






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						__UpperCamelCase   :   Tuple      =    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   :   str      =    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)







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			list:





				"""simple docstring"""


				if n_term == "":
								return []
				__lowerCamelCase									=       []
				for temp in range(int(UpperCamelCase__ ) ):
								series.append(F"""1/{temp + 1}""" if series else '1' )
				return series


if __name__ == "__main__":
					__A								=       input("Enter the last number (nth term) of the Harmonic Series")
					print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
					print(harmonic_series(nth_term))


