
"""simple docstring"""




from pathlib import Path

import numpy as np
from PIL import Image


def 						UpperCAmelCase      (     a_						)		->     List[Any]:







				"""simple docstring"""



				__A             =			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""simple docstring"""



				return (gray > 1_2_7) & (gray <= 2_5_5)


def 						UpperCAmelCase      (     a_		,       a_						)		->     List[str]:







				"""simple docstring"""



				__A             =			np.zeros_like(__lowerCamelCase						)
				__A             =			np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

				# Copy image to padded image
				__A             =			image

				# Iterate over image & apply kernel
				for x in range(image.shape[1]						):
								for y in range(image.shape[0]						):
												__A             =			(
												    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
												).sum()
												__A             =			int(summation > 0						)
				return output


if __name__ == "__main__":
					# read original image
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
					SCREAMING_SNAKE_CASE							:Optional[Any]										=    np.array(Image.open(lena_path))
					# kernel to be applied
					SCREAMING_SNAKE_CASE							:Optional[Any]										=    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
					SCREAMING_SNAKE_CASE							:int										=    dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
					# Save the output image
					SCREAMING_SNAKE_CASE							:List[Any]										=    Image.fromarray(output).convert('RGB')
					pil_img.save('result_dilation.png')


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			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:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

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

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

    # 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      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			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(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""simple docstring"""



				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()
