





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       a__:      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.',
       )
       a__:      str									=		parser.parse_args()

       a__:      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'''




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     return {key.lstrip('-'			): value for key, value in zip(unknown_args[::2] ,							unknown_args[1::2]			)}






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: int      =       ArgumentParser(
         'HuggingFace Datasets CLI tool' ,							usage='datasets-cli <command> [<args>]' ,							allow_abbrev=__snake_case			)
     __A						: Optional[Any]      =       parser.add_subparsers(help='datasets-cli command helpers'			)
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__snake_case			)
     EnvironmentCommand.register_subcommand(__snake_case			)
     TestCommand.register_subcommand(__snake_case			)
     RunBeamCommand.register_subcommand(__snake_case			)
     DummyDataCommand.register_subcommand(__snake_case			)

     # Parse args
     __A ,__A						: Optional[Any]      =       parser.parse_known_args()
     if not hasattr(__snake_case ,							'func'			):
          parser.print_help()
          exit(1			)
     __A						: Any      =       parse_unknown_args(__snake_case			)

     # Run
     __A						: List[Any]      =       args.func(__snake_case ,							**__snake_case			)
     service.run()


if __name__ == "__main__":
  main()