import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
			A__           =							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.""",
			)
			A__           =							parser.parse_args()

			A__           =							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 operator as op





def  _lowerCamelCase					(   a_				: Tuple):
			lowerCamelCase						:int						  =     []
			lowerCamelCase						:List[str]						  =     lambda a_						,			a_: int(x / y)  # noqa: E731 integer division operation
			lowerCamelCase						:Optional[int]						  =     {
			    '''^''': op.pow,
			    '''*''': op.mul,
			    '''/''': div,
			    '''+''': op.add,
			    '''-''': op.sub,
			}  # operators & their respective operation

			# print table header
			print('''Symbol'''.center(8)						,			'''Action'''.center(12)						,			'''Stack'''						,			sep=''' | ''')
			print('''-''' * (30 + len(a_)))

			for x in post_fix:
						if x.isdigit():  # if x in digit
									stack.append(a_)  # append x to stack
									# output in tabular format
									print(x.rjust(8)						,			('''push(''' + x + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')
						else:
									lowerCamelCase						:Optional[Any]						  =     stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8)						,			('''pop(''' + b + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')

									lowerCamelCase						:str						  =     stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8)						,			('''pop(''' + a + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')

									stack.append(
									    str(opr[x](int(a_)						,			int(a_))))  # evaluate the 2 values popped from stack & push result to stack
									# output in tabular format
									print(
									    x.rjust(8)						,			('''push(''' + a + x + b + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | '''						,			)

			return int(stack[0])


if __name__ == "__main__":
			A__           =							input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))



