





"""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       ,__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       ,__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       ,__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"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowerCamelCase_          =	logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_          =	{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

lowerCamelCase_          =	{
    "RUCAIBox/mvp": 1_0_2_4,
}



@lru_cache()
def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(
          list(range(ord('''!'''							)				,     ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,     ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,     ord('''ÿ'''							) + 1							)							)
      )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						bs[:]
      __SCREAMING_SNAKE_CASE     :str	       =						0
      for b in range(2**8							):
            if b not in bs:
                  bs.append(a_							)
                  cs.append(2**8 + n							)
                  n += 1
      __SCREAMING_SNAKE_CASE     :int	       =						[chr(a_							) for n in cs]
      return dict(zip(a_				,     a_							)							)



def   __lowerCamelCase     (							a_  :	Dict							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						set()
      __SCREAMING_SNAKE_CASE     :Dict	       =						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            __SCREAMING_SNAKE_CASE     :List[str]	       =						char
      return pairs







class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   str					=						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_     :   Tuple					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_     :   List[Any]					=						['''input_ids''', '''attention_mask''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__="replace"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="<unk>"	,SCREAMING_SNAKE_CASE__="<pad>"	,SCREAMING_SNAKE_CASE__="<mask>"	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__	,)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else bos_token
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else eos_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else sep_token
             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else cls_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else unk_token
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else mask_token

             super().__init__(
                 errors=SCREAMING_SNAKE_CASE__	,bos_token=SCREAMING_SNAKE_CASE__	,eos_token=SCREAMING_SNAKE_CASE__	,unk_token=SCREAMING_SNAKE_CASE__	,sep_token=SCREAMING_SNAKE_CASE__	,cls_token=SCREAMING_SNAKE_CASE__	,pad_token=SCREAMING_SNAKE_CASE__	,mask_token=SCREAMING_SNAKE_CASE__	,add_prefix_space=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__	,)
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as vocab_handle:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						json.load(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{v: k for k, v in self.encoder.items()}
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						errors  # how to handle errors in decoding
             __SCREAMING_SNAKE_CASE     :Dict	       =						bytes_to_unicode()
             __SCREAMING_SNAKE_CASE     :Dict	       =						{v: k for k, v in self.byte_encoder.items()}
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as merges_handle:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						merges_handle.read().split('''\n'''							)[1:-1]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[tuple(merge.split()							) for merge in bpe_merges]
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						dict(zip(SCREAMING_SNAKE_CASE__	,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{}
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						add_prefix_space

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




       @property
       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return len(self.encoder							)




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             if token in self.cache:
                   return self.cache[token]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						tuple(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)

             if not pairs:
                   return token

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

                         if word[i] == first and i < len(SCREAMING_SNAKE_CASE__							) - 1 and word[i + 1] == second:
                               new_word.append(first + second							)
                               i += 2
                         else:
                               new_word.append(word[i]							)
                               i += 1
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						tuple(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						new_word
                   if len(SCREAMING_SNAKE_CASE__							) == 1:
                         break
                   else:
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						get_pairs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''' '''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :int	       =						word
             return word




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						[]
             for token in re.findall(self.pat	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						''''''.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(SCREAMING_SNAKE_CASE__							).split(''' '''							)							)
             return bpe_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return self.decoder.get(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''''''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''	,errors=self.errors							)
             return text




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   Tuple[str]:





             """simple docstring"""






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

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

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						0
             with open(SCREAMING_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 SCREAMING_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!'''							)
                               __SCREAMING_SNAKE_CASE     :int	       =						token_index
                         writer.write(''' '''.join(SCREAMING_SNAKE_CASE__							) + '''\n'''							)
                         index += 1

             return vocab_file, merge_file




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[self.cls_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False							)    ->   List[int]:





             """simple docstring"""






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

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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[Any]	       =						[self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :str	       =						[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 + sep + token_ids_a + sep							) * [0]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						kwargs.pop('''add_prefix_space'''	,self.add_prefix_space							)
             if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__							) > 0 and not text[0].isspace()):
                   __SCREAMING_SNAKE_CASE     :Any	       =						''' ''' + text
             return (text, kwargs)