





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
    _UpperCAmelCase				:       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.""",
    )
    _UpperCAmelCase				:       List[Any]	   =				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)












import flax.linen as nn
import jax
import jax.numpy as jnp

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





