






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

import cva
import numpy as np

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







def 	SCREAMING_SNAKE_CASE							(					):
	snake_case__,		snake_case__					: Tuple  =    get_dataset(_lowercase	,    _lowercase  )
	for index in range(_lowercase  ):
		snake_case__					: Any  =    random.sample(range(len(_lowercase  )  )	,    4  )
		snake_case__,		snake_case__,		snake_case__					: Dict  =    update_image_and_anno(
		    _lowercase	,    _lowercase	,    _lowercase	,    _lowercase	,    _lowercase	,    filter_scale=_lowercase	,    )

		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		snake_case__					: Tuple  =    random_chars(32  )
		snake_case__					: Dict  =    path.split(os.sep  )[-1].rsplit("."	,    1  )[0]
		snake_case__					: str  =    F'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
		cva.imwrite(F'''{file_root}.jpg'''	,    _lowercase	,    [cva.IMWRITE_JPEG_QUALITY, 85]  )
		print(F'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''  )
		snake_case__					: int  =    []
		for anno in new_annos:
			snake_case__					: int  =    anno[3] - anno[1]
			snake_case__					: Dict  =    anno[4] - anno[2]
			snake_case__					: Dict  =    anno[1] + width / 2
			snake_case__					: List[str]  =    anno[2] + height / 2
			snake_case__					: List[Any]  =    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							(					snake_case_   :				str	,    snake_case_   :				Optional[int]  ):
	snake_case__					: Any  =    []
	snake_case__					: Tuple  =    []
	for label_file in glob.glob(os.path.join(_lowercase	,    "*.txt"  )  ):
		snake_case__					: Optional[Any]  =    label_file.split(os.sep  )[-1].rsplit("."	,    1  )[0]
		with open(_lowercase  ) as in_file:
			snake_case__					: Dict  =    in_file.readlines()
		snake_case__					: str  =    os.path.join(_lowercase	,    F'''{label_name}.jpg'''  )

		snake_case__					: str  =    []
		for obj_list in obj_lists:
			snake_case__					: Tuple  =    obj_list.rstrip("\n"  ).split(" "  )
			snake_case__					: List[str]  =    float(obj[1]  ) - float(obj[3]  ) / 2
			snake_case__					: int  =    float(obj[2]  ) - float(obj[4]  ) / 2
			snake_case__					: Union[str, Any]  =    float(obj[1]  ) + float(obj[3]  ) / 2
			snake_case__					: Dict  =    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							(					snake_case_   :				List[Any]	,    snake_case_   :				Dict	,    snake_case_   :				Optional[Any]	,    snake_case_   :				Any	,    snake_case_   :				str	,    snake_case_   :				Optional[int] = 0.0	,    ):
	snake_case__					: Union[str, Any]  =    np.zeros([output_size[0], output_size[1], 3]	,    dtype=np.uinta  )
	snake_case__					: Optional[int]  =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case__					: Optional[Any]  =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
	snake_case__					: Tuple  =    int(scale_x * output_size[1]  )
	snake_case__					: Optional[Any]  =    int(scale_y * output_size[0]  )

	snake_case__					: int  =    []
	snake_case__					: List[str]  =    []
	for i, index in enumerate(_lowercase  ):
		snake_case__					: Dict  =    all_img_list[index]
		path_list.append(_lowercase  )
		snake_case__					: Any  =    all_annos[index]
		snake_case__					: Dict  =    cva.imread(_lowercase  )
		if i == 0:  # top-left
			snake_case__					: int  =    cva.resize(_lowercase	,    (divid_point_x, divid_point_y)  )
			snake_case__					: Union[str, Any]  =    img
			for bbox in img_annos:
				snake_case__					: Tuple  =    bbox[1] * scale_x
				snake_case__					: Optional[int]  =    bbox[2] * scale_y
				snake_case__					: List[Any]  =    bbox[3] * scale_x
				snake_case__					: Any  =    bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
		elif i == 1:  # top-right
			snake_case__					: Union[str, Any]  =    cva.resize(_lowercase	,    (output_size[1] - divid_point_x, divid_point_y)  )
			snake_case__					: Union[str, Any]  =    img
			for bbox in img_annos:
				snake_case__					: List[str]  =    scale_x + bbox[1] * (1 - scale_x)
				snake_case__					: Any  =    bbox[2] * scale_y
				snake_case__					: List[Any]  =    scale_x + bbox[3] * (1 - scale_x)
				snake_case__					: str  =    bbox[4] * scale_y
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
		elif i == 2:  # bottom-left
			snake_case__					: Any  =    cva.resize(_lowercase	,    (divid_point_x, output_size[0] - divid_point_y)  )
			snake_case__					: List[str]  =    img
			for bbox in img_annos:
				snake_case__					: Union[str, Any]  =    bbox[1] * scale_x
				snake_case__					: Optional[Any]  =    scale_y + bbox[2] * (1 - scale_y)
				snake_case__					: Any  =    bbox[3] * scale_x
				snake_case__					: Tuple  =    scale_y + bbox[4] * (1 - scale_y)
				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
		else:  # bottom-right
			snake_case__					: Optional[int]  =    cva.resize(
			    _lowercase	,    (output_size[1] - divid_point_x, output_size[0] - divid_point_y)  )
			snake_case__					: Optional[int]  =    img
			for bbox in img_annos:
				snake_case__					: Tuple  =    scale_x + bbox[1] * (1 - scale_x)
				snake_case__					: Any  =    scale_y + bbox[2] * (1 - scale_y)
				snake_case__					: str  =    scale_x + bbox[3] * (1 - scale_x)
				snake_case__					: Optional[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:
		snake_case__					: List[str]  =    [
		    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							(					snake_case_   :				Optional[int]  ):
	assert number_char > 1, "The number of character should greater than 1"
	snake_case__					: List[str]  =    ascii_lowercase + digits
	return "".join(random.choice(_lowercase  ) for _ in range(_lowercase  )  )


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







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case   							=						{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


