



"""simple docstring"""

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowercase__			   =  logging.getLogger()




@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase	):



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None						,     _lowercase = None						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		Dict									=       [file for file in os.listdir(lowercase_							) if os.path.isfile(os.path.join(lowercase_						,     lowercase_							)							)]

													if identifier is not None:
																				__a		:		Union[str, Any]									=       [file for file in files if identifier in file]

													if n_identifier is not None:
																				if isinstance(lowercase_						,     lowercase_							):
																											for n_ in n_identifier:
																																		__a		:		Optional[Any]									=       [file for file in files if n_ not in file]
																				else:
																											__a		:		Optional[Any]									=       [file for file in files if n_identifier not in file]

													__a		:		str									=       ignore_files or []
													ignore_files.append("""__init__.py"""							)
													__a		:		str									=       [file for file in files if file not in ignore_files]

													for file in files:
																				# Open all files
																				print("""Testing"""						,     lowercase_							)

																				if only_modules:
																											__a		:		Optional[Any]									=       file.split("""."""							)[0]
																											try:
																																		__a		:		int									=       getattr(lowercase_						,     lowercase_							)
																																		__a		:		Dict									=       doctest.DocTestSuite(lowercase_							)
																																		__a		:		Dict									=       unittest.TextTestRunner().run(lowercase_							)
																																		self.assertIs(len(result.failures							)						,     0							)
																											except AttributeError:
																																		logger.info(F'''{module_identifier} is not a module.'''							)
																				else:
																											__a		:		int									=       doctest.testfile(str("""..""" / directory / file							)						,     optionflags=doctest.ELLIPSIS							)
																											self.assertIs(result.failed						,     0							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       Path("""src/transformers"""							)
													__a		:		Dict									=       "modeling"
													__a		:		Tuple									=       [
													    "modeling_ctrl.py",
													    "modeling_tf_ctrl.py",
													]
													self.analyze_directory(lowercase_						,     identifier=lowercase_						,     ignore_files=lowercase_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       Path("""src/transformers"""							)
													__a		:		Optional[Any]									=       "tokenization"
													self.analyze_directory(lowercase_						,     identifier=lowercase_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       Path("""src/transformers"""							)
													__a		:		Union[str, Any]									=       "configuration"
													self.analyze_directory(lowercase_						,     identifier=lowercase_							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       Path("""src/transformers"""							)
													__a		:		Dict									=       ["configuration", "modeling", "tokenization"]
													self.analyze_directory(lowercase_						,     n_identifier=lowercase_							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       Path("""docs/source"""							)
													__a		:		Any									=       ["favicon.ico"]
													self.analyze_directory(lowercase_						,     ignore_files=lowercase_						,     only_modules=lowercase_							)






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       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					):
																					__a		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       5
							return filename, spatial_variance, intensity_variance, kernel_size


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

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

