

from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


snake_case_    						=				logging.get_logger(__name__)

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

snake_case_    						=				{
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
    '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
}

snake_case_    						=				{
    '''openai-gpt''': 512,
}


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):

    '''simple docstring'''





    __lowerCamelCase		: int														=  VOCAB_FILES_NAMES
    __lowerCamelCase		: List[str]														=  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase		: Union[str, Any]														=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase		: Any														=  ["""input_ids""", """attention_mask"""]
    __lowerCamelCase		: List[str]														=  OpenAIGPTTokenizer





    def __init__(   self		,     a=None		,     a=None		,     a=None		,     a="<unk>"		,     **a):
         super().__init__(a		,     a		,     tokenizer_file=a		,     unk_token=a		,     **a)





    @property
    def    snake_case_       (   self):
         return True





    def    snake_case_       (   self		,     a		,     a = None):
         lowercase__					:     Optional[int]   = self._tokenizer.model.save(a		,     name=a)
         return tuple(a)




from cva import destroyAllWindows, imread, imshow, waitKey

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]

     def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
          return min(SCREAMING_SNAKE_CASE_			,  255    )

     for i in range(SCREAMING_SNAKE_CASE_    ):
          for j in range(SCREAMING_SNAKE_CASE_    ):
               lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
               lowercase__					:     Dict   = [
                   normalize(SCREAMING_SNAKE_CASE_    ),
                   normalize(greyscale + factor    ),
                   normalize(greyscale + 2 * factor    ),
               ]

     return img


if __name__ == "__main__":
       # read original image
       snake_case_    						=				{
           percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'''Original image with sepia (factor: {percentage})''', img)

       waitKey(0)
       destroyAllWindows()

