



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if (ksize % 2) == 0:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ksize + 1
						SCREAMING_SNAKE_CASE__     :				Any   =					np.zeros((ksize, ksize)			,   dtype=np.floataa     )

						# each value
						for y in range(SCREAMING_SNAKE_CASE_     ):
												for x in range(SCREAMING_SNAKE_CASE_     ):
																		# distance from center
																		SCREAMING_SNAKE_CASE__     :				int   =					x - ksize // 2
																		SCREAMING_SNAKE_CASE__     :				List[str]   =					y - ksize // 2

																		# degree to radiant
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					theta / 180 * np.pi
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					np.cos(_theta     )
																		SCREAMING_SNAKE_CASE__     :				str   =					np.sin(_theta     )

																		# get kernel x
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					cos_theta * px + sin_theta * py

																		# get kernel y
																		SCREAMING_SNAKE_CASE__     :				int   =					-sin_theta * px + cos_theta * py

																		# fill kernel
																		SCREAMING_SNAKE_CASE__     :				Any   =					np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)     ) * np.cos(2 * np.pi * _x / lambd + psi     )

						return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				__lowercase       :Union[str, Any]       =  imread("../image_data/lena.jpg")
				# turn image in gray scale value
				__lowercase       :Any       =  cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				__lowercase       :Union[str, Any]       =  np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								__lowercase       :Dict       =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				__lowercase       :Dict       =  out / out.max() * 255
				__lowercase       :Tuple       =  out.astype(np.uinta)

				imshow("Original", gray)
				imshow("Gabor filter with 20x20 mask and 6 directions", out)

				waitKey(0)






import warnings

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



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["image_processor", "tokenizer"]
							snake_case_							 =						"CLIPImageProcessor"
							snake_case_							 =						("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(      self				:     Any							,	a				:     List[Any]=None							,	a				:     Any=None							,	**a				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[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."							,	a							,	)
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					kwargs.pop("feature_extractor"    )

													SCREAMING_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__(a							,	a    )

							def __call__(      self				:     Tuple							,	a				:     Tuple=None							,	a				:     Union[str, Any]=None							,	a				:     List[str]=None							,	**a				:     Optional[Any]    )	->Optional[Any]:

													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:
																			SCREAMING_SNAKE_CASE__     :				str   =					self.tokenizer(a							,	return_tensors=a							,	**a    )

													if images is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					self.image_processor(a							,	return_tensors=a							,	**a    )

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

							def      A_  (      self				:     Optional[int]							,	*a				:     Any							,	**a				:     List[str]    )	->Any:
													return self.tokenizer.batch_decode(*a							,	**a    )

							def      A_  (      self				:     Any							,	*a				:     Optional[int]							,	**a				:     Dict    )	->Any:
													return self.tokenizer.decode(*a							,	**a    )

							@property
							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer.model_input_names
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

							@property
							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,	a							,	)
													return self.image_processor_class






							@property
							def      A_  (      self				:     Dict    )	->str:
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,	a							,	)
													return self.image_processor