"""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		:      Union[str, Any]					,   _snake_case		:      int					,   _snake_case		:      List[Any]					,   _snake_case		:      Union[str, Any]				)				->			int:




    '''simple docstring'''






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


 def        _lowerCAmelCase				(   self		:      Union[str, Any]					,   _snake_case		:      Union[str, Any]				)				->			int:




    '''simple docstring'''






    a__				,       a__     				=		torchaudio.load(_snake_case				)
    a__     				=		self.resampler(_snake_case				).squeeze().numpy()
    return speech


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      Optional[Any]				)				->			Optional[Any]:




    '''simple docstring'''






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

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

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

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

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

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

    def get_trellis(_snake_case		:      List[Any]					,   _snake_case		:      int					,   _snake_case		:      List[Any]=0				):
       a__     				=		emission.size(0				)
       a__     				=		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.
       a__     				=		torch.full((num_frames + 1, num_tokens + 1)					,   -float('inf'				)				)
       a__     				=		0
       for t in range(_snake_case				):
          a__     				=		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__     				=		get_trellis(_snake_case					,   _snake_case					,   _snake_case				)

    @dataclass
    class SCREAMING_SNAKE_CASE     :







     """simple docstring"""


     a_       :	int
     a_       :	int
     a_       :	float

    def backtrack(_snake_case		:      Optional[Any]					,   _snake_case		:      List[str]					,   _snake_case		:      str					,   _snake_case		:      str=0				):
       a__     				=		trellis.size(1				) - 1
       a__     				=		torch.argmax(trellis[:, j]				).item()

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

          # 2. Store the path with frame-wise probability.
          a__     				=		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]

    a__     				=		backtrack(_snake_case					,   _snake_case					,   _snake_case					,   _snake_case				)

    @dataclass
    class SCREAMING_SNAKE_CASE     :







     """simple docstring"""


     a_       :	str
     a_       :	int
     a_       :	int
     a_       :	float


     def __repr__(   self		:      Union[str, Any]				)				->			int:




        '''simple docstring'''






        return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''



     @property
     def        _lowerCAmelCase				(   self		:      Tuple				)				->			Tuple:




        '''simple docstring'''






        return self.end - self.start

    def merge_repeats(_snake_case		:      List[str]				):
       a__				,       a__     				=		0, 0
       a__     				=		[]
       while ia < len(_snake_case				):
          while ia < len(_snake_case				) and path[ia].token_index == path[ia].token_index:
             ia += 1
          a__     				=		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					,   )				)
          a__     				=		ia
       return segments

    a__     				=		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        _lowerCAmelCase				(   self		:      Dict					,   _snake_case		:      Optional[Any]					,   _snake_case		:      str					,   _snake_case		:      List[Any]				)				->			int:




    '''simple docstring'''






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

    # load text file
    a__     				=		open(_snake_case					,   encoding='utf8'				).readlines()

    a__     				=		[]
    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__     				=		line.strip().split('\t'				)
       a__     				=		os.path.join(_snake_case					,   wav_name + '.wav'				)
       a__     				=		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 ( )							->     Any:


   '''simple docstring'''


   a__     				=		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_60_00,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__     				=		parser.parse_args()

   a__     				=		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"""



import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

__magic_name__    				=					logging.get_logger(__name__)

__magic_name__    				=					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__magic_name__    				=					{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

__magic_name__    				=					{
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





@lru_cache()
def   _lowerCamelCase ( )							->     int:


   '''simple docstring'''


   a__     				=		(
       list(range(ord('!'					),ord('~'					) + 1					)					) + list(range(ord('¡'					),ord('¬'					) + 1					)					) + list(range(ord('®'					),ord('ÿ'					) + 1					)					)
   )
   a__     				=		bs[:]
   a__     				=		0
   for b in range(2**8					):
      if b not in bs:
         bs.append(UpperCAmelCase__					)
         cs.append(2**8 + n					)
         n += 1
   a__     				=		[chr(UpperCAmelCase__					) for n in cs]
   return dict(zip(UpperCAmelCase__,UpperCAmelCase__					)					)





def   _lowerCamelCase ( UpperCAmelCase__					)							->     List[str]:


   '''simple docstring'''


   a__     				=		set()
   a__     				=		word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)					)
      a__     				=		char
   return pairs




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Union[str, Any]  			=VOCAB_FILES_NAMES
 a_       :	Optional[Any]  			=PRETRAINED_VOCAB_FILES_MAP
 a_       :	Dict  			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a_       :	str  			=["input_ids", "attention_mask"]


 def __init__(   self		:      Optional[int]					,   _snake_case		:      List[str]					,   _snake_case		:      Any					,   _snake_case		:      Union[str, Any]="replace"					,   _snake_case		:      Tuple="<|endoftext|>"					,   _snake_case		:      Optional[int]="<|endoftext|>"					,   _snake_case		:      Any="<|endoftext|>"					,   _snake_case		:      Dict=None					,   _snake_case		:      List[str]=False					,   _snake_case		:      Optional[int]=False					,   **_snake_case		:      Any					,   )				->			Optional[int]:




    '''simple docstring'''






    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else bos_token
    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else eos_token
    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else unk_token
    a__     				=		AddedToken(_snake_case					,   lstrip=_snake_case					,   rstrip=_snake_case				) if isinstance(_snake_case					,   _snake_case				) else pad_token
    super().__init__(
        errors=_snake_case					,   unk_token=_snake_case					,   bos_token=_snake_case					,   eos_token=_snake_case					,   pad_token=_snake_case					,   add_prefix_space=_snake_case					,   add_bos_token=_snake_case					,   **_snake_case					,   )
    a__     				=		add_bos_token

    with open(_snake_case					,   encoding='utf-8'				) as vocab_handle:
       a__     				=		json.load(_snake_case				)
    a__     				=		{v: k for k, v in self.encoder.items()}
    a__     				=		errors  # how to handle errors in decoding
    a__     				=		bytes_to_unicode()
    a__     				=		{v: k for k, v in self.byte_encoder.items()}
    with open(_snake_case					,   encoding='utf-8'				) as merges_handle:
       a__     				=		merges_handle.read().split('\n'				)[1:-1]
    a__     				=		[tuple(merge.split()				) for merge in bpe_merges]
    a__     				=		dict(zip(_snake_case					,   range(len(_snake_case				)				)				)				)
    a__     				=		{}
    a__     				=		add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    a__     				=		re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'				)


 @property
 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    return len(self.encoder				)


 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Any:




    '''simple docstring'''






    return dict(self.encoder					,   **self.added_tokens_encoder				)


 def        _lowerCAmelCase				(   self		:      str					,   _snake_case		:      Union[str, Any]				)				->			str:




    '''simple docstring'''






    if token in self.cache:
       return self.cache[token]
    a__     				=		tuple(_snake_case				)
    a__     				=		get_pairs(_snake_case				)

    if not pairs:
       return token

    while True:
       a__     				=		min(_snake_case					,   key=lambda _snake_case				: self.bpe_ranks.get(_snake_case					,   float('inf'				)				)				)
       if bigram not in self.bpe_ranks:
          break
       a__				,       a__     				=		bigram
       a__     				=		[]
       a__     				=		0
       while i < len(_snake_case				):
          try:
             a__     				=		word.index(_snake_case					,   _snake_case				)
          except ValueError:
             new_word.extend(word[i:]				)
             break
          else:
             new_word.extend(word[i:j]				)
             a__     				=		j

          if word[i] == first and i < len(_snake_case				) - 1 and word[i + 1] == second:
             new_word.append(first + second				)
             i += 2
          else:
             new_word.append(word[i]				)
             i += 1
       a__     				=		tuple(_snake_case				)
       a__     				=		new_word
       if len(_snake_case				) == 1:
          break
       else:
          a__     				=		get_pairs(_snake_case				)
    a__     				=		' '.join(_snake_case				)
    a__     				=		word
    return word


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      Dict					,   _snake_case		:      List[Any]=None				)				->			List[Any]:




    '''simple docstring'''






    if self.add_bos_token:
       a__     				=		[self.bos_token_id]
    else:
       a__     				=		[]

    a__     				=		bos_token_ids + token_ids_a

    if token_ids_a is None:
       return output

    return output + bos_token_ids + token_ids_a


 def        _lowerCAmelCase				(   self		:      Any					,   _snake_case		:      List[int]					,   _snake_case		:      Optional[List[int]] = None					,   _snake_case		:      bool = False				)				->			List[int]:




    '''simple docstring'''






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

    if not self.add_bos_token:
       return super().get_special_tokens_mask(
           token_ids_a=_snake_case					,   token_ids_a=_snake_case					,   already_has_special_tokens=_snake_case				)

    if token_ids_a is None:
       return [1] + ([0] * len(_snake_case				))
    return [1] + ([0] * len(_snake_case				)) + [1] + ([0] * len(_snake_case				))


 def        _lowerCAmelCase				(   self		:      Dict					,   _snake_case		:      Optional[Any]				)				->			Tuple:




    '''simple docstring'''






    a__     				=		[]
    for token in re.findall(self.pat					,   _snake_case				):
       a__     				=		''.join(
           self.byte_encoder[b] for b in token.encode('utf-8'				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case				).split(' '				)				)
    return bpe_tokens


 def        _lowerCAmelCase				(   self		:      Optional[Any]					,   _snake_case		:      Tuple				)				->			int:




    '''simple docstring'''






    return self.encoder.get(_snake_case					,   self.encoder.get(self.unk_token				)				)


 def        _lowerCAmelCase				(   self		:      List[Any]					,   _snake_case		:      int				)				->			List[str]:




    '''simple docstring'''






    return self.decoder.get(_snake_case				)


 def        _lowerCAmelCase				(   self		:      Tuple					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






    a__     				=		''.join(_snake_case				)
    a__     				=		bytearray([self.byte_decoder[c] for c in text]				).decode('utf-8'					,   errors=self.errors				)
    return text


 def        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      str					,   _snake_case		:      Optional[str] = None				)				->			Tuple[str]:




    '''simple docstring'''






    if not os.path.isdir(_snake_case				):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
       return
    a__     				=		os.path.join(
        _snake_case					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
    a__     				=		os.path.join(
        _snake_case					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)

    with open(_snake_case					,   'w'					,   encoding='utf-8'				) as f:
       f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=_snake_case					,   ensure_ascii=_snake_case				) + '\n'				)

    a__     				=		0
    with open(_snake_case					,   'w'					,   encoding='utf-8'				) as writer:
       writer.write('#version: 0.2\n'				)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda _snake_case				: kv[1]				):
          if index != token_index:
             logger.warning(
                 F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                 ' Please check that the tokenizer is not corrupted!'				)
             a__     				=		token_index
          writer.write(' '.join(_snake_case				) + '\n'				)
          index += 1

    return vocab_file, merge_file


 def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      Union[str, Any]					,   _snake_case		:      List[Any]=False					,   **_snake_case		:      Optional[int]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		kwargs.pop('add_prefix_space'					,   self.add_prefix_space				)
    if is_split_into_words or add_prefix_space:
       a__     				=		' ' + text
    return (text, kwargs)



 def        _lowerCAmelCase				(   self		:      Dict					,   _snake_case		:      "Conversation"				)				->			List[int]:




    '''simple docstring'''






    a__     				=		[]
    for is_user, text in conversation.iter_texts():
       input_ids.extend(self.encode(_snake_case					,   add_special_tokens=_snake_case				) + [self.eos_token_id]				)
    if len(_snake_case				) > self.model_max_length:
       a__     				=		input_ids[-self.model_max_length :]
    return input_ids


