





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					A   : List[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.''',
					)
					A   : Union[str, Any]			=				parser.parse_args()

					A   : 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)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					A   : str			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



