



'''simple docstring'''


import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
						lowercase_      :				List[Any]   					=    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.''',
						)
						lowercase_      :				Dict   					=    parser.parse_args()

						lowercase_      :				Union[str, Any]   					=    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)







'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							[]
						lowercase        =							1

						while len(lowercase_						) < 1E6:
												constant.append(str(lowercase_						)						)
												i += 1

						lowercase        =							"""""".join(lowercase_						)

						return (
						    int(constant[0]						)
						    * int(constant[9]						)
						    * int(constant[99]						)
						    * int(constant[999]						)
						    * int(constant[9999]						)
						    * int(constant[9_9999]						)
						    * int(constant[99_9999]						)
						)


if __name__ == "__main__":
						print(solution())


