'''simple docstring'''
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
      lowercase__  :      List[str]        =					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  __lowerCamelCase       (      _UpperCamelCase  :  Dict					):


            '''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)

      lowercase__  :      Optional[Any]        =					parser.parse_args()

      lowercase__  :      Union[str, Any]        =					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)






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

      return [s[i:] + s[:i] for i in range(len(_UpperCamelCase					)					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

      UpperCAmelCase_            =  all_rotations(_UpperCamelCase					)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      UpperCAmelCase_            =  {
          "bwt_string": "".join([word[-1] for word in rotations]					),
          "idx_original_string": rotations.index(_UpperCamelCase					),
      }
      return response



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter bwt_string type must be str.'''					)
      if not bwt_string:
            raise ValueError('''The parameter bwt_string must not be empty.'''					)
      try:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      except ValueError:
            raise TypeError(
                '''The parameter idx_original_string type must be int or passive'''
                ''' of cast to int.'''					)
      if idx_original_string < 0:
            raise ValueError('''The parameter idx_original_string must not be lower than 0.'''					)
      if idx_original_string >= len(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      Dict        =					reverse_bwt(result["bwt_string"], result["idx_original_string"])
      print(
          F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
          F'''we get original string \'{original_string}\''''
      )





