





"""simple docstring"""






import gc
import threading
import time

import psutil
import torch




class 			__lowercase     :







					def __init__(  self       ):
									__a	:  List[str]         =					psutil.Process()
									__a	:  List[str]         =					False







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					-1

									while True:
													__a	:  int         =					max(self.process.memory_info().rss		,     self.cpu_memory_peak       )

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													if not self.peak_monitoring:
																	break







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					True
									__a	:  Dict         =					threading.Thread(target=self.peak_monitor       )
									__a	:  Any         =					True
									self.thread.start()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					False
									self.thread.join()
									return self.cpu_memory_peak






A	     =					PeakCPUMemory()

def       __A (     )						->						Optional[int]:
				# Time
				__a	:  List[Any]         =					{'''time''': time.time()}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__a	:  int         =					psutil.Process().memory_info().rss
				cpu_peak_tracker.start()

				# GPU mem
				for i in range(torch.cuda.device_count()):
								__a	:  Dict         =					torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_)
				torch.cuda.reset_peak_memory_stats()

				return measures

def       __A (     a_  :Union[str, Any])						->						Union[str, Any]:
				# Time
				__a	:  Any         =					{'''time''': time.time() - start_measures['''time''']}

				gc.collect()
				torch.cuda.empty_cache()

				# CPU mem
				__a	:  Any         =					(psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
				__a	:  str         =					(cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

				# GPU mem
				for i in range(torch.cuda.device_count()):
								__a	:  Optional[Any]         =					(torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_) - start_measures[str(SCREAMING_SNAKE_CASE_)]) / 2**20
								__a	:  Union[str, Any]         =					(torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_) - start_measures[str(SCREAMING_SNAKE_CASE_)]) / 2**20

				return measures






def       __A (     a_  :Any				,    a_  :Union[str, Any])						->						int:
				print(F"""{description}:""")
				print(F"""- Time: {measures["time"]:.2f}s""")
				for i in range(torch.cuda.device_count()):
								print(F"""- GPU {i} allocated: {measures[str(SCREAMING_SNAKE_CASE_)]:.2f}MiB""")
								__a	:  Dict         =					measures[F"""{i}-peak"""]
								print(F"""- GPU {i} peak: {peak:.2f}MiB""")
				print(F"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""")
				print(F"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""")






"""simple docstring"""






from pathlib import Path

import numpy as np
from PIL import Image

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				__a    ,  __a    ,  __a	:  Union[str, Any]         =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
				return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b

def       __A (     a_  :np.ndarray)						->						np.ndarray:
				return (gray > 1_27) & (gray <= 2_55)

def       __A (     a_  :np.ndarray				,    a_  :np.ndarray)						->						np.ndarray:
				__a	:  Optional[int]         =					np.zeros_like(a_)
				__a	:  Dict         =					np.zeros(
				    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))

				# Copy image to padded image
				__a	:  int         =					image

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


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