





from typing import List, Optional, Union

import torch

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,
)


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

UpperCAmelCase_     							= '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			Optional[Any]=8      )      ->	List[str]:
    UpperCamelCase__							:				Tuple          =      height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCamelCase__							:				Optional[Any]          =      width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      str:






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

       self.register_modules(
           unet=__magic_name__,		scheduler=__magic_name__,		movq=__magic_name__,		)
       UpperCamelCase__							:				Dict          =      2 ** (len(self.movq.config.block_out_channels							) - 1)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       if latents is None:
           UpperCamelCase__							:				Union[str, Any]          =      randn_tensor(__magic_name__,		generator=__magic_name__,		device=__magic_name__,		dtype=__magic_name__							)
       else:
           if latents.shape != shape:
               raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"							)
           UpperCamelCase__							:				int          =      latents.to(__magic_name__							)

       UpperCamelCase__							:				List[Any]          =      latents * scheduler.init_noise_sigma
       return latents




   def       UpperCamelCase__  (    self,		__magic_name__=0							) ->      Tuple:






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

       UpperCamelCase__							:				List[Any]          =      torch.device(f"cuda:{gpu_id}"							)

       UpperCamelCase__							:				Union[str, Any]          =      [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
               cpu_offload(__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__=0							) ->      Any:






       """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.'''							)

       UpperCamelCase__							:				Optional[int]          =      torch.device(f"cuda:{gpu_id}"							)

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

       UpperCamelCase__							:				Any          =      None
       for cpu_offloaded_model in [self.unet, self.movq]:
           UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      cpu_offload_with_hook(__magic_name__,		__magic_name__,		prev_module_hook=__magic_name__							)

       # We'll offload the last model manually.
       UpperCamelCase__							:				Optional[Any]          =      hook




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       if not hasattr(self.unet,		'''_hf_hook'''							):
           return self.device
       for module in self.unet.modules():
           if (
               hasattr(__magic_name__,		'''_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(__magic_name__							)
   def __call__(    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = 512,		__magic_name__ = 512,		__magic_name__ = 100,		__magic_name__ = 4.0,		__magic_name__ = 1,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = "pil",		__magic_name__ = True,		) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self._execution_device

       UpperCamelCase__							:				Union[str, Any]          =      guidance_scale > 1.0

       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				List[Any]          =      torch.cat(__magic_name__,		dim=0							)
       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				List[Any]          =      torch.cat(__magic_name__,		dim=0							)
       if isinstance(__magic_name__,		__magic_name__							):
           UpperCamelCase__							:				Dict          =      torch.cat(__magic_name__,		dim=0							)

       UpperCamelCase__							:				List[Any]          =      image_embeds.shape[0] * num_images_per_prompt

       if do_classifier_free_guidance:
           UpperCamelCase__							:				str          =      image_embeds.repeat_interleave(__magic_name__,		dim=0							)
           UpperCamelCase__							:				int          =      negative_image_embeds.repeat_interleave(__magic_name__,		dim=0							)
           UpperCamelCase__							:				Optional[Any]          =      hint.repeat_interleave(__magic_name__,		dim=0							)

       UpperCamelCase__							:				List[str]          =      torch.cat([negative_image_embeds, image_embeds],		dim=0							).to(dtype=self.unet.dtype,		device=__magic_name__							)
       UpperCamelCase__							:				Any          =      torch.cat([hint, hint],		dim=0							).to(dtype=self.unet.dtype,		device=__magic_name__							)
       self.scheduler.set_timesteps(__magic_name__,		device=__magic_name__							)
       UpperCamelCase__							:				Tuple          =      self.scheduler.timesteps

       UpperCamelCase__							:				Dict          =      self.movq.config.latent_channels

       UpperCamelCase__   ,UpperCamelCase__							:				Tuple          =      downscale_height_and_width(__magic_name__,		__magic_name__,		self.movq_scale_factor							)

       # create initial latent
       UpperCamelCase__							:				Union[str, Any]          =      self.prepare_latents(
           (batch_size, num_channels_latents, height, width),		image_embeds.dtype,		__magic_name__,		__magic_name__,		__magic_name__,		self.scheduler,		)

       for i, t in enumerate(self.progress_bar(__magic_name__							)							):
           # expand the latents if we are doing classifier free guidance
           UpperCamelCase__							:				int          =      torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

           UpperCamelCase__							:				int          =      {'''image_embeds''': image_embeds, '''hint''': hint}
           UpperCamelCase__							:				List[Any]          =      self.unet(
               sample=__magic_name__,		timestep=__magic_name__,		encoder_hidden_states=__magic_name__,		added_cond_kwargs=__magic_name__,		return_dict=__magic_name__,		)[0]

           if do_classifier_free_guidance:
               UpperCamelCase__   ,UpperCamelCase__							:				Tuple          =      noise_pred.split(latents.shape[1],		dim=1							)
               UpperCamelCase__   ,UpperCamelCase__							:				Tuple          =      noise_pred.chunk(2							)
               UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      variance_pred.chunk(2							)
               UpperCamelCase__							:				Union[str, Any]          =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
               UpperCamelCase__							:				str          =      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"]
           ):
               UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      noise_pred.split(latents.shape[1],		dim=1							)

           # compute the previous noisy sample x_t -> x_t-1
           UpperCamelCase__							:				Optional[Any]          =      self.scheduler.step(
               __magic_name__,		__magic_name__,		__magic_name__,		generator=__magic_name__,		)[0]
       # post-processing
       UpperCamelCase__							:				Any          =      self.movq.decode(__magic_name__,		force_not_quantize=__magic_name__							)['''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"]:
           UpperCamelCase__							:				List[str]          =      image * 0.5 + 0.5
           UpperCamelCase__							:				Union[str, Any]          =      image.clamp(0,		1							)
           UpperCamelCase__							:				Dict          =      image.cpu().permute(0,		2,		3,		1							).float().numpy()

       if output_type == "pil":
           UpperCamelCase__							:				Dict          =      self.numpy_to_pil(__magic_name__							)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__magic_name__							)








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


if __name__ == "__main__":
  print(F'''{solution() = }''')

