
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		inspect.getfile(accelerate.test_utils)
												lowercase					=		os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
												lowercase					=		os.path.sep.join(
												    mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''])
												lowercase					=		os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_ops.py'''])




							@require_multi_gpu
							def 				A__   (       self):
												print(f'Found {torch.cuda.device_count()} devices.')
												lowercase					=		['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
												with patch_environment(omp_num_threads=1):
																	execute_subprocess_async(A__     ,env=os.environ.copy())




							@require_multi_gpu
							def 				A__   (       self):
												print(f'Found {torch.cuda.device_count()} devices.')
												lowercase					=		['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
												print(f'Command: {cmd}')
												with patch_environment(omp_num_threads=1):
																	execute_subprocess_async(A__     ,env=os.environ.copy())




							@require_multi_gpu
							def 				A__   (       self):
												lowercase					=		['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
												with patch_environment(omp_num_threads=1):
																	execute_subprocess_async(A__     ,env=os.environ.copy())





							@require_multi_gpu
							def 				A__   (       self):
												print(f'Found {torch.cuda.device_count()} devices, using 2 devices only')
												lowercase					=		['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
												with patch_environment(omp_num_threads=1     ,cuda_visible_devices='''0,1'''):
																	execute_subprocess_async(A__     ,env=os.environ.copy())


if __name__ == "__main__":
							lowercase__	:int										=					Accelerator()
							lowercase__	:Optional[int]										=					(accelerator.state.process_index + 2, 10)
							lowercase__	:List[str]										=					torch.randint(0, 10, shape).to(accelerator.device)

							lowercase__	:Any										=					""

							lowercase__	:List[str]										=					accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							lowercase__	:Dict										=					accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							lowercase__	:Tuple										=					accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# Raise error at the end to make sure we don't stop at the first failure.
							if len(error_msg) > 0:
														raise ValueError(error_msg)






from pathlib import Path

import numpy as np
from PIL import Image



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase			,		lowercase					=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
					return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return (gray > 127) & (gray <= 255)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.zeros_like(lowerCAmelCase__	)
					lowercase					=		np.zeros(
					    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

					# Copy image to padded image
					lowercase					=		image

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


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




