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

import cva
import numpy as np

# Parrameters
lowerCamelCase      =(7_2_0, 1_2_8_0)  # Height, Width
lowerCamelCase      =(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCamelCase      =1 / 1_0_0
lowerCamelCase      =""
lowerCamelCase      =""
lowerCamelCase      =""
lowerCamelCase      =2_5_0


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__				,UpperCamelCase__    :   int					=				get_dataset(UpperCamelCase__ ,		UpperCamelCase__		)
      for index in range(UpperCamelCase__		):
            UpperCamelCase__    :   Optional[int]					=				random.sample(range(len(UpperCamelCase__		)		) ,		4		)
            UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   str					=				update_image_and_anno(
                UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		filter_scale=UpperCamelCase__ ,		)

            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            UpperCamelCase__    :   Any					=				random_chars(3_2		)
            UpperCamelCase__    :   Union[str, Any]					=				path.split(os.sep		)[-1].rsplit('''.''' ,		1		)[0]
            UpperCamelCase__    :   int					=				f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
            cva.imwrite(f'''{file_root}.jpg''' ,		UpperCamelCase__ ,		[cva.IMWRITE_JPEG_QUALITY, 8_5]		)
            print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''		)
            UpperCamelCase__    :   Optional[int]					=				[]
            for anno in new_annos:
                  UpperCamelCase__    :   Union[str, Any]					=				anno[3] - anno[1]
                  UpperCamelCase__    :   List[Any]					=				anno[4] - anno[2]
                  UpperCamelCase__    :   List[Any]					=				anno[1] + width / 2
                  UpperCamelCase__    :   Union[str, Any]					=				anno[2] + height / 2
                  UpperCamelCase__    :   Any					=				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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   int					=				[]
      UpperCamelCase__    :   Dict					=				[]
      for label_file in glob.glob(os.path.join(UpperCamelCase__ ,		'''*.txt'''		)		):
            UpperCamelCase__    :   List[str]					=				label_file.split(os.sep		)[-1].rsplit('''.''' ,		1		)[0]
            with open(UpperCamelCase__		) as in_file:
                  UpperCamelCase__    :   Tuple					=				in_file.readlines()
            UpperCamelCase__    :   Optional[int]					=				os.path.join(UpperCamelCase__ ,		f'''{label_name}.jpg'''		)

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

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


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




lowerCamelCase      ={
    "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",
}


