


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


_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/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

_snake_case   = {
    """openai/clip-vit-base-patch32""": 7_7,
}




class    A_   ( a_     ):







	"""simple docstring"""





	_UpperCamelCase				: List[Any]      =					VOCAB_FILES_NAMES
	_UpperCamelCase				: str      =					PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase				: List[Any]      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCamelCase				: Tuple      =					['input_ids', 'attention_mask']
	_UpperCamelCase				: List[str]      =					CLIPTokenizer
	def __init__(			self					: Dict       ,    _a					: Dict=None       ,    _a					: str=None       ,    _a					: Tuple=None       ,    _a					: Tuple="<|endoftext|>"       ,    _a					: Any="<|startoftext|>"       ,    _a					: str="<|endoftext|>"       ,    _a					: Optional[Any]="<|endoftext|>"       ,    **_a					: Dict       ,    )    ->			Union[str, Any]:
						super().__init__(
						    _snake_case       ,    _snake_case       ,    tokenizer_file=_snake_case       ,    unk_token=_snake_case       ,    bos_token=_snake_case       ,    eos_token=_snake_case       ,    pad_token=_snake_case       ,    **_snake_case       ,    )

						if not isinstance(self.backend_tokenizer.pre_tokenizer       ,    pre_tokenizers.Sequence				):
											raise ValueError(
											    "The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"
											    " heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"
											    " to be compatible with this version.The easiest way to do so is"
											    " `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"
											    " to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"
											    " transformers."				)

						self._wrap_decode_method_backend_tokenizer()
	def   _UpperCamelCase    (			self					: List[Any]				)    ->			int:
						UpperCAmelCase						:				Optional[Any]		  =			self.backend_tokenizer.decode

						def new_decode_method(*_a					: Optional[int]       ,    **_a					: Dict				):
											UpperCAmelCase						:				Optional[int]		  =			orig_decode_method(*_snake_case       ,    **_snake_case				)
											UpperCAmelCase						:				str		  =			text.replace(self.backend_tokenizer.model.end_of_word_suffix       ,    " "				).strip()
											return text

						UpperCAmelCase						:				List[str]		  =			new_decode_method
	def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: Optional[int]       ,    _a					: Dict = None				)    ->			List[int]:
						UpperCAmelCase						:				Tuple		  =			[self.bos_token_id]
						UpperCAmelCase						:				str		  =			[self.eos_token_id]

						if token_ids_a is None:
											return bos_token + token_ids_a + eos_token
						return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
	def   _UpperCamelCase    (			self					: Any       ,    _a					: List[str]       ,    _a					: Tuple = None				)    ->			List[int]:
						UpperCAmelCase						:				Union[str, Any]		  =			[self.bos_token_id]
						UpperCAmelCase						:				List[Any]		  =			[self.eos_token_id]

						if token_ids_a is None:
											return len(bos_token + token_ids_a + eos_token				) * [0]
						return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token				) * [0]

	def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: str       ,    _a					: int = None				)    ->			Tuple[str]:
						UpperCAmelCase						:				Tuple		  =			self._tokenizer.model.save(_snake_case       ,    name=_snake_case				)
						return tuple(_snake_case				)







from cva import destroyAllWindows, imread, imshow, waitKey







def   A   (		A__		,		A__ ):
     UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			img.shape[0], img.shape[1]

     def to_grayscale(A__		,		A__		,		A__ ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(A__ ):
          return min(A__		,		2_5_5 )

     for i in range(A__ ):
          for j in range(A__ ):
               UpperCAmelCase						:				Optional[Any]		  =			int(to_grayscale(*img[i][j] ) )
               UpperCAmelCase						:				List[str]		  =			[
                   normalize(A__ ),
                   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 (1_0, 2_0, 3_0, 4_0)
      }

      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()



