





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase__      =logging.get_logger(__name__)


UpperCamelCase__      ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__      ={
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__      ={
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    VOCAB_FILES_NAMES
       __snake_case 			=    PRETRAINED_VOCAB_FILES_MAP
       __snake_case 			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __snake_case 			=    ['input_ids', 'attention_mask']
       __snake_case 			=    BartTokenizer




       def __init__(      self    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase=None    ,						__lowerCamelCase="replace"    ,						__lowerCamelCase="<s>"    ,						__lowerCamelCase="</s>"    ,						__lowerCamelCase="</s>"    ,						__lowerCamelCase="<s>"    ,						__lowerCamelCase="<unk>"    ,						__lowerCamelCase="<pad>"    ,						__lowerCamelCase="<mask>"    ,						__lowerCamelCase=False    ,						__lowerCamelCase=True    ,						**__lowerCamelCase    ,						)			->     Tuple:
            super().__init__(
                __lowerCamelCase    ,						__lowerCamelCase    ,						tokenizer_file=__lowerCamelCase    ,						errors=__lowerCamelCase    ,						bos_token=__lowerCamelCase    ,						eos_token=__lowerCamelCase    ,						sep_token=__lowerCamelCase    ,						cls_token=__lowerCamelCase    ,						unk_token=__lowerCamelCase    ,						pad_token=__lowerCamelCase    ,						mask_token=__lowerCamelCase    ,						add_prefix_space=__lowerCamelCase    ,						trim_offsets=__lowerCamelCase    ,						**__lowerCamelCase    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
            if pre_tok_state.get("add_prefix_space"    ,						__lowerCamelCase							) != add_prefix_space:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							getattr(__lowerCamelCase    ,						pre_tok_state.pop("type"							)							)
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							add_prefix_space
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							pre_tok_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	int						    =							add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            _SCREAMING_SNAKE_CASE       :	Any						    =							"post_processor"
            _SCREAMING_SNAKE_CASE       :	Any						    =							getattr(self.backend_tokenizer    ,						__lowerCamelCase    ,						__lowerCamelCase							)
            if tokenizer_component_instance:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							json.loads(tokenizer_component_instance.__getstate__()							)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                      _SCREAMING_SNAKE_CASE       :	List[str]						    =							tuple(state["sep"]							)
                 if "cls" in state:
                      _SCREAMING_SNAKE_CASE       :	List[Any]						    =							tuple(state["cls"]							)

                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							False

                 if state.get("add_prefix_space"    ,						__lowerCamelCase							) != add_prefix_space:
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							add_prefix_space
                      _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							True

                 if state.get("trim_offsets"    ,						__lowerCamelCase							) != trim_offsets:
                      _SCREAMING_SNAKE_CASE       :	Any						    =							trim_offsets
                      _SCREAMING_SNAKE_CASE       :	Any						    =							True

                 if changes_to_apply:
                      _SCREAMING_SNAKE_CASE       :	Any						    =							getattr(__lowerCamelCase    ,						state.pop("type"							)							)
                      _SCREAMING_SNAKE_CASE       :	str						    =							component_class(**__lowerCamelCase							)
                      setattr(self.backend_tokenizer    ,						__lowerCamelCase    ,						__lowerCamelCase							)




       @property
       def     UpperCamelCase_			(      self							)			->     str:
            if self._mask_token is None:
                 if self.verbose:
                      logger.error("Using mask_token, but it is not set yet."							)
                 return None
            return str(self._mask_token							)




       @mask_token.setter
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							AddedToken(__lowerCamelCase    ,						lstrip=__lowerCamelCase    ,						rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase    ,						__lowerCamelCase							) else value
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							value




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     BatchEncoding:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							kwargs.get("is_split_into_words"    ,						__lowerCamelCase							)

            if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                     "to use it with pretokenized inputs."							)

            return super()._batch_encode_plus(*__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						*__lowerCamelCase    ,						**__lowerCamelCase							)			->     BatchEncoding:
            _SCREAMING_SNAKE_CASE       :	str						    =							kwargs.get("is_split_into_words"    ,						__lowerCamelCase							)

            if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                     "to use it with pretokenized inputs."							)

            return super()._encode_plus(*__lowerCamelCase    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None							)			->     Tuple[str]:
            _SCREAMING_SNAKE_CASE       :	int						    =							self._tokenizer.model.save(__lowerCamelCase    ,						name=__lowerCamelCase							)
            return tuple(__lowerCamelCase							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=None							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase = None							)			->     List[int]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							[self.sep_token_id]
            _SCREAMING_SNAKE_CASE       :	Any						    =							[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]






from PIL import Image






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							image.size
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							image.load()
     for i in range(__lowerCamelCase	):
          for j in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__lowerCamelCase	):
          for i in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	int						    =							255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
       UpperCamelCase__      =mean_threshold(Image.open('path_to_image').convert('L'))
       image.save('output_image_path')