




'''simple docstring'''

import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				test_file.split(os.path.sep	)
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
										    F"{test_file} instead."	)
					snake_case_									=				components[-1]
					if not test_fn.endswith('''py'''	):
										raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead."	)
					if not test_fn.startswith('''test_modeling_'''	):
										raise ValueError(
										    F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."	)

					snake_case_									=				components[:-1] + [test_fn.replace('''.py''',      ''''''	)]
					snake_case_									=				'''.'''.join(__UpperCAmelCase	)

					return test_module_path


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				get_module_path(__UpperCAmelCase	)
					snake_case_									=				importlib.import_module(__UpperCAmelCase	)

					return test_module


def 		__magic_name__    (	__UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				[]
					snake_case_									=				get_test_module(__UpperCAmelCase	)
					for attr in dir(__UpperCAmelCase	):
										if attr.endswith('''ModelTester'''	):
															tester_classes.append(getattr(__UpperCAmelCase,      __UpperCAmelCase	)	)

    # sort with class names
					return sorted(__UpperCAmelCase,      key=lambda __UpperCAmelCase	: x.__name__	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				[]
					snake_case_									=				get_test_module(__UpperCAmelCase	)
					for attr in dir(__UpperCAmelCase	):
										snake_case_									=				getattr(__UpperCAmelCase,      __UpperCAmelCase	)
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										snake_case_									=				getattr(__UpperCAmelCase,      '''all_model_classes''',      []	)
										if len(__UpperCAmelCase	) > 0:
															test_classes.append(__UpperCAmelCase	)

    # sort with class names
					return sorted(__UpperCAmelCase,      key=lambda __UpperCAmelCase	: x.__name__	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				get_test_classes(__UpperCAmelCase	)
					snake_case_									=				set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes	)

					# sort with class names
					return sorted(__UpperCAmelCase,      key=lambda __UpperCAmelCase	: x.__name__	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				test_class()
					if hasattr(__UpperCAmelCase,      '''setUp'''	):
										test.setUp()

					snake_case_									=				None
					if hasattr(__UpperCAmelCase,      '''model_tester'''	):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															snake_case_									=				test.model_tester.__class__

					return model_tester


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				get_test_classes(__UpperCAmelCase	)

					snake_case_									=				[]
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(__UpperCAmelCase	)

    # sort with class names
					return sorted(__UpperCAmelCase,      key=lambda __UpperCAmelCase	: x.__name__	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				get_test_classes_for_model(__UpperCAmelCase,      __UpperCAmelCase	)

					snake_case_									=				[]
					for test_class in test_classes:
										snake_case_									=				get_model_tester_from_test_class(__UpperCAmelCase	)
										if tester_class is not None:
															tester_classes.append(__UpperCAmelCase	)

    # sort with class names
					return sorted(__UpperCAmelCase,      key=lambda __UpperCAmelCase	: x.__name__	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				get_test_classes(__UpperCAmelCase	)
					snake_case_									=				{test_class: get_model_tester_from_test_class(__UpperCAmelCase	) for test_class in test_classes}
					return test_tester_mapping


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				get_model_classes(__UpperCAmelCase	)
					snake_case_									=				{
					    model_class: get_test_classes_for_model(__UpperCAmelCase,      __UpperCAmelCase	) for model_class in model_classes
					}
					return model_test_mapping


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				get_model_classes(__UpperCAmelCase	)
					snake_case_									=				{
					    model_class: get_tester_classes_for_model(__UpperCAmelCase,      __UpperCAmelCase	) for model_class in model_classes
					}
					return model_to_tester_mapping


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										return o
					elif isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										return o.__name__
					elif isinstance(__UpperCAmelCase,      (list, tuple)	):
										return [to_json(__UpperCAmelCase	) for x in o]
					elif isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										return {to_json(__UpperCAmelCase	): to_json(__UpperCAmelCase	) for k, v in o.items()}
					else:
										return o







'''simple docstring'''

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
a  :				Dict									=  (720, 1280)  # Height, Width
a  :				Tuple									=  (0.4, 0.6)  # if height or width lower than this scale, drop it.
a  :				Dict									=  1 / 100
a  :				str									=  ''
a  :				Any									=  ''
a  :				Optional[int]									=  ''
a  :				List[str]									=  250


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_			,snake_case_									=				get_dataset(__UpperCAmelCase,      __UpperCAmelCase	)
					for index in range(__UpperCAmelCase	):
										snake_case_									=				random.sample(range(len(__UpperCAmelCase	)	),      4	)
										snake_case_			,snake_case_			,snake_case_									=				update_image_and_anno(
										    __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      filter_scale=__UpperCAmelCase,      )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										snake_case_									=				random_chars(32	)
										snake_case_									=				path.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										snake_case_									=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
										cva.imwrite(F"{file_root}.jpg",      __UpperCAmelCase,      [cva.IMWRITE_JPEG_QUALITY, 85]	)
										print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"	)
										snake_case_									=				[]
										for anno in new_annos:
															snake_case_									=				anno[3] - anno[1]
															snake_case_									=				anno[4] - anno[2]
															snake_case_									=				anno[1] + width / 2
															snake_case_									=				anno[2] + height / 2
															snake_case_									=				F"{anno[0]} {x_center} {y_center} {width} {height}"
															annos_list.append(__UpperCAmelCase	)
										with open(F"{file_root}.txt",      '''w'''	) as outfile:
															outfile.write('''\n'''.join(line for line in annos_list	)	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					tuple[list, list]:

					'''simple docstring'''

					snake_case_									=				[]
					snake_case_									=				[]
					for label_file in glob.glob(os.path.join(__UpperCAmelCase,      '''*.txt'''	)	):
										snake_case_									=				label_file.split(os.sep	)[-1].rsplit('''.''',      1	)[0]
										with open(__UpperCAmelCase	) as in_file:
															snake_case_									=				in_file.readlines()
										snake_case_									=				os.path.join(__UpperCAmelCase,      F"{label_name}.jpg"	)

										snake_case_									=				[]
										for obj_list in obj_lists:
															snake_case_									=				obj_list.rstrip('''\n'''	).split(''' '''	)
															snake_case_									=				float(obj[1]	) - float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) - float(obj[4]	) / 2
															snake_case_									=				float(obj[1]	) + float(obj[3]	) / 2
															snake_case_									=				float(obj[2]	) + float(obj[4]	) / 2

															boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
										if not boxes:
															continue
										img_paths.append(__UpperCAmelCase	)
										labels.append(__UpperCAmelCase	)
					return img_paths, labels


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase = 0.0,      )		->					tuple[list, list, str]:

					'''simple docstring'''

					snake_case_									=				np.zeros([output_size[0], output_size[1], 3],      dtype=np.uinta	)
					snake_case_									=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_									=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					snake_case_									=				int(scale_x * output_size[1]	)
					snake_case_									=				int(scale_y * output_size[0]	)

					snake_case_									=				[]
					snake_case_									=				[]
					for i, index in enumerate(__UpperCAmelCase	):
										snake_case_									=				all_img_list[index]
										path_list.append(__UpperCAmelCase	)
										snake_case_									=				all_annos[index]
										snake_case_									=				cva.imread(__UpperCAmelCase	)
										if i == 0:  # top-left
															snake_case_									=				cva.resize(__UpperCAmelCase,      (divid_point_x, divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				bbox[1] * scale_x
																				snake_case_									=				bbox[2] * scale_y
																				snake_case_									=				bbox[3] * scale_x
																				snake_case_									=				bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										elif i == 1:  # top-right
															snake_case_									=				cva.resize(__UpperCAmelCase,      (output_size[1] - divid_point_x, divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				scale_x + bbox[1] * (1 - scale_x)
																				snake_case_									=				bbox[2] * scale_y
																				snake_case_									=				scale_x + bbox[3] * (1 - scale_x)
																				snake_case_									=				bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										elif i == 2:  # bottom-left
															snake_case_									=				cva.resize(__UpperCAmelCase,      (divid_point_x, output_size[0] - divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				bbox[1] * scale_x
																				snake_case_									=				scale_y + bbox[2] * (1 - scale_y)
																				snake_case_									=				bbox[3] * scale_x
																				snake_case_									=				scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
										else:  # bottom-right
															snake_case_									=				cva.resize(
															    __UpperCAmelCase,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
															snake_case_									=				img
															for bbox in img_annos:
																				snake_case_									=				scale_x + bbox[1] * (1 - scale_x)
																				snake_case_									=				scale_y + bbox[2] * (1 - scale_y)
																				snake_case_									=				scale_x + bbox[3] * (1 - scale_x)
																				snake_case_									=				scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
					if filter_scale > 0:
										snake_case_									=				[
										    anno
										    for anno in new_anno
										    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
										]

					return output_img, new_anno, path_list[0]


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert number_char > 1, "The number of character should greater than 1"
					snake_case_									=				ascii_lowercase + digits
					return "".join(random.choice(__UpperCAmelCase	) for _ in range(__UpperCAmelCase	)	)


if __name__ == "__main__":
							main()
							print('DONE ✅')

