


def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



 if not isinstance(UpperCAmelCase_     ,			UpperCAmelCase_  ):
  raise ValueError('''double_factorial() only accepts integral values'''  )
 if num < 0:
  raise ValueError('''double_factorial() not defined for negative values'''  )
 UpperCamelCase__       :     Any 							= 1
 for i in range(UpperCAmelCase_     ,			0     ,			-2  ):
  value *= i
 return value


if __name__ == "__main__":
       import doctest

       doctest.testmod()








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,
)


SCREAMING_SNAKE_CASE_    :       int       =							logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE_    :       int       =							'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_=8		):
    a__  			=							height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    a__  			=							width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_=512 ,     UpperCAmelCase_=512		):
    a__  			=							pil_image.resize((w, h) ,     resample=Image.BICUBIC ,     reducing_gap=1		)
    a__  			=							np.array(pil_image.convert("""RGB"""		)		)
    a__  			=							arr.astype(np.floataa		) / 1_2_7.5 - 1
    a__  			=							np.transpose(UpperCAmelCase_ ,     [2, 0, 1]		)
    a__  			=							torch.from_numpy(UpperCAmelCase_		).unsqueeze(0		)
    return image







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def __init__(   self			:							Optional[Any]    ,					A			:							UNetaDConditionModel    ,					A			:							DDPMScheduler    ,					A			:							VQModel    ,					)      ->      Any:






         """simple docstring"""
         super().__init__()

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[int]    ,					A			:							int    ,					A			:							Any     )      ->      Dict:






         """simple docstring"""
         # 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 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							List[Any]    ,					A			:							Dict    ,					A			:							str    ,					A			:							Union[str, Any]    ,					A			:							Union[str, Any]    ,					A			:							str    ,					A			:							str=None     )      ->      List[Any]:






         """simple docstring"""
         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     )

         a__  			=							batch_size * num_images_per_prompt

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

         else:
             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."""     )

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

             a__  			=							self.movq.config.scaling_factor * init_latents

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

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

         # get latents
         a__  			=							self.scheduler.add_noise(A    ,					A    ,					A     )

         a__  			=							init_latents

         return latents

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Union[str, Any]=0     )      ->      Tuple:






         """simple docstring"""
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

         a__  			=							torch.device(f"""cuda:{gpu_id}"""     )

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[Any]=0     )      ->      Dict:






         """simple docstring"""
         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."""     )

         a__  			=							torch.device(f"""cuda:{gpu_id}"""     )

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

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

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

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      Optional[int]:






         """simple docstring"""
         if not hasattr(self.unet    ,					"""_hf_hook"""     ):
             return self.device
         for module in self.unet.modules():
             if (
                 hasattr(A    ,					"""_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(A     )
     def __call__(   self			:							int    ,					A			:							Union[torch.FloatTensor, List[torch.FloatTensor]]    ,					A			:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]    ,					A			:							Union[torch.FloatTensor, List[torch.FloatTensor]]    ,					A			:							torch.FloatTensor    ,					A			:							int = 5_12    ,					A			:							int = 5_12    ,					A			:							int = 1_00    ,					A			:							float = 4.0    ,					A			:							float = 0.3    ,					A			:							int = 1    ,					A			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,					A			:							Optional[str] = "pil"    ,					A			:							bool = True    ,					)      ->      List[Any]:






         """simple docstring"""
         a__  			=							self._execution_device

         a__  			=							guidance_scale > 1.0

         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )
         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )
         if isinstance(A    ,					A     ):
             a__  			=							torch.cat(A    ,					dim=0     )

         a__  			=							image_embeds.shape[0]

         if do_classifier_free_guidance:
             a__  			=							image_embeds.repeat_interleave(A    ,					dim=0     )
             a__  			=							negative_image_embeds.repeat_interleave(A    ,					dim=0     )
             a__  			=							hint.repeat_interleave(A    ,					dim=0     )

         a__  			=							torch.cat([negative_image_embeds, image_embeds]    ,					dim=0     ).to(dtype=self.unet.dtype    ,					device=A     )
         a__  			=							torch.cat([hint, hint]    ,					dim=0     ).to(dtype=self.unet.dtype    ,					device=A     )

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

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

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

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

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

             # compute the previous noisy sample x_t -> x_t-1

             a__  			=							self.scheduler.step(
                 A    ,					A    ,					A    ,					generator=A    ,					)[0]

         # post-processing
         a__  			=							self.movq.decode(A    ,					force_not_quantize=A     )["""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"]:
             a__  			=							image * 0.5 + 0.5
             a__  			=							image.clamp(0    ,					1     )
             a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     ).float().numpy()

         if output_type == "pil":
             a__  			=							self.numpy_to_pil(A     )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A     )






