




import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = 8   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					ascii_letters + digits + punctuation
			return "".join(secrets.choice(SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# Password Generator = full boot with random_number, random_letters, and
			# random_character FUNCTIONS
			# Put your code here...
			i -= len(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					i // 3
			SCREAMING_SNAKE_CASE           =					i % 3
			# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
			#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
			SCREAMING_SNAKE_CASE           =					(
			    chars_incl
			    + random(SCREAMING_SNAKE_CASE					,	quotient + remainder   )
			    + random(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			    + random(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			)
			SCREAMING_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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return "".join(secrets.choice(SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			pass  # Put your code here...
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			pass  # Put your code here...
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			pass  # Put your code here...
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = 8   ):
			'''simple docstring'''

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

			SCREAMING_SNAKE_CASE           =					any(char in ascii_uppercase for char in password   )
			SCREAMING_SNAKE_CASE           =					any(char in ascii_lowercase for char in password   )
			SCREAMING_SNAKE_CASE           =					any(char in digits for char in password   )
			SCREAMING_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 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					int(input("""Please indicate the max length of your password: """   ).strip()   )
			SCREAMING_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()












import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							Tuple							=  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=5_1_2,
		    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_    (    SCREAMING_SNAKE_CASE   ):
					'''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						:							Union[str, Any]							=  parser.parse_args()

		__A						:							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)






