





'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__magic_name__ :    Union[str, Any]			 =      logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__ :    Optional[Any]			 =      '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__=8							):







  '''simple docstring'''



  _snake_case  		=					height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _snake_case  		=					width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor




def 					snake_case_   (		SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__=5_12       ,						SCREAMING_SNAKE_CASE__=5_12							):







  '''simple docstring'''



  _snake_case  		=					pil_image.resize((w, h)       ,						resample=Image.BICUBIC       ,						reducing_gap=1							)
  _snake_case  		=					np.array(pil_image.convert("RGB"							)							)
  _snake_case  		=					arr.astype(np.floataa							) / 1_27.5 - 1
  _snake_case  		=					np.transpose(__a       ,						[2, 0, 1]							)
  _snake_case  		=					torch.from_numpy(__a							).unsqueeze(0							)
  return image




class    __SCREAMING_SNAKE_CASE						(    lowercase__      ):



     '''simple docstring'''






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

       self.register_modules(
           unet=lowercase_    , scheduler=lowercase_    , movq=lowercase_    , )
       _snake_case  		=					2 ** (len(self.movq.config.block_out_channels						) - 1)

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase						):
       # get the original timestep using init_timestep
       _snake_case  		=					min(int(num_inference_steps * strength						)    , lowercase_						)

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

       return timesteps, num_inference_steps - t_start

     def 						UpperCamelCase(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase=None						):
       if not isinstance(lowercase_    , (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(lowercase_						)}'''						)

       _snake_case  		=					image.to(device=lowercase_    , dtype=lowercase_						)

       _snake_case  		=					batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         _snake_case  		=					image

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

         elif isinstance(lowercase_    , lowercase_						):
           _snake_case  		=					[
               self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(lowercase_						)
           ]
           _snake_case  		=					torch.cat(lowercase_    , dim=0						)
         else:
           _snake_case  		=					self.movq.encode(lowercase_						).latent_dist.sample(lowercase_						)

         _snake_case  		=					self.movq.config.scaling_factor * init_latents

       _snake_case  		=					torch.cat([init_latents]    , dim=0						)

       _snake_case  		=					init_latents.shape
       _snake_case  		=					randn_tensor(lowercase_    , generator=lowercase_    , device=lowercase_    , dtype=lowercase_						)

       # get latents
       _snake_case  		=					self.scheduler.add_noise(lowercase_    , lowercase_    , lowercase_						)

       _snake_case  		=					init_latents

       return latents

     def 						UpperCamelCase(       self    , lowerCamelCase=0						):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("Please install accelerate via `pip install accelerate`"						)

       _snake_case  		=					torch.device(F'''cuda:{gpu_id}'''						)

       _snake_case  		=					[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(lowercase_    , lowercase_						)

     def 						UpperCamelCase(       self    , lowerCamelCase=0						):
       if is_accelerate_available() and is_accelerate_version(">="    , "0.17.0.dev0"						):
         from accelerate import cpu_offload_with_hook
       else:
         raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."						)

       _snake_case  		=					torch.device(F'''cuda:{gpu_id}'''						)

       if self.device.type != "cpu":
         self.to("cpu"    , silence_dtype_warnings=lowercase_						)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       _snake_case  		=					None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _snake_case		,		_snake_case  		=					cpu_offload_with_hook(lowercase_    , lowercase_    , prev_module_hook=lowercase_						)

       # We'll offload the last model manually.
       _snake_case  		=					hook

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 						UpperCamelCase(       self						):
       if not hasattr(self.unet    , "_hf_hook"						):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(lowercase_    , "_hf_hook"						)
             and hasattr(module._hf_hook    , "execution_device"						)
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device						)
       return self.device






     @torch.no_grad()
     @replace_example_docstring(lowercase_						)
     def __call__(       self    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase    , lowerCamelCase = 512    , lowerCamelCase = 512    , lowerCamelCase = 100    , lowerCamelCase = 4.0    , lowerCamelCase = 0.3    , lowerCamelCase = 1    , lowerCamelCase = None    , lowerCamelCase = "pil"    , lowerCamelCase = True    , ):
       _snake_case  		=					self._execution_device

       _snake_case  		=					guidance_scale > 1.0

       if isinstance(lowercase_    , lowercase_						):
         _snake_case  		=					torch.cat(lowercase_    , dim=0						)
       _snake_case  		=					image_embeds.shape[0]
       if isinstance(lowercase_    , lowercase_						):
         _snake_case  		=					torch.cat(lowercase_    , dim=0						)

       if do_classifier_free_guidance:
         _snake_case  		=					image_embeds.repeat_interleave(lowercase_    , dim=0						)
         _snake_case  		=					negative_image_embeds.repeat_interleave(lowercase_    , dim=0						)

       _snake_case  		=					torch.cat([negative_image_embeds, image_embeds]    , dim=0						).to(dtype=self.unet.dtype    , device=lowercase_						)

       if not isinstance(lowercase_    , lowercase_						):
         _snake_case  		=					[image]
       if not all(isinstance(lowercase_    , (PIL.Image.Image, torch.Tensor)						) for i in image						):
         raise ValueError(
             F'''Input is in incorrect format: {[type(lowercase_						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

       _snake_case  		=					torch.cat([prepare_image(lowercase_    , lowercase_    , lowercase_						) for i in image]    , dim=0						)
       _snake_case  		=					image.to(dtype=image_embeds.dtype    , device=lowercase_						)

       _snake_case  		=					self.movq.encode(lowercase_						)["latents"]
       _snake_case  		=					latents.repeat_interleave(lowercase_    , dim=0						)
       self.scheduler.set_timesteps(lowercase_    , device=lowercase_						)
       _snake_case		,		_snake_case  		=					self.get_timesteps(lowercase_    , lowercase_    , lowercase_						)
       _snake_case  		=					timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       _snake_case		,		_snake_case  		=					downscale_height_and_width(lowercase_    , lowercase_    , self.movq_scale_factor						)
       _snake_case  		=					self.prepare_latents(
           lowercase_    , lowercase_    , lowercase_    , lowercase_    , image_embeds.dtype    , lowercase_    , lowercase_						)
       for i, t in enumerate(self.progress_bar(lowercase_						)						):
         # expand the latents if we are doing classifier free guidance
         _snake_case  		=					torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

         _snake_case  		=					{"image_embeds": image_embeds}
         _snake_case  		=					self.unet(
             sample=lowercase_    , timestep=lowercase_    , encoder_hidden_states=lowercase_    , added_cond_kwargs=lowercase_    , return_dict=lowercase_    , )[0]

         if do_classifier_free_guidance:
           _snake_case		,		_snake_case  		=					noise_pred.split(latents.shape[1]    , dim=1						)
           _snake_case		,		_snake_case  		=					noise_pred.chunk(2						)
           _snake_case		,		_snake_case  		=					variance_pred.chunk(2						)
           _snake_case  		=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
           _snake_case  		=					torch.cat([noise_pred, variance_pred_text]    , dim=1						)

         if not (
             hasattr(self.scheduler.config    , "variance_type"						)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
           _snake_case		,		_snake_case  		=					noise_pred.split(latents.shape[1]    , dim=1						)

         # compute the previous noisy sample x_t -> x_t-1
         _snake_case  		=					self.scheduler.step(
             lowercase_    , lowercase_    , lowercase_    , generator=lowercase_    , )[0]

       # post-processing
       _snake_case  		=					self.movq.decode(lowercase_    , force_not_quantize=lowercase_						)["sample"]

       if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''						)

       if output_type in ["np", "pil"]:
         _snake_case  		=					image * 0.5 + 0.5
         _snake_case  		=					image.clamp(0    , 1						)
         _snake_case  		=					image.cpu().permute(0    , 2    , 3    , 1						).float().numpy()

       if output_type == "pil":
         _snake_case  		=					self.numpy_to_pil(lowercase_						)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=lowercase_						)






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       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   _lowercase(					__a				:						Optional[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)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       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)

