




'''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_(	snake_case							:       Dict							,				snake_case							:       Dict     ):

							'''simple docstring'''






							snake_case_						=      k_size // 2
							snake_case_					,      snake_case_						=      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							snake_case_						=      1 / (2 * pi * sigma) * exp(-(square(snake_case     ) + square(snake_case     )) / (2 * square(snake_case     ))     )
							return g


def      UpperCamelCase_(	snake_case							:       List[str]							,				snake_case							:       Any							,				snake_case							:       List[Any]     ):

							'''simple docstring'''






							snake_case_					,      snake_case_						=      image.shape[0], image.shape[1]
							# dst image height and width
							snake_case_						=      height - k_size + 1
							snake_case_						=      width - k_size + 1

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

							#  turn the kernel into shape(k*k, 1)
							snake_case_						=      gen_gaussian_kernel(snake_case							,				snake_case     )
							snake_case_						=      ravel(snake_case     )

							# reshape and get the dst image
							snake_case_						=      dot(snake_case							,				snake_case     ).reshape(snake_case							,				snake_case     ).astype(snake_case     )

							return dst


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

		# get values with two different mask size
		_SCREAMING_SNAKE_CASE							:   Optional[Any]      		= gaussian_filter(gray, 3, sigma=1)
		_SCREAMING_SNAKE_CASE							:   Any      		= 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"""







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       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Union[str, Any]								= inspect.getfile(accelerate.test_utils       )
        lowerCAmelCase_   :				Dict								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_script.py']       )
        lowerCAmelCase_   :				int								= os.path.sep.join(
            mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_distributed_data_loop.py']       )
        lowerCAmelCase_   :				Union[str, Any]								= os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_ops.py']       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				Optional[int]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        print(F"Found {torch.cuda.device_count()} devices."       )
        lowerCAmelCase_   :				int								= ['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(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )



      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= ['torchrun', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__       )]
        with patch_environment(omp_num_threads=1       ):
          execute_subprocess_async(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )





      @require_multi_gpu
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        print(F"Found {torch.cuda.device_count()} devices, using 2 devices only"       )
        lowerCAmelCase_   :				Any								= ['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(SCREAMING_SNAKE_CASE_    ,			env=os.environ.copy()       )


if __name__ == "__main__":
     lowercase__		:				Dict          =	Accelerator()
     lowercase__		:				List[Any]          =	(accelerator.state.process_index + 2, 1_0)
     lowercase__		:				Any          =	torch.randint(0, 1_0, shape).to(accelerator.device)

     lowercase__		:				List[Any]          =	""""""

     lowercase__		:				Union[str, Any]          =	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__		:				Optional[Any]          =	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__		:				Any          =	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)





