






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     int:
							lowerCAmelCase		,				lowerCAmelCase     		=		image.shape
							lowerCAmelCase     		=		cols - block_size[1] + 1
							lowerCAmelCase     		=		rows - block_size[0] + 1
							lowerCAmelCase     		=		zeros((dst_height * dst_width, block_size[1] * block_size[0])							)
							lowerCAmelCase     		=		0
							for i in range(0       ,  snake_case__							):
														for j in range(0       ,  snake_case__							):
																					lowerCAmelCase     		=		ravel(image[i : i + block_size[0], j : j + block_size[1]]							)
																					lowerCAmelCase     		=		window
																					row += 1

							return image_array

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     str:
							lowerCAmelCase		,				lowerCAmelCase     		=		image.shape[0], image.shape[1]
							lowerCAmelCase     		=		filter_kernel.shape[0]
							lowerCAmelCase     		=		k_size // 2
							# Pads image with the edge values of array.
							lowerCAmelCase     		=		pad(snake_case__       ,  snake_case__       ,  mode='''edge'''							)

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							lowerCAmelCase     		=		imacol(snake_case__       ,  (k_size, k_size)							)

							#  turn the kernel into shape(k*k, 1)
							lowerCAmelCase     		=		ravel(snake_case__							)
							# reshape and get the dst image
							lowerCAmelCase     		=		dot(snake_case__       ,  snake_case__							).reshape(snake_case__       ,  snake_case__							)
							return dst


if __name__ == "__main__":
	# read original image
	lowercase__      :    Optional[Any]          =     imread(R'''../image_data/lena.jpg''')
	# turn image in gray scale value
	lowercase__      :    Union[str, Any]          =     cvtColor(img, COLOR_BGR2GRAY)
	# Laplace operator
	lowercase__      :    Any          =     array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
	lowercase__      :    List[str]          =     img_convolve(gray, Laplace_kernel).astype(uinta)
	imshow('''Laplacian''', out)
	waitKey(0)




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Union[str, Any]:
								return cls




	@slow
	@require_torch_gpu
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.get_auto_remove_tmp_dir()
								lowerCAmelCase     		=		F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

								# we want this test to run even if there is only one GPU, but if there are more we use them all
								lowerCAmelCase     		=		get_gpu_count()
								lowerCAmelCase     		=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
								lowerCAmelCase     		=		[sys.executable] + distributed_args + args
								execute_subprocess_async(__SCREAMING_SNAKE_CASE		,      env=self.get_env()    )

								lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''test_bleu.json'''    )
								lowerCAmelCase     		=		load_json(__SCREAMING_SNAKE_CASE    )
								# print(metrics)
								self.assertGreaterEqual(metrics['''bleu''']		,      25    )



