




import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowercase_			:       Union[str, Any]						       =1_80


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  np.zeros(snake_case_		)

						for row in range(1			,					image_shape[0] - 1		):
												for col in range(1			,					image_shape[1] - 1		):
																		_UpperCAmelCase									=  gradient_direction[row, col]

																		if (
																		    0 <= direction < PI / 8
																		    or 15 * PI / 8 <= direction <= 2 * PI
																		    or 7 * PI / 8 <= direction <= 9 * PI / 8
																		):
																								_UpperCAmelCase									=  sobel_grad[row, col - 1]
																								_UpperCAmelCase									=  sobel_grad[row, col + 1]
																								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																														_UpperCAmelCase									=  sobel_grad[row, col]

																		elif (
																		    PI / 8 <= direction < 3 * PI / 8
																		    or 9 * PI / 8 <= direction < 11 * PI / 8
																		):
																								_UpperCAmelCase									=  sobel_grad[row + 1, col - 1]
																								_UpperCAmelCase									=  sobel_grad[row - 1, col + 1]
																								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																														_UpperCAmelCase									=  sobel_grad[row, col]

																		elif (
																		    3 * PI / 8 <= direction < 5 * PI / 8
																		    or 11 * PI / 8 <= direction < 13 * PI / 8
																		):
																								_UpperCAmelCase									=  sobel_grad[row - 1, col]
																								_UpperCAmelCase									=  sobel_grad[row + 1, col]
																								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																														_UpperCAmelCase									=  sobel_grad[row, col]

																		elif (
																		    5 * PI / 8 <= direction < 7 * PI / 8
																		    or 13 * PI / 8 <= direction < 15 * PI / 8
																		):
																								_UpperCAmelCase									=  sobel_grad[row - 1, col - 1]
																								_UpperCAmelCase									=  sobel_grad[row + 1, col + 1]
																								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																														_UpperCAmelCase									=  sobel_grad[row, col]

						return destination


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						for row in range(1			,					image_shape[0] - 1		):
												for col in range(1			,					image_shape[1] - 1		):
																		if destination[row, col] >= threshold_high:
																								_UpperCAmelCase									=  strong
																		elif destination[row, col] <= threshold_low:
																								_UpperCAmelCase									=  0
																		else:
																								_UpperCAmelCase									=  weak


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						for row in range(1			,					image_shape[0]		):
												for col in range(1			,					image_shape[1]		):
																		if destination[row, col] == weak:
																								if 255 in (
																								    destination[row, col + 1],
																								    destination[row, col - 1],
																								    destination[row - 1, col],
																								    destination[row + 1, col],
																								    destination[row - 1, col - 1],
																								    destination[row + 1, col - 1],
																								    destination[row - 1, col + 1],
																								    destination[row + 1, col + 1],
																								):
																														_UpperCAmelCase									=  strong
																								else:
																														_UpperCAmelCase									=  0


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_=15			,					snake_case_=30			,					snake_case_=128			,					snake_case_=255		):

						'''simple docstring'''


						_UpperCAmelCase									=  img_convolve(snake_case_			,					gen_gaussian_kernel(9			,					sigma=1.4		)		)
						# get the gradient and degree by sobel_filter
						_UpperCAmelCase							,				_UpperCAmelCase									=  sobel_filter(snake_case_		)
						_UpperCAmelCase									=  PI + np.radadeg(snake_case_		)

						_UpperCAmelCase									=  suppress_non_maximum(image.shape			,					snake_case_			,					snake_case_		)

						detect_high_low_threshold(
						    image.shape			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_		)

						track_edge(image.shape			,					snake_case_			,					snake_case_			,					snake_case_		)

						return destination


if __name__ == "__main__":
				# read original image in gray mode
				lowercase_			:       int						       =cva.imread(r'../image_data/lena.jpg', 0)
				# canny edge detection
				lowercase_			:       List[str]						       =canny(lena)
				cva.imshow('canny', canny_destination)
				cva.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"   )   )




