






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,)














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()






