
'''simple docstring'''







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
						_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		:   int  ):
												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)

						_snake_case				:   str     =				parser.parse_args()

						_snake_case				:   Tuple     =				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 collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ) -> bool:
												_SCREAMING_SNAKE_CASE				= sqrt((x**2) + (y**2)  )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						_SCREAMING_SNAKE_CASE				= mean(
						    int(is_in_circle(uniform(-1.0	,			1.0  )	,			uniform(-1.0	,			1.0  )  )  )
						    for _ in range(_SCREAMING_SNAKE_CASE  )  )
						# The ratio of the area for circle to square is pi/4.
						_SCREAMING_SNAKE_CASE				= proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'  )
						print(F'The numpy value of pi is {pi}'  )
						print(F'The total error is {abs(pi - pi_estimate  )}'  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Callable[[float], float]	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0	,			):
						return mean(
						    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  ) for _ in range(_SCREAMING_SNAKE_CASE  )  ) * (max_value - min_value)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0  ):

						def identity_function(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return x

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (max_value * max_value - min_value * min_value) / 2

						print("******************"  )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {expected_value}'  )
						print(F'Total error is {abs(estimated_value - expected_value  )}'  )
						print("******************"  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						def function_to_integrate(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return sqrt(4.0 - x * x  )

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			0.0	,			2.0  )

						print("******************"  )
						print("Estimating pi using area_under_curve_estimator"  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {pi}'  )
						print(F'Total error is {abs(estimated_value - pi  )}'  )
						print("******************"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()