





'''simple docstring'''




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							__UpperCAmelCase            =       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		(  lowerCamelCase_	):


									"""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)

							__UpperCAmelCase            =       parser.parse_args()

							__UpperCAmelCase            =       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 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			1  # To kept the Calculated Value
		# Since C(n, k) = C(n, n-k)
		if k > (n - k):
				SCREAMING_SNAKE_CASE :				Dict		=			n - k
		# Calculate C(n,k)
		for i in range(lowerCamelCase_	):
				result *= n - i
				result //= i + 1
		return result



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return binomial_coefficient(2 * node_count			,			lowerCamelCase_	) // (node_count + 1)



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if n < 0:
				raise ValueError("""factorial() not defined for negative values"""	)
		SCREAMING_SNAKE_CASE :				Dict		=			1
		for i in range(1			,			n + 1	):
				result *= i
		return result



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return catalan_number(lowerCamelCase_	) * factorial(lowerCamelCase_	)


if __name__ == "__main__":
							__UpperCAmelCase            =       int(input("""Enter the number of nodes: """).strip() or 0)
							if node_count <= 0:
														raise ValueError("""We need some nodes to work with.""")
							print(
							    f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
							    f'''binary trees and {catalan_number(node_count)} binary search trees.'''
							)



