import numpy as np
from PIL import Image







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					np.array   ):







    '''simple docstring'''
    __snake_case			, __snake_case			, __snake_case       :				List[str] 			=    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					np.array   ):







    '''simple docstring'''
    return (gray > 1_2_7) & (gray <= 2_5_5)







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					np.array		,							__SCREAMING_SNAKE_CASE       :					np.array   ):







    '''simple docstring'''
    __snake_case       :				Union[str, Any] 			=    np.zeros_like(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Optional[Any] 			=    np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)   )

    # Copy image to padded image
    __snake_case       :				int 			=    image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]   ):
        for y in range(image.shape[0]   ):
            __snake_case       :				Dict 			=    (
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            __snake_case       :				Optional[int] 			=    int(summation == 5   )
    return output


# kernel to be applied
lowercase_  							=			np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
     # read original image
     lowercase_  							=			np.array(Image.open(r"..\image_data\lena.jpg"))
     # Apply erosion operation to a binary image
     lowercase_  							=			erosion(grayabinary(rgbagray(image)), structuring_element)
     # Save the output image
     lowercase_  							=			Image.fromarray(output).convert("RGB")
     pil_img.save("result_erosion.png")




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""			)
           if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""			)

           super().__init__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           if text is None and videos is None:
               raise ValueError("""You have to specify either text or videos. Both cannot be none."""			)

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



