
'''simple docstring'''







# Copyright 2022 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 os
from typing import List



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: str	):
						if name != "training_function" and name != "main":
												raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''	)
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			[], False
						for line in lines:
												if not found_start and F'def {name}' in line:
																		_SCREAMING_SNAKE_CASE					=			True
																		good_lines.append(_SCREAMING_SNAKE_CASE	)
																		continue
												if found_start:
																		if name == "training_function" and "def main" in line:
																								return good_lines
																		if name == "main" and "if __name__" in line:
																								return good_lines
																		good_lines.append(_SCREAMING_SNAKE_CASE	)



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]	):
						return [line for line in lines if not line.lstrip().startswith("#"	) and line != "\n"]




def       _a		(						_SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: bool						,      _SCREAMING_SNAKE_CASE		: str = None	):
						with open(_SCREAMING_SNAKE_CASE						,      "r"	) as f:
												_SCREAMING_SNAKE_CASE					=			f.readlines()
						with open(os.path.abspath(os.path.join("examples"						,      "nlp_example.py"	)	)						,      "r"	) as f:
												_SCREAMING_SNAKE_CASE					=			f.readlines()
						with open(_SCREAMING_SNAKE_CASE						,      "r"	) as f:
												_SCREAMING_SNAKE_CASE					=			f.readlines()
						if secondary_filename is not None:
												with open(_SCREAMING_SNAKE_CASE						,      "r"	) as f:
																		_SCREAMING_SNAKE_CASE					=			f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
						if parser_only:
												_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "main"	)	)
												_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "main"	)	)
												_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "main"	)	)
												if secondary_filename is not None:
																		_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "main"	)	)
						else:
												_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "training_function"	)	)
												_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "training_function"	)	)
												_SCREAMING_SNAKE_CASE					=			clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "training_function"	)	)
												if secondary_filename is not None:
																		_SCREAMING_SNAKE_CASE					=			clean_lines(
																		    get_function_contents_by_name(_SCREAMING_SNAKE_CASE						,      "training_function"	)	)

						_SCREAMING_SNAKE_CASE					=			"train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

						# Specific code in our script that differs from the full version, aka what is new
						_SCREAMING_SNAKE_CASE					=			[]
						_SCREAMING_SNAKE_CASE					=			[]  # We keep track of the idxs just in case it's a repeated statement
						_SCREAMING_SNAKE_CASE					=			iter(_SCREAMING_SNAKE_CASE	)
						for i in range(len(_SCREAMING_SNAKE_CASE	) - 1	):
												if i not in passed_idxs:
																		_SCREAMING_SNAKE_CASE					=			next(_SCREAMING_SNAKE_CASE	)
																		if (line not in full_file_func) and (line.lstrip() != _dl_line):
																								if "TESTING_MOCKED_DATALOADERS" not in line:
																														new_feature_code.append(_SCREAMING_SNAKE_CASE	)
																														passed_idxs.append(_SCREAMING_SNAKE_CASE	)
																								else:
																														# Skip over the `config['num_epochs'] = 2` statement
																														_SCREAMING_SNAKE_CASE					=			next(_SCREAMING_SNAKE_CASE	)

    # Extract out just the new parts from the full_file_training_func
						_SCREAMING_SNAKE_CASE					=			[]
						_SCREAMING_SNAKE_CASE					=			[]  # We keep track of the idxs just in case it's a repeated statement
						for i, line in enumerate(_SCREAMING_SNAKE_CASE	):
												if i not in passed_idxs:
																		if (line not in full_file_func) and (line.lstrip() != _dl_line):
																								if "TESTING_MOCKED_DATALOADERS" not in line:
																														new_full_example_parts.append(_SCREAMING_SNAKE_CASE	)
																														passed_idxs.append(_SCREAMING_SNAKE_CASE	)

    # Finally, get the overall diff
						_SCREAMING_SNAKE_CASE					=			[line for line in new_feature_code if line not in new_full_example_parts]
						if secondary_filename is not None:
												_SCREAMING_SNAKE_CASE					=			[line for line in full_file_contents if line not in secondary_file_func]
												_SCREAMING_SNAKE_CASE					=			[line for line in diff_from_example if line not in diff_from_two]

						return diff_from_example

'''simple docstring'''







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

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    1
						    / (2 * np.pi * sigma)
						    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE	) + np.square(_SCREAMING_SNAKE_CASE	)) / (2 * np.square(_SCREAMING_SNAKE_CASE	))	)
						)
						return g



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

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

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

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

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

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

						return destination



def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						for row in range(1						,      image_shape[0] - 1	):
												for col in range(1						,      image_shape[1] - 1	):
																		if destination[row, col] >= threshold_high:
																								_SCREAMING_SNAKE_CASE					=			strong
																		elif destination[row, col] <= threshold_low:
																								_SCREAMING_SNAKE_CASE					=			0
																		else:
																								_SCREAMING_SNAKE_CASE					=			weak



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						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],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



def       _a		(						_SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: List[str]=15						,      _SCREAMING_SNAKE_CASE		: List[Any]=30						,      _SCREAMING_SNAKE_CASE		: Optional[int]=128						,      _SCREAMING_SNAKE_CASE		: List[Any]=255	):
						# gaussian_filter
						_SCREAMING_SNAKE_CASE					=			img_convolve(_SCREAMING_SNAKE_CASE						,      gen_gaussian_kernel(9						,      sigma=1.4	)	)
						# get the gradient and degree by sobel_filter
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			sobel_filter(_SCREAMING_SNAKE_CASE	)
						_SCREAMING_SNAKE_CASE					=			PI + np.radadeg(_SCREAMING_SNAKE_CASE	)

						_SCREAMING_SNAKE_CASE					=			suppress_non_maximum(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						detect_high_low_threshold(
						    image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						track_edge(image.shape						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

						return destination


if __name__ == "__main__":
						# read original image in gray mode
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)