



'''simple docstring'''

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 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Union[str, Any]:
	_lowercase					:       int       =		k_size // 2
	_lowercase  ,    _lowercase					:       List[Any]       =		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	_lowercase					:       Optional[int]       =		1 / (2 * pi * sigma) * exp(-(square(_lowercase					) + square(_lowercase					)) / (2 * square(_lowercase					))					)
	return g







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[int]:
	_lowercase  ,    _lowercase					:       Any       =		image.shape[0], image.shape[1]
	# dst image height and width
	_lowercase					:       List[str]       =		height - k_size + 1
	_lowercase					:       List[str]       =		width - k_size + 1

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

	#  turn the kernel into shape(k*k, 1)
	_lowercase					:       Optional[Any]       =		gen_gaussian_kernel(_lowercase,				_lowercase					)
	_lowercase					:       str       =		ravel(_lowercase					)

	# reshape and get the dst image
	_lowercase					:       List[str]       =		dot(_lowercase,				_lowercase					).reshape(_lowercase,				_lowercase					).astype(_lowercase					)

	return dst


if __name__ == "__main__":
		# read original image
		_A				:    Optional[int]     						=imread(r'''../image_data/lena.jpg''')
		# turn image in gray scale value
		_A				:    Union[str, Any]     						=cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		_A				:    Dict     						=gaussian_filter(gray, 3, sigma=1)
		_A				:    Optional[int]     						=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()





'''simple docstring'''

from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def 						__UpperCamelCase				(							_lowercase					) ->			None:
	_lowercase  ,    _lowercase					:       List[Any]       =		analyze_text(_lowercase					)
	_lowercase					:       Any       =		list(' ' + ascii_lowercase					)
	# what is our total sum of probabilities.
	_lowercase					:       Union[str, Any]       =		sum(single_char_strings.values()					)

	# one length string
	_lowercase					:       Union[str, Any]       =		0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			_lowercase					:       Any       =		single_char_strings[ch]
			_lowercase					:       int       =		my_str / all_sum
			my_fir_sum += prob * math.loga(_lowercase					)  # entropy formula.

    # print entropy
	print(f'''{round(-1 * my_fir_sum					):.1f}'''					)

	# two len string
	_lowercase					:       str       =		sum(two_char_strings.values()					)
	_lowercase					:       str       =		0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			_lowercase					:       Optional[Any]       =		cha + cha
			if sequence in two_char_strings:
				_lowercase					:       int       =		two_char_strings[sequence]
				_lowercase					:       Optional[int]       =		int(_lowercase					) / all_sum
				my_sec_sum += prob * math.loga(_lowercase					)

    # print second entropy
	print(f'''{round(-1 * my_sec_sum					):.1f}'''					)

	# print the difference between them
	print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'''					)







def 						__UpperCamelCase				(							_lowercase					) ->			tuple[dict, dict]:
	_lowercase					:       Optional[Any]       =		Counter()  # type: ignore
	_lowercase					:       List[Any]       =		Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0,				len(_lowercase					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings







def 						__UpperCamelCase				(							) ->			List[Any]:
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
		main()
