"""simple docstring"""






import cva
import numpy as np
class        snake_case :




		"""simple docstring"""

		def __init__(       self     :			Any					,lowerCamelCase__     :			float					,lowerCamelCase__     :			int    ):

						if k in (0.0_4, 0.0_6):
										UpperCAmelCase__    =							k
										UpperCAmelCase__    =							window_size
						else:
										raise ValueError('invalid k value'    )







		def __str__(       self     :			List[Any]    ):
						return str(self.k    )







		def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			str    ):
						UpperCAmelCase__    =							cva.imread(lowerCamelCase__					,0    )
						UpperCAmelCase__   ,			UpperCAmelCase__    =							img.shape
						UpperCAmelCase__    =							[]
						UpperCAmelCase__    =							img.copy()
						UpperCAmelCase__    =							cva.cvtColor(lowerCamelCase__					,cva.COLOR_GRAY2RGB    )
						UpperCAmelCase__   ,			UpperCAmelCase__    =							np.gradient(lowerCamelCase__    )
						UpperCAmelCase__    =							dx**2
						UpperCAmelCase__    =							dy**2
						UpperCAmelCase__    =							dx * dy
						UpperCAmelCase__    =							0.0_4
						UpperCAmelCase__    =							self.window_size // 2
						for y in range(lowerCamelCase__					,h - offset    ):
										for x in range(lowerCamelCase__					,w - offset    ):
														UpperCAmelCase__    =							ixx[
														    y - offset : y + offset + 1, x - offset : x + offset + 1
														].sum()
														UpperCAmelCase__    =							iyy[
														    y - offset : y + offset + 1, x - offset : x + offset + 1
														].sum()
														UpperCAmelCase__    =							ixy[
														    y - offset : y + offset + 1, x - offset : x + offset + 1
														].sum()

														UpperCAmelCase__    =							(wxx * wyy) - (wxy**2)
														UpperCAmelCase__    =							wxx + wyy
														UpperCAmelCase__    =							det - k * (trace**2)
														# Can change the value
														if r > 0.5:
																		corner_list.append([x, y, r]    )
																		color_img.itemset((y, x, 0)					,0    )
																		color_img.itemset((y, x, 1)					,0    )
																		color_img.itemset((y, x, 2)					,255    )
						return color_img, corner_list


if __name__ == "__main__":
						lowerCAmelCase__						:						List[str]		  =			HarrisCorner(0.04, 3)
						lowerCAmelCase__						:						Optional[Any]		  =			edge_detect.detect('path_to_image')
						cva.imwrite('detect.png', color_img)




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCAmelCase__						:						Dict		  =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'



def 						a_		(      ):
				UpperCAmelCase__    =							_ask_options(
				    'In which compute environment are you running?'      ,    ['This machine', 'AWS (Amazon SageMaker)']      ,    _convert_compute_environment      ,    )
				if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
								UpperCAmelCase__    =							get_sagemaker_input()
				else:
								UpperCAmelCase__    =							get_cluster_input()
				return config



def 						a_		(      lowerCamelCase=None ):
				if subparsers is not None:
								UpperCAmelCase__    =							subparsers.add_parser('config'      ,    description=lowerCamelCase )
				else:
								UpperCAmelCase__    =							argparse.ArgumentParser('Accelerate config command'      ,    description=lowerCamelCase )

				parser.add_argument(
				    '--config_file'      ,    default=lowerCamelCase      ,    help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )      ,    )

				if subparsers is not None:
								parser.set_defaults(func=lowerCamelCase )
				return parser



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							get_user_input()
				if args.config_file is not None:
								UpperCAmelCase__    =							args.config_file
				else:
								if not os.path.isdir(lowerCamelCase ):
												os.makedirs(lowerCamelCase )
								UpperCAmelCase__    =							default_yaml_config_file

				if config_file.endswith('.json' ):
								config.to_json_file(lowerCamelCase )
				else:
								config.to_yaml_file(lowerCamelCase )
				print(f'''accelerate configuration saved at {config_file}''' )



def 						a_		(      ):
				UpperCAmelCase__    =							config_command_parser()
				UpperCAmelCase__    =							parser.parse_args()
				config_command(lowerCamelCase )


if __name__ == "__main__":
						main()



