




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

import cva
import numpy as np

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



def 			_SCREAMING_SNAKE_CASE						( )						->      None:





    """simple docstring"""

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

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __A         =	random_chars(3_2    )
        __A         =	path.split(os.sep    )[-1].rsplit("""."""			,				1    )[0]
        __A         =	f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
        cva.imwrite(f"{file_root}.jpg"			,				__lowercase			,				[cva.IMWRITE_JPEG_QUALITY, 8_5]    )
        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(__lowercase    )
        with open(f"{file_root}.txt"			,				"""w"""    ) as outfile:
            outfile.write("""\n""".join(line for line in annos_list    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			str    )						->      tuple[list, list]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    for label_file in glob.glob(os.path.join(__lowercase			,				"""*.txt"""    )    ):
        __A         =	label_file.split(os.sep    )[-1].rsplit("""."""			,				1    )[0]
        with open(__lowercase    ) as in_file:
            __A         =	in_file.readlines()
        __A         =	os.path.join(__lowercase			,				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(__lowercase    )
        labels.append(__lowercase    )
    return img_paths, labels



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list			,				__lowercase   :			list			,				__lowercase   :			list[int]			,				__lowercase   :			tuple[int, int]			,				__lowercase   :			tuple[float, float]			,				__lowercase   :			float = 0.0			,				)						->      tuple[list, list, str]:





    """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(__lowercase    ):
        __A         =	all_img_list[index]
        path_list.append(__lowercase    )
        __A         =	all_annos[index]
        __A         =	cva.imread(__lowercase    )
        if i == 0:  # top-left
            __A         =	cva.resize(__lowercase			,				(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(__lowercase			,				(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(__lowercase			,				(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(
                __lowercase			,				(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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      str:





    """simple docstring"""

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


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












from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			float | Decimal			,				__lowercase   :			float = 1_0**-1_0    )						->      float:





    """simple docstring"""

    __A         =	a
    while True:
        __A         =	Decimal(__lowercase    ) - (
            Decimal(eval(__lowercase    )    ) / Decimal(eval(str(diff(__lowercase    )    )    )    )  # noqa: S307
        )
        # This number dictates the accuracy of the answer
        if abs(eval(__lowercase    )    ) < precision:  # noqa: S307
            return float(__lowercase    )


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
  # Find root of polynomial
  print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
  # Find Square Root of 5
  print(f"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
  # Exponential Roots
  print(f"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")




