



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowercase						  =		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  List[str]:
    if isinstance(UpperCamelCase_ ,       torch.Tensor	):
        return image
    elif isinstance(UpperCamelCase_ ,       PIL.Image.Image	):
        A__		 =					[image]

    A__		 =					[trans(img.convert('''RGB'''	)	) for img in image]
    A__		 =					torch.stack(UpperCamelCase_	)
    return image



class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         A__		 =					DDIMScheduler.from_config(scheduler.config)

         self.register_modules(unet=a__ ,						scheduler=a__)




     def 		snake_case_		(				self ,						a__):
         if strength < 0 or strength > 1:
             raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}")




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         # get the original timestep using init_timestep
         A__		 =					min(int(num_inference_steps * strength) ,						a__)

         A__		 =					max(num_inference_steps - init_timestep ,						0)
         A__		 =					self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__=None):
         if not isinstance(a__ ,						(torch.Tensor, PIL.Image.Image, list)):
             raise ValueError(
                 F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__)}")

         A__		 =					image.to(device=a__ ,						dtype=a__)

         if isinstance(a__ ,						a__) and len(a__) != batch_size:
             raise ValueError(
                 F"You have passed a list of generators of length {len(a__)}, but requested an effective batch"
                 F" size of {batch_size}. Make sure the batch size matches the length of the generators.")

         A__		 =					init_latents.shape
         A__		 =					randn_tensor(a__ ,						generator=a__ ,						device=a__ ,						dtype=a__)

         # get latents
         print('''add noise to latents at timestep''' ,						a__)
         A__		 =					self.scheduler.add_noise(a__ ,						a__ ,						a__)
         A__		 =					init_latents

         return latents




     @torch.no_grad()
     def __call__(				self ,						a__ = None ,						a__ = 0.8 ,						a__ = 1 ,						a__ = None ,						a__ = 0.0 ,						a__ = 5_0 ,						a__ = None ,						a__ = "pil" ,						a__ = True ,						):
         self.check_inputs(a__)

         # 2. Preprocess image
         A__		 =					preprocess(a__)

         # 3. set timesteps
         self.scheduler.set_timesteps(a__ ,						device=self.device)
         A__ ,	A__		 =					self.get_timesteps(a__ ,						a__ ,						self.device)
         A__		 =					timesteps[:1].repeat(a__)

         # 4. Prepare latent variables
         A__		 =					self.prepare_latents(a__ ,						a__ ,						a__ ,						self.unet.dtype ,						self.device ,						a__)
         A__		 =					latents

         # 5. Denoising loop
         for t in self.progress_bar(a__):
             # 1. predict noise model_output
             A__		 =					self.unet(a__ ,						a__).sample

             # 2. predict previous mean of image x_t-1 and add variance depending on eta
             # eta corresponds to η in paper and should be between [0, 1]
             # do x_t -> x_t-1
             A__		 =					self.scheduler.step(
                 a__ ,						a__ ,						a__ ,						eta=a__ ,						use_clipped_model_output=a__ ,						generator=a__ ,						).prev_sample

         A__		 =					(image / 2 + 0.5).clamp(0 ,						1)
         A__		 =					image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()
         if output_type == "pil":
             A__		 =					self.numpy_to_pil(a__)

         if not return_dict:
             return (image, latent_timestep.item())

         return ImagePipelineOutput(images=a__)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )
   parser.add_argument(
       "--original_config_file",
       type=str,
       required=True,
       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(
       "--image_size",
       default=512,
       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(
       "--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.)")



   def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       if string == "True":
           return True
       elif string == "False":
           return False
       else:
           raise ValueError(f"could not parse string as bool {string}"	)

   parser.add_argument(
       "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
   )

   parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		download_controlnet_from_original_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       extract_ema=args.extract_ema,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       use_linear_projection=args.use_linear_projection,
       cross_attention_dim=args.cross_attention_dim,
   )



   controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
