





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
    _UpperCAmelCase				:       str	   =				argparse.ArgumentParser()

    parser.add_argument(
        """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
    )
    # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
    parser.add_argument(
        """--original_config_file""",
        default=None,
        type=str,
        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(
        """--scheduler_type""",
        default="""pndm""",
        type=str,
        help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
    )
    parser.add_argument(
        """--pipeline_type""",
        default=None,
        type=str,
        help=(
            """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
            """. If `None` pipeline will be automatically inferred."""
        ),
    )
    parser.add_argument(
        """--image_size""",
        default=None,
        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(
        """--prediction_type""",
        default=None,
        type=str,
        help=(
            """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
            """ Diffusion v2 Base. Use 'v_prediction' 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.)""")
    parser.add_argument(
        """--stable_unclip""",
        type=str,
        default=None,
        required=False,
        help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
    )
    parser.add_argument(
        """--stable_unclip_prior""",
        type=str,
        default=None,
        required=False,
        help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
    )
    parser.add_argument(
        """--clip_stats_path""",
        type=str,
        help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
        required=False,
    )
    parser.add_argument(
        """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
    )
    parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
    parser.add_argument(
        """--vae_path""",
        type=str,
        default=None,
        required=False,
        help="""Set to a path, hub id to an already converted vae to not convert it again.""",
    )
    _UpperCAmelCase				:       List[Any]	   =				parser.parse_args()

    _UpperCAmelCase				:       str	   =				download_from_original_stable_diffusion_ckpt(
        checkpoint_path=args.checkpoint_path,
        original_config_file=args.original_config_file,
        image_size=args.image_size,
        prediction_type=args.prediction_type,
        model_type=args.pipeline_type,
        extract_ema=args.extract_ema,
        scheduler_type=args.scheduler_type,
        num_in_channels=args.num_in_channels,
        upcast_attention=args.upcast_attention,
        from_safetensors=args.from_safetensors,
        device=args.device,
        stable_unclip=args.stable_unclip,
        stable_unclip_prior=args.stable_unclip_prior,
        clip_stats_path=args.clip_stats_path,
        controlnet=args.controlnet,
        vae_path=args.vae_path,
    )

    if args.half:
        pipe.to(torch_dtype=torch.floataa)

    if args.controlnet:
        # only save the controlnet model
        pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
    else:
        pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







import math





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				if 0 not in (x, y):
								# We use the relation x^y = y*log10(x), where 10 is the base.
								return y * math.logaa(lowerCamelCase__    )
				else:
								if x == 0:  # 0 raised to any number is 0
												return 0
								elif y == 0:
												return 1  # any number raised to 0 is 1
				raise AssertionError("This should never happen"    )


if __name__ == "__main__":  # Main function
			# Read two numbers from input and typecast them to int using map function.
			# Here x is the base and y is the power.
			__A				='''Enter the base and the power separated by a comma: '''
			__A,						__A				=map(int, input(prompt).split(''','''))
			__A,						__A				=map(int, input(prompt).split(''','''))

			# We find the log of each number, using the function res(), which takes two
			# arguments.
			__A				=res(xa, ya)
			__A				=res(xa, ya)

			# We check for the largest number
			if resa > resa:
						print('''Largest number is''', xa, '''^''', ya)
			elif resa > resa:
						print('''Largest number is''', xa, '''^''', ya)
			else:
						print('''Both are equal''')


