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






class      __lowercase					(    _A    ):
						lowercase					 =     ['image_processor', 'tokenizer']
						lowercase					 =     'AutoImageProcessor'
						lowercase					 =     'AutoTokenizer'

						def __init__(	self				:      List[str]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Any   )							->		str:


									'''simple docstring'''

									super().__init__(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		self.image_processor

						def __call__(	self				:      int							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      List[Any]=None							,							**__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

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

									if text is not None:
												lowercase							=		self.tokenizer(__lowerCamelCase							,							return_tensors=__lowerCamelCase							,							**__lowerCamelCase   )

									if images is not None:
												lowercase							=		self.image_processor(__lowerCamelCase							,							return_tensors=__lowerCamelCase							,							**__lowerCamelCase   )

									if text is not None and images is not None:
												lowercase							=		image_features.pixel_values
												return encoding
									elif text is not None:
												return encoding
									else:
												return BatchEncoding(data=dict(**__lowerCamelCase   )							,							tensor_type=__lowerCamelCase   )

						def        __a		(	self				:      Optional[int]							,							*__lowerCamelCase				:      int							,							**__lowerCamelCase				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									return self.tokenizer.batch_decode(*__lowerCamelCase							,							**__lowerCamelCase   )

						def        __a		(	self				:      Optional[Any]							,							*__lowerCamelCase				:      Dict							,							**__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									return self.tokenizer.decode(*__lowerCamelCase							,							**__lowerCamelCase   )



						@property
						def        __a		(	self				:      Optional[int]   )							->		Tuple:


									'''simple docstring'''

									return ["input_ids", "attention_mask", "pixel_values"]






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		img.shape
			for i in range(kernel_size // 2,		size_x - kernel_size // 2       ):
						for j in range(kernel_size // 2,		size_y - kernel_size // 2       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						A_    ,    A_    ,    A_    ,    A_      =      parse_args(sys.argv)
						A_      =      cva.imread(filename, 0)
						cva.imshow("input image", img)

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()





