



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

import cva
import numpy as np

# Parrameters
lowerCamelCase__			= (720, 1280)  # Height, Width
lowerCamelCase__			= (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCamelCase__			= 1 / 100
lowerCamelCase__			= """"""
lowerCamelCase__			= """"""
lowerCamelCase__			= """"""
lowerCamelCase__			= 250

def   lowerCAmelCase__						(							):
				"""simple docstring"""







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

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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







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

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

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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list[int]				,     _SCREAMING_SNAKE_CASE       :			tuple[int, int]				,     _SCREAMING_SNAKE_CASE       :			tuple[float, float]				,     _SCREAMING_SNAKE_CASE       :			float = 0.0				,     ):
				"""simple docstring"""







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

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







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


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





import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	List[Any]    =ComputeEnvironment.AMAZON_SAGEMAKER
							__lowerCamelCase	:	Optional[int]    =True
							__lowerCamelCase	:	List[Any]    ='ml.p3.2xlarge'
							__lowerCamelCase	:	Optional[int]    ='accelerate_sagemaker_execution_role'
							__lowerCamelCase	:	Optional[Any]    ='hf-sm'
							__lowerCamelCase	:	Any    ='us-east-1'
							__lowerCamelCase	:	Any    =1
							__lowerCamelCase	:	List[str]    ='accelerate-sagemaker-1'
							__lowerCamelCase	:	Union[str, Any]    ='1.6'
							__lowerCamelCase	:	str    ='4.4'
							__lowerCamelCase	:	List[str]    ='train.py'
							__lowerCamelCase	:	List[Any]    =[
							    '--model_name_or_path',
							    'bert',
							    '--do_train',
							    'False',
							    '--epochs',
							    '3',
							    '--learning_rate',
							    '5e-5',
							    '--max_steps',
							    '50.5',
							]
							__lowerCamelCase	:	Tuple    =[
							    '--model_name_or_path',
							    'bert',
							    '--do_train',
							    '--do_test',
							    'False',
							    '--do_predict',
							    '--epochs',
							    '3',
							    '--learning_rate',
							    '5e-5',
							    '--max_steps',
							    '50.5',
							]






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											__a       =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args	)
											assert isinstance(converted_args["""model_name_or_path"""]      ,				__lowercase	)
											assert isinstance(converted_args["""do_train"""]      ,				__lowercase	)
											assert isinstance(converted_args["""epochs"""]      ,				__lowercase	)
											assert isinstance(converted_args["""learning_rate"""]      ,				__lowercase	)
											assert isinstance(converted_args["""max_steps"""]      ,				__lowercase	)

											with pytest.raises(__lowercase	):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args	)
