


"""simple docstring"""
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      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			)      ->			Optional[Any]:
											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)

				__SCREAMING_SNAKE_CASE							:      List[str]						=      parser.parse_args()

				__SCREAMING_SNAKE_CASE							:      List[str]						=      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"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation







def   _a			( _SCREAMING_SNAKE_CASE = 8			)      ->			str:
							snake_case_          =						ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE			) for _ in range(_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						i // 3
							snake_case_          =						i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_          =						(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE			,	quotient + remainder			)
							    + random(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							    + random(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							)
							snake_case_          =						list(_SCREAMING_SNAKE_CASE			)
							shuffle(_SCREAMING_SNAKE_CASE			)
							return "".join(_SCREAMING_SNAKE_CASE			)

							# random is a generalised function for letters, characters and numbers







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE			) for _ in range(_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 8			)      ->			bool:

							if len(_SCREAMING_SNAKE_CASE			) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_          =						any(char in ascii_uppercase for char in password			)
							snake_case_          =						any(char in ascii_lowercase for char in password			)
							snake_case_          =						any(char in digits for char in password			)
							snake_case_          =						any(char in punctuation for char in password			)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters







def   _a			( )      ->			Union[str, Any]:
							snake_case_          =						int(input("""Please indicate the max length of your password: """			).strip()			)
							snake_case_          =						input(
							    """Please indicate the characters that must be in your password: """			).strip()
							print("""Password generated:"""			,	password_generator(_SCREAMING_SNAKE_CASE			)			)
							print(
							    """Alternative Password generated:"""			,	alternative_password_generator(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)			,	)
							print("""[If you are thinking of using this passsword, You better save it.]"""			)


if __name__ == "__main__":
				main()



