





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,
)


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

__A     =    "\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_			(	__a     ,   __a     ,   __a=8		)					->		int:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowerCamelCase__:				List[Any]        =width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor






class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       UNetaDConditionModel	,			UpperCAmelCase_    :       DDPMScheduler	,			UpperCAmelCase_    :       VQModel	,			)	->int:



   '''simple docstring'''



   super().__init__()

   self.register_modules(
       unet=UpperCAmelCase_	,			scheduler=UpperCAmelCase_	,			movq=UpperCAmelCase_	,			)
   lowerCamelCase__:				List[str]        =2 ** (len(self.movq.config.block_out_channels) - 1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       int)	->List[str]:



   '''simple docstring'''



   if latents is None:
     lowerCamelCase__:				Any        =randn_tensor(UpperCAmelCase_	,			generator=UpperCAmelCase_	,			device=UpperCAmelCase_	,			dtype=UpperCAmelCase_)
   else:
     if latents.shape != shape:
       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
     lowerCamelCase__:				int        =latents.to(UpperCAmelCase_)

   lowerCamelCase__:				Any        =latents * scheduler.init_noise_sigma
   return latents

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       Any=0)	->int:



   '''simple docstring'''



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

   lowerCamelCase__:				Dict        =torch.device(F"""cuda:{gpu_id}""")

   lowerCamelCase__:				List[str]        =[
       self.unet,
       self.movq,
   ]
   for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
       cpu_offload(UpperCAmelCase_	,			UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Any=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.")

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

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

   lowerCamelCase__:				Optional[int]        =None
   for cpu_offloaded_model in [self.unet, self.movq]:
     lowerCamelCase__  , lowerCamelCase__:				str        =cpu_offload_with_hook(UpperCAmelCase_	,			UpperCAmelCase_	,			prev_module_hook=UpperCAmelCase_)

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

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



   '''simple docstring'''



   if not hasattr(self.unet	,			"_hf_hook"):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(UpperCAmelCase_	,			"_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(UpperCAmelCase_)
 def __call__(self    :       int	,			UpperCAmelCase_    :       Union[torch.FloatTensor, List[torch.FloatTensor]]	,			UpperCAmelCase_    :       Union[torch.FloatTensor, List[torch.FloatTensor]]	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       int = 512	,			UpperCAmelCase_    :       int = 512	,			UpperCAmelCase_    :       int = 100	,			UpperCAmelCase_    :       float = 4.0	,			UpperCAmelCase_    :       int = 1	,			UpperCAmelCase_    :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			UpperCAmelCase_    :       Optional[torch.FloatTensor] = None	,			UpperCAmelCase_    :       Optional[str] = "pil"	,			UpperCAmelCase_    :       bool = True	,			)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self._execution_device

   lowerCamelCase__:				Optional[Any]        =guidance_scale > 1.0

   if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Any        =torch.cat(UpperCAmelCase_	,			dim=0)
   if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Dict        =torch.cat(UpperCAmelCase_	,			dim=0)
   if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Union[str, Any]        =torch.cat(UpperCAmelCase_	,			dim=0)

   lowerCamelCase__:				Tuple        =image_embeds.shape[0] * num_images_per_prompt

   if do_classifier_free_guidance:
     lowerCamelCase__:				Dict        =image_embeds.repeat_interleave(UpperCAmelCase_	,			dim=0)
     lowerCamelCase__:				int        =negative_image_embeds.repeat_interleave(UpperCAmelCase_	,			dim=0)
     lowerCamelCase__:				Tuple        =hint.repeat_interleave(UpperCAmelCase_	,			dim=0)

   lowerCamelCase__:				Optional[int]        =torch.cat([negative_image_embeds, image_embeds]	,			dim=0).to(dtype=self.unet.dtype	,			device=UpperCAmelCase_)
   lowerCamelCase__:				Optional[int]        =torch.cat([hint, hint]	,			dim=0).to(dtype=self.unet.dtype	,			device=UpperCAmelCase_)
   self.scheduler.set_timesteps(UpperCAmelCase_	,			device=UpperCAmelCase_)
   lowerCamelCase__:				int        =self.scheduler.timesteps

   lowerCamelCase__:				int        =self.movq.config.latent_channels

   lowerCamelCase__  , lowerCamelCase__:				List[str]        =downscale_height_and_width(UpperCAmelCase_	,			UpperCAmelCase_	,			self.movq_scale_factor)

   # create initial latent
   lowerCamelCase__:				Tuple        =self.prepare_latents(
       (batch_size, num_channels_latents, height, width)	,			image_embeds.dtype	,			UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			self.scheduler	,			)

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

     lowerCamelCase__:				List[str]        ={"image_embeds": image_embeds, "hint": hint}
     lowerCamelCase__:				Any        =self.unet(
         sample=UpperCAmelCase_	,			timestep=UpperCAmelCase_	,			encoder_hidden_states=UpperCAmelCase_	,			added_cond_kwargs=UpperCAmelCase_	,			return_dict=UpperCAmelCase_	,			)[0]

     if do_classifier_free_guidance:
       lowerCamelCase__  , lowerCamelCase__:				Tuple        =noise_pred.split(latents.shape[1]	,			dim=1)
       lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =noise_pred.chunk(2)
       lowerCamelCase__  , lowerCamelCase__:				List[Any]        =variance_pred.chunk(2)
       lowerCamelCase__:				List[Any]        =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
       lowerCamelCase__:				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"]
     ):
       lowerCamelCase__  , lowerCamelCase__:				int        =noise_pred.split(latents.shape[1]	,			dim=1)

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

   if output_type == "pil":
     lowerCamelCase__:				Optional[Any]        =self.numpy_to_pil(UpperCAmelCase_)

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=UpperCAmelCase_)








from math import pow





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   )					->		tuple[int, int]:







  """simple docstring"""





  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  lowerCamelCase__:				Optional[Any]        =int(pow(__a     ,   __a		)		)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    lowerCamelCase__  , lowerCamelCase__:				int        =backtrack(
        __a     ,   __a     ,   current_number + 1     ,   __a     ,   __a		)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    lowerCamelCase__  , lowerCamelCase__:				Dict        =backtrack(
        __a     ,   __a     ,   current_number + 1     ,   __a     ,   __a		)
  return current_sum, solutions_count





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		int:







  """simple docstring"""





  if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
    raise ValueError(
        "Invalid input\n"
        "needed_sum must be between 1 and 1000, power between 2 and 10."		)

  return backtrack(__a     ,   __a     ,   1     ,   0     ,   0		)[1]  # Return the solutions_count


if __name__ == "__main__":
     import doctest

     doctest.testmod()

