

from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   k_size // 2
							snake_case_    ,   snake_case_          =   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							snake_case_          =   1 / (2 * pi * sigma) * exp(-(square(UpperCamelCase__				) + square(UpperCamelCase__				)) / (2 * square(UpperCamelCase__				))				)
							return g





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_    ,   snake_case_          =   image.shape[0], image.shape[1]
							# dst image height and width
							snake_case_          =   height - k_size + 1
							snake_case_          =   width - k_size + 1

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							snake_case_          =   zeros((dst_height * dst_width, k_size * k_size)				)
							snake_case_          =   0
							for i, j in product(range(UpperCamelCase__				)       ,							range(UpperCamelCase__				)				):
														snake_case_          =   ravel(image[i : i + k_size, j : j + k_size]				)
														snake_case_          =   window
														row += 1

							#  turn the kernel into shape(k*k, 1)
							snake_case_          =   gen_gaussian_kernel(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   ravel(UpperCamelCase__				)

							# reshape and get the dst image
							snake_case_          =   dot(UpperCamelCase__       ,							UpperCamelCase__				).reshape(UpperCamelCase__       ,							UpperCamelCase__				).astype(UpperCamelCase__				)

							return dst


if __name__ == "__main__":
					# read original image
					_UpperCAmelCase		:       str						    =	imread(R"""../image_data/lena.jpg""")
					# turn image in gray scale value
					_UpperCAmelCase		:       List[str]						    =	cvtColor(img, COLOR_BGR2GRAY)

					# get values with two different mask size
					_UpperCAmelCase		:       int						    =	gaussian_filter(gray, 3, sigma=1)
					_UpperCAmelCase		:       Union[str, Any]						    =	gaussian_filter(gray, 5, sigma=0.8)

					# show result images
					imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
					imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
					waitKey()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase		:       Tuple						    =	{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       int						    =	[
					    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """XLMRobertaXLForCausalLM""",
					    """XLMRobertaXLForMaskedLM""",
					    """XLMRobertaXLForMultipleChoice""",
					    """XLMRobertaXLForQuestionAnswering""",
					    """XLMRobertaXLForSequenceClassification""",
					    """XLMRobertaXLForTokenClassification""",
					    """XLMRobertaXLModel""",
					    """XLMRobertaXLPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[Any]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)




