




"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       a      =      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 						_snake_case					( _snake_case							: Union[str, Any]							)   -> 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)

       a      =      parser.parse_args()

       a      =      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"""

def 						_snake_case					( _snake_case							: str							)   -> str:






      '''simple docstring'''


      _A        =       ''
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 						_snake_case					( _snake_case							: str							)   -> dict[str, str]:






      '''simple docstring'''


      _A        =       [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      _A        =       remove_duplicates(key.upper()							)
      _A        =       len(_snake_case							)
      # First fill cipher with key characters
      _A        =       {alphabet[i]: char for i, char in enumerate(_snake_case							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_snake_case							)    ,  26							):
            _A        =       alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  _A        =       alphabet[i - offset]
            _A        =       char
      return cipher_alphabet






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      return "".join(cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      _A        =       {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( )   -> None:






      '''simple docstring'''


      _A        =       input('Enter message to encode or decode: '							).strip()
      _A        =       input('Enter keyword: '							).strip()
      _A        =       input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            _A        =       {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      _A        =       create_cipher_map(_snake_case							)
      print(func(_snake_case    ,  _snake_case							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

