__lowercase							 =					{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}






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

import cva
import numpy as np

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



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		get_dataset(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for index in range(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		random.sample(range(len(SCREAMING_SNAKE_CASE	)	)  ,			4	)
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :str			  =		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'
        __UpperCamelCase     :List[Any]			  =		random_chars(32	)
        __UpperCamelCase     :List[str]			  =		path.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        __UpperCamelCase     :Tuple			  =		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}"""	)
        __UpperCamelCase     :Optional[Any]			  =		[]
        for anno in new_annos:
            __UpperCamelCase     :int			  =		anno[3] - anno[1]
            __UpperCamelCase     :Optional[int]			  =		anno[4] - anno[2]
            __UpperCamelCase     :int			  =		anno[1] + width / 2
            __UpperCamelCase     :List[str]			  =		anno[2] + height / 2
            __UpperCamelCase     :str			  =		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  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		[]
    __UpperCamelCase     :str			  =		[]
    for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE  ,			'''*.txt'''	)	):
        __UpperCamelCase     :Any			  =		label_file.split(os.sep	)[-1].rsplit('''.'''  ,			1	)[0]
        with open(SCREAMING_SNAKE_CASE	) as in_file:
            __UpperCamelCase     :str			  =		in_file.readlines()
        __UpperCamelCase     :Optional[int]			  =		os.path.join(SCREAMING_SNAKE_CASE  ,			f"""{label_name}.jpg"""	)

        __UpperCamelCase     :int			  =		[]
        for obj_list in obj_lists:
            __UpperCamelCase     :Optional[int]			  =		obj_list.rstrip('''\n'''	).split(''' '''	)
            __UpperCamelCase     :Any			  =		float(obj[1]	) - float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		float(obj[2]	) - float(obj[4]	) / 2
            __UpperCamelCase     :Dict			  =		float(obj[1]	) + float(obj[3]	) / 2
            __UpperCamelCase     :List[str]			  =		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  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 0.0  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		np.zeros([output_size[0], output_size[1], 3]  ,			dtype=np.uinta	)
    __UpperCamelCase     :List[Any]			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :int			  =		scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    __UpperCamelCase     :Optional[int]			  =		int(scale_x * output_size[1]	)
    __UpperCamelCase     :Any			  =		int(scale_y * output_size[0]	)

    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :Dict			  =		[]
    for i, index in enumerate(SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Any			  =		all_img_list[index]
        path_list.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Any			  =		all_annos[index]
        __UpperCamelCase     :Union[str, Any]			  =		cva.imread(SCREAMING_SNAKE_CASE	)
        if i == 0:  # top-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, divid_point_y)	)
            __UpperCamelCase     :Union[str, Any]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Union[str, Any]			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		bbox[2] * scale_y
                __UpperCamelCase     :int			  =		bbox[3] * scale_x
                __UpperCamelCase     :Union[str, Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 1:  # top-right
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, divid_point_y)	)
            __UpperCamelCase     :List[str]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :str			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Dict			  =		bbox[2] * scale_y
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :List[Any]			  =		bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        elif i == 2:  # bottom-left
            __UpperCamelCase     :str			  =		cva.resize(SCREAMING_SNAKE_CASE  ,			(divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Tuple			  =		bbox[1] * scale_x
                __UpperCamelCase     :Optional[Any]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Tuple			  =		bbox[3] * scale_x
                __UpperCamelCase     :Dict			  =		scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
        else:  # bottom-right
            __UpperCamelCase     :Optional[int]			  =		cva.resize(
                SCREAMING_SNAKE_CASE  ,			(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
            __UpperCamelCase     :Optional[int]			  =		img
            for bbox in img_annos:
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[1] * (1 - scale_x)
                __UpperCamelCase     :Optional[int]			  =		scale_y + bbox[2] * (1 - scale_y)
                __UpperCamelCase     :Optional[Any]			  =		scale_x + bbox[3] * (1 - scale_x)
                __UpperCamelCase     :int			  =		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:
        __UpperCamelCase     :List[Any]			  =		[
            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	):
    '''simple docstring'''







    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase     :Optional[Any]			  =		ascii_lowercase + digits
    return "".join(random.choice(SCREAMING_SNAKE_CASE	) for _ in range(SCREAMING_SNAKE_CASE	)	)


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





