






"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
   from .tokenization_deberta_va import DebertaVaTokenizer
else:
   _UpperCamelCase   :						Optional[int]     						= None

_UpperCamelCase   :						Any     						= logging.get_logger(__name__)

_UpperCamelCase   :						List[str]     						= {'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

_UpperCamelCase   :						str     						= {
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

_UpperCamelCase   :						List[str]     						= {
    '''microsoft/deberta-v2-xlarge''': 5_12,
    '''microsoft/deberta-v2-xxlarge''': 5_12,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_12,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_12,
}

_UpperCamelCase   :						Optional[int]     						= {
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}






class __a   (   A__				):
  __lowercase    :    str						=					VOCAB_FILES_NAMES
  __lowercase    :    Optional[Any]						=					PRETRAINED_VOCAB_FILES_MAP
  __lowercase    :    List[Any]						=					PRETRAINED_INIT_CONFIGURATION
  __lowercase    :    Optional[Any]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase    :    Tuple						=					DebertaVaTokenizer



  def __init__(self  :		List[str]				,			lowercase_  :		List[str]=None				,			lowercase_  :		Any=None				,			lowercase_  :		Tuple=False				,			lowercase_  :		Any=False				,			lowercase_  :		List[str]="[CLS]"				,			lowercase_  :		List[str]="[SEP]"				,			lowercase_  :		Tuple="[UNK]"				,			lowercase_  :		int="[SEP]"				,			lowercase_  :		Tuple="[PAD]"				,			lowercase_  :		Optional[Any]="[CLS]"				,			lowercase_  :		Optional[Any]="[MASK]"				,			**lowercase_  :		Tuple				,			)		->							Optional[int]:
   super().__init__(
       lowercase_				,			tokenizer_file=lowercase_				,			do_lower_case=lowercase_				,			bos_token=lowercase_				,			eos_token=lowercase_				,			unk_token=lowercase_				,			sep_token=lowercase_				,			pad_token=lowercase_				,			cls_token=lowercase_				,			mask_token=lowercase_				,			split_by_punct=lowercase_				,			**lowercase_				,			)

   __A	:       Union[str, Any]       =	do_lower_case
   __A	:       Optional[int]       =	split_by_punct
   __A	:       Optional[Any]       =	vocab_file
   __A	:       List[Any]       =	False if not self.vocab_file else True



  def 						A_				(self  :		List[str]				,			lowercase_  :		Optional[int]				,			lowercase_  :		Tuple=None   )		->							List[Any]:






   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   __A	:       Tuple       =	[self.cls_token_id]
   __A	:       Any       =	[self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		Optional[int]				,			lowercase_  :		str=None				,			lowercase_  :		Tuple=False   )		->							Optional[Any]:






   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=lowercase_				,			token_ids_a=lowercase_				,			already_has_special_tokens=lowercase_   )

   if token_ids_a is not None:
    return [1] + ([0] * len(lowercase_   )) + [1] + ([0] * len(lowercase_   )) + [1]
   return [1] + ([0] * len(lowercase_   )) + [1]



  def 						A_				(self  :		int				,			lowercase_  :		int				,			lowercase_  :		Dict=None   )		->							Tuple:
   __A	:       Tuple       =	[self.sep_token_id]
   __A	:       Optional[int]       =	[self.cls_token_id]
   if token_ids_a is None:
    return len(cls + token_ids_a + sep   ) * [0]
   return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		str				,			lowercase_  :		Optional[str] = None   )		->							List[Any]:






   if not self.can_save_slow_tokenizer:
    raise ValueError(
        'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
        'tokenizer.'   )

   if not os.path.isdir(lowercase_   ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
    return
   __A	:       Union[str, Any]       =	os.path.join(
       lowercase_				,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

   if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase_   ):
    copyfile(self.vocab_file				,			lowercase_   )

   return (out_vocab_file,)













"""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 __a   :



  def __init__(self  :		Dict				,			lowercase_  :		str				,			lowercase_  :		Optional[Any]				,			lowercase_  :		Tuple   )		->							int:
   __A	:       Union[str, Any]       =	cuda
   __A	:       List[Any]       =	AutoConfig.from_pretrained(lowercase_   )
   __A	:       Any       =	AutoModelForCTC.from_pretrained(lowercase_   )
   self.model.eval()
   if self.cuda:
    self.model.to(device='cuda'   )
   __A	:       Union[str, Any]       =	AutoProcessor.from_pretrained(lowercase_   )
   __A	:       List[str]       =	torchaudio.transforms.Resample(lowercase_				,			16_000   )
   __A	:       Dict       =	0
   __A	:       Optional[int]       =	list(self.processor.tokenizer.get_vocab().keys()   )
   for i in range(len(lowercase_   )   ):
    if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
     __A	:       List[str]       =	i
   print('Blank Token id [PAD]/<pad>'				,			lowercase_   )
   __A	:       List[str]       =	blank_id



  def 						A_				(self  :		List[str]				,			lowercase_  :		Union[str, Any]   )		->							Optional[int]:
   __A    ,					__A	:       Tuple       =	torchaudio.load(lowercase_   )
   __A	:       int       =	self.resampler(lowercase_   ).squeeze().numpy()
   return speech



  def 						A_				(self  :		Any				,			lowercase_  :		Any   )		->							List[Any]:
   __A	:       int       =	self.blank_id
   __A	:       List[str]       =	'|'.join(item['sent'].split(' '   )   )
   if not os.path.isfile(item['wav_path']   ):
    print(item['wav_path']				,			'not found in wavs directory'   )

   __A	:       List[Any]       =	self.speech_file_to_array_fn(item['wav_path']   )
   __A	:       Any       =	self.processor(lowercase_				,			sampling_rate=16_000				,			return_tensors='pt'				,			padding=lowercase_   )
   if self.cuda:
    __A	:       int       =	inputs.to(device='cuda'   )

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

   # get the emission probability at frame level
   __A	:       Any       =	torch.log_softmax(lowercase_				,			dim=-1   )
   __A	:       Tuple       =	emissions[0].cpu().detach()

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

   __A	:       int       =	{c: i for i, c in enumerate(lowercase_   )}
   __A	:       str       =	[]
   for c in transcript:
    if c in dictionary:
     tokens.append(dictionary[c]   )

   def get_trellis(lowercase_  :		int				,			lowercase_  :		List[Any]				,			lowercase_  :		str=0   ):
    __A	:       List[str]       =	emission.size(0   )
    __A	:       List[Any]       =	len(lowercase_   )

    # 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.
    __A	:       int       =	torch.full((num_frames + 1, num_tokens + 1)				,			-float('inf'   )   )
    __A	:       str       =	0
    for t in range(lowercase_   ):
     __A	:       List[str]       =	torch.maximum(
         # Score for staying at the same token
         trellis[t, 1:] + emission[t, blank_id]				,			trellis[t, :-1] + emission[t, tokens]				,			)
    return trellis

   __A	:       Dict       =	get_trellis(lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float

   def backtrack(lowercase_  :		Any				,			lowercase_  :		str				,			lowercase_  :		Dict				,			lowercase_  :		int=0   ):
    __A	:       str       =	trellis.size(1   ) - 1
    __A	:       int       =	torch.argmax(trellis[:, j]   ).item()

    __A	:       int       =	[]
    for t in range(lowercase_				,			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.
     __A	:       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.
     __A	:       Tuple       =	trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

     # 2. Store the path with frame-wise probability.
     __A	:       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				,			lowercase_   )   )

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

   __A	:       Any       =	backtrack(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    str
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float



     def __repr__(self  :		Optional[Any]   )		->							Union[str, Any]:
      return f"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"



     @property
     def 						A_				(self  :		int   )		->							Dict:
      return self.end - self.start

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

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



  def 						A_				(self  :		int				,			lowercase_  :		Optional[int]				,			lowercase_  :		List[str]				,			lowercase_  :		str   )		->							Optional[Any]:
   if not os.path.exists(lowercase_   ):
    os.makedirs(lowercase_   )

   # load text file
   __A	:       Optional[int]       =	open(lowercase_				,			encoding='utf8'   ).readlines()

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

    __A    ,					__A	:       Union[str, Any]       =	line.strip().split('\t'   )
    __A	:       List[Any]       =	os.path.join(lowercase_				,			wav_name + '.wav'   )
    __A	:       Optional[Any]       =	os.path.join(lowercase_				,			wav_name + '.txt'   )

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

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






def 				a (     )  ->		Any:
 __A	:       Any       =	argparse.ArgumentParser()

 parser.add_argument(
     '--model_name'     ,	type=UpperCamelCase_     ,	default='arijitx/wav2vec2-xls-r-300m-bengali'     ,	help='wav2vec model name'		)
 parser.add_argument('--wav_dir'     ,	type=UpperCamelCase_     ,	default='./wavs'     ,	help='directory containing wavs'		)
 parser.add_argument('--text_file'     ,	type=UpperCamelCase_     ,	default='script.txt'     ,	help='file containing text'		)
 parser.add_argument('--input_wavs_sr'     ,	type=UpperCamelCase_     ,	default=1_6_0_0_0     ,	help='sampling rate of input audios'		)
 parser.add_argument(
     '--output_dir'     ,	type=UpperCamelCase_     ,	default='./out_alignment'     ,	help='output directory containing the alignment files'		)
 parser.add_argument('--cuda'     ,	action='store_true'		)

 __A	:       Tuple       =	parser.parse_args()

 __A	:       Optional[Any]       =	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()





