


"""simple docstring"""







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

import cva
import numpy as np

# Parrameters
lowerCAmelCase__				    =       (720, 1_280)  # Height, Width
lowerCAmelCase__				    =       (0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__				    =       1 / 100
lowerCAmelCase__				    =       ''''''
lowerCAmelCase__				    =       ''''''
lowerCAmelCase__				    =       ''''''
lowerCAmelCase__				    =       250
def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase					:					List[str]								=							get_dataset(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  for index in range(SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					List[str]								=							random.sample(range(len(SCREAMING_SNAKE_CASE    )    )    ,				4    )
    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							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'
    lowerCAmelCase					:					List[Any]								=							random_chars(3_2    )
    lowerCAmelCase					:					Dict								=							path.split(os.sep    )[-1].rsplit("."    ,				1    )[0]
    lowerCAmelCase					:					Union[str, Any]								=							f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
    cva.imwrite(f"""{file_root}.jpg"""    ,				SCREAMING_SNAKE_CASE    ,				[cva.IMWRITE_JPEG_QUALITY, 8_5]    )
    print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""    )
    lowerCAmelCase					:					List[Any]								=							[]
    for anno in new_annos:
      lowerCAmelCase					:					str								=							anno[3] - anno[1]
      lowerCAmelCase					:					List[Any]								=							anno[4] - anno[2]
      lowerCAmelCase					:					Optional[int]								=							anno[1] + width / 2
      lowerCAmelCase					:					List[str]								=							anno[2] + height / 2
      lowerCAmelCase					:					Optional[int]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







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

    lowerCAmelCase					:					Optional[int]								=							[]
    for obj_list in obj_lists:
      lowerCAmelCase					:					Optional[Any]								=							obj_list.rstrip("\n"    ).split(" "    )
      lowerCAmelCase					:					Union[str, Any]								=							float(obj[1]    ) - float(obj[3]    ) / 2
      lowerCAmelCase					:					Tuple								=							float(obj[2]    ) - float(obj[4]    ) / 2
      lowerCAmelCase					:					List[str]								=							float(obj[1]    ) + float(obj[3]    ) / 2
      lowerCAmelCase					:					Optional[Any]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   list    ,				SCREAMING_SNAKE_CASE   :   list    ,				SCREAMING_SNAKE_CASE   :   list[int]    ,				SCREAMING_SNAKE_CASE   :   tuple[int, int]    ,				SCREAMING_SNAKE_CASE   :   tuple[float, float]    ,				SCREAMING_SNAKE_CASE   :   float = 0.0    ,				):


  '''simple docstring'''







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

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


  '''simple docstring'''







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


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









"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     lowerCAmelCase__				    =       argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )
     parser.add_argument(
         '''--original_config_file''',
         type=str,
         required=True,
         help='''The YAML config file corresponding to the original architecture.''',
     )
     parser.add_argument(
         '''--num_in_channels''',
         default=None,
         type=int,
         help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
     )
     parser.add_argument(
         '''--image_size''',
         default=512,
         type=int,
         help=(
             '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
             ''' Base. Use 768 for Stable Diffusion v2.'''
         ),
     )
     parser.add_argument(
         '''--extract_ema''',
         action='''store_true''',
         help=(
             '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
             ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
             ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
         ),
     )
     parser.add_argument(
         '''--upcast_attention''',
         action='''store_true''',
         help=(
             '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
             ''' diffusion 2.1.'''
         ),
     )
     parser.add_argument(
         '''--from_safetensors''',
         action='''store_true''',
         help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
     )
     parser.add_argument(
         '''--to_safetensors''',
         action='''store_true''',
         help='''Whether to store pipeline in safetensors format or not.''',
     )
     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
     parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
     def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


       '''simple docstring'''







       if string == "True":
         return True
       elif string == "False":
         return False
       else:
         raise ValueError(f"""could not parse string as bool {string}"""    )

     parser.add_argument(
         '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
     )

     parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

     lowerCAmelCase__				    =       parser.parse_args()

     lowerCAmelCase__				    =       download_controlnet_from_original_ckpt(
         checkpoint_path=args.checkpoint_path,
         original_config_file=args.original_config_file,
         image_size=args.image_size,
         extract_ema=args.extract_ema,
         num_in_channels=args.num_in_channels,
         upcast_attention=args.upcast_attention,
         from_safetensors=args.from_safetensors,
         device=args.device,
         use_linear_projection=args.use_linear_projection,
         cross_attention_dim=args.cross_attention_dim,
     )





     controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





