




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_		):

						'''simple docstring'''


						_UpperCAmelCase							,				_UpperCAmelCase									=  image.shape
						_UpperCAmelCase									=  cols - block_size[1] + 1
						_UpperCAmelCase									=  rows - block_size[0] + 1
						_UpperCAmelCase									=  zeros((dst_height * dst_width, block_size[1] * block_size[0])		)
						_UpperCAmelCase									=  0
						for i in range(0			,					snake_case_		):
												for j in range(0			,					snake_case_		):
																		_UpperCAmelCase									=  ravel(image[i : i + block_size[0], j : j + block_size[1]]		)
																		_UpperCAmelCase									=  window
																		row += 1

						return image_array


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase							,				_UpperCAmelCase									=  image.shape[0], image.shape[1]
						_UpperCAmelCase									=  filter_kernel.shape[0]
						_UpperCAmelCase									=  k_size // 2
						# Pads image with the edge values of array.
						_UpperCAmelCase									=  pad(snake_case_			,					snake_case_			,					mode="edge"		)

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

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


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










import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class 					__lowerCAmelCase				( unittest.TestCase				):







			@patch("sys.argv"					,					["fakeprogrampath", "env"]   )
			def UpperCamelCase    ( self					:    List[str]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
									self.assertIn("Python version"					,					cs.out   )
									self.assertIn("Platform"					,					cs.out   )
									self.assertIn("Using distributed or parallel set-up in script?"					,					cs.out   )







			@is_pt_tf_cross_test
			@patch(
			    "sys.argv"					,					["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"					,					ignore_errors=snake_case__   )  # cleans potential past runs
									transformers.commands.transformers_cli.main()

									# The original repo has no TF weights -- if they exist, they were created by the CLI
									self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )







			@require_torch
			@patch("sys.argv"					,					["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )







			@require_torch
			@patch(
			    "sys.argv"					,					[
			        "fakeprogrampath",
			        "download",
			        "hf-internal-testing/test_dynamic_model_with_tokenizer",
			        "--trust-remote-code",
			        "--cache-dir",
			        "/tmp",
			    ]					,					)
			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
									self.assertTrue(
									    os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )




