstring'''






          A__      : Dict       =						42
          A__      : Any       =						42
          A__      : Any       =						42
          A__      : Dict       =						42


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






          @property
          def       A__    (    self:     List[Any]							)	->      str:
              return self.end - self.start

       def merge_repeats(lowerCamelCase_:     Dict							):
           UpperCAmelCase_		, UpperCAmelCase_     :       Optional[int]  		=					0, 0
           UpperCAmelCase_     :       Optional[Any]  		=					[]
           while ia < len(lowerCAmelCase_							):
               while ia < len(lowerCAmelCase_							) and path[ia].token_index == path[ia].token_index:
                   ia += 1
               UpperCAmelCase_     :       str  		=					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_  ,)							)
               UpperCAmelCase_     :       Union[str, Any]  		=					ia
           return segments

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






   def       A__    (    self:     Any  ,lowerCamelCase_:     int  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     Dict							)	->      Optional[Any]:
       if not os.path.exists(lowerCAmelCase_							):
           os.makedirs(lowerCAmelCase_							)

       # load text file
       UpperCAmelCase_     :       Optional[Any]  		=					open(lowerCAmelCase_  ,encoding="""utf8"""							).readlines()

       UpperCAmelCase_     :       List[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_     :       List[str]  		=					line.strip().split("""\t"""							)
           UpperCAmelCase_     :       List[Any]  		=					os.path.join(lowerCAmelCase_  ,wav_name + """.wav"""							)
           UpperCAmelCase_     :       Tuple  		=					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       lowerCamelCase_  (    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Dict  		=					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_6000   ,		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"""    )

    UpperCAmelCase_     :       Union[str, Any]  		=					parser.parse_args()

    UpperCAmelCase_     :       Optional[int]  		=					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()

def 	__lowerCamelCase							(				__a    : float		)      ->       float:
	return -num if num < 0 else num

def 	__lowerCamelCase							(				__a    : list[int]		)      ->       int:
	if len(__a		) == 0:
		raise ValueError("abs_min() arg is an empty sequence"		)
	_lowercase   =x[0]
	for i in x:
		if abs_val(__a		) < abs_val(__a		):
			_lowercase   =i
	return j

def 	__lowerCamelCase							(				__a    : list[int]		)      ->       int:
	if len(__a		) == 0:
		raise ValueError("abs_max() arg is an empty sequence"		)
	_lowercase   =x[0]
	for i in x:
		if abs(__a		) > abs(__a		):
			_lowercase   =i
	return j

def 	__lowerCamelCase							(				__a    : list[int]		)      ->       int:
	if len(__a		) == 0:
		raise ValueError("abs_max_sort() arg is an empty sequence"		)
	return sorted(__a      ,							key=__a		)[-1]

def 	__lowerCamelCase							(				)      ->       Optional[Any]:
	assert abs_val(0		) == 0
	assert abs_val(34		) == 34
	assert abs_val(-100_000_000_000		) == 100_000_000_000

	_lowercase   =[-3, -1, 2, -11]
	assert abs_max(__a		) == -11
	assert abs_max_sort(__a		) == -11
	assert abs_min(__a		) == -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						test_abs_val()
						print(abs_val(-3_4))  # --> 34




