

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_    :						str    		=      1 / 100
a_    :						Dict    		=      ""
a_    :						Optional[int]    		=      ""
a_    :						int    		=      ""
a_    :						Any    		=      250


def        __lowerCAmelCase    (       )		->							None:
	'''simple docstring'''


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

		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		SCREAMING_SNAKE_CASE								=			random_chars(32	)
		SCREAMING_SNAKE_CASE								=			path.split(os.sep	)[-1].rsplit('.'		,							1	)[0]
		SCREAMING_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}"""	)
		SCREAMING_SNAKE_CASE								=			[]
		for anno in new_annos:
			SCREAMING_SNAKE_CASE								=			anno[3] - anno[1]
			SCREAMING_SNAKE_CASE								=			anno[4] - anno[2]
			SCREAMING_SNAKE_CASE								=			anno[1] + width / 2
			SCREAMING_SNAKE_CASE								=			anno[2] + height / 2
			SCREAMING_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        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  str	)		->							tuple[list, list]:
	'''simple docstring'''


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

		SCREAMING_SNAKE_CASE								=			[]
		for obj_list in obj_lists:
			SCREAMING_SNAKE_CASE								=			obj_list.rstrip('\n'	).split(' '	)
			SCREAMING_SNAKE_CASE								=			float(obj[1]	) - float(obj[3]	) / 2
			SCREAMING_SNAKE_CASE								=			float(obj[2]	) - float(obj[4]	) / 2
			SCREAMING_SNAKE_CASE								=			float(obj[1]	) + float(obj[3]	) / 2
			SCREAMING_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        __lowerCAmelCase    (       _UpperCamelCase		:  list		,							_UpperCamelCase		:  list		,							_UpperCamelCase		:  list[int]		,							_UpperCamelCase		:  tuple[int, int]		,							_UpperCamelCase		:  tuple[float, float]		,							_UpperCamelCase		:  float = 0.0		,							)		->							tuple[list, list, str]:
	'''simple docstring'''


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

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


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


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




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)

