code: 
  
  
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
        from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
        SCREAMING_SNAKE_CASE__		:       List[str]   							=							None
  
  
  SCREAMING_SNAKE_CASE__		:       str   							=							logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__		:       Dict   							=							{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
  
  SCREAMING_SNAKE_CASE__		:       List[str]   							=							{
      """vocab_file""": {
          """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
          """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
          """xlm-roberta-large-finetuned-conll02-dutch""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
          ),
          """xlm-roberta-large-finetuned-conll02-spanish""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
          ),
          """xlm-roberta-large-finetuned-conll03-english""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
          ),
          """xlm-roberta-large-finetuned-conll03-german""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
          ),
      },
      """tokenizer_file""": {
          """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
          """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
          """xlm-roberta-large-finetuned-conll02-dutch""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
          ),
          """xlm-roberta-large-finetuned-conll02-spanish""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
          ),
          """xlm-roberta-large-finetuned-conll03-english""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
          ),
          """xlm-roberta-large-finetuned-conll03-german""": (
              """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  SCREAMING_SNAKE_CASE__		:       int   							=							{
      """xlm-roberta-base""": 5_12,
      """xlm-roberta-large""": 5_12,
      """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
      """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
      """xlm-roberta-large-finetuned-conll03-english""": 5_12,
      """xlm-roberta-large-finetuned-conll03-german""": 5_12,
  }
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		lowerCAmelCase__		):
       __lowerCamelCase										=     VOCAB_FILES_NAMES
       __lowerCamelCase										=     PRETRAINED_VOCAB_FILES_MAP
       __lowerCamelCase										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCamelCase										=     ['input_ids', 'attention_mask']
       __lowerCamelCase										=     XLMRobertaTokenizer
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase="<s>"  ,	_lowerCAmelCase="</s>"  ,	_lowerCAmelCase="</s>"  ,	_lowerCAmelCase="<s>"  ,	_lowerCAmelCase="<unk>"  ,	_lowerCAmelCase="<pad>"  ,	_lowerCAmelCase="<mask>"  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     Any										= AddedToken(_lowerCamelCase  ,	lstrip=_lowerCamelCase  ,	rstrip=_lowerCamelCase		) if isinstance(_lowerCamelCase  ,	_lowerCamelCase		) else mask_token
  
            super().__init__(
                _lowerCamelCase  ,	tokenizer_file=_lowerCamelCase  ,	bos_token=_lowerCamelCase  ,	eos_token=_lowerCamelCase  ,	sep_token=_lowerCamelCase  ,	cls_token=_lowerCamelCase  ,	unk_token=_lowerCamelCase  ,	pad_token=_lowerCamelCase  ,	mask_token=_lowerCamelCase  ,	**_lowerCamelCase  ,	)
  
            UpperCAmelCase__			:     Any										= vocab_file
            UpperCAmelCase__			:     Union[str, Any]										= False if not self.vocab_file else True
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
  
            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            UpperCAmelCase__			:     Dict										= [self.cls_token_id]
            UpperCAmelCase__			:     Tuple										= [self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
            UpperCAmelCase__			:     Tuple										= [self.sep_token_id]
            UpperCAmelCase__			:     Dict										= [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  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
  
            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(_lowerCamelCase		):
                 logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
                 return
            UpperCAmelCase__			:     str										= os.path.join(
                _lowerCamelCase  ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
  
            if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		):
                 copyfile(self.vocab_file  ,	_lowerCamelCase		)
  
            return (out_vocab_file,)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import math
  import os
  
  import cva
  import numpy as np
  
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
       if mse == 0:
            return 100
  
       return 20 * math.logaa(PIXEL_MAX / math.sqrt(__lowerCamelCase					)					)
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> None:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
       # Loading images (original image and compressed image)
       UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
       UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)
  
       UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
       UpperCAmelCase__			:     int										= cva.imread(
           os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-comp-10.jpg"""					)   ,       1					)
  
       # Value expected: 29.73dB
       print("""-- First Test --"""					)
       print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)
  
       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""					)
       print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  