

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,
)


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

lowerCAmelCase__       = "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=8			):
  UpperCAmelCase					      =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  UpperCAmelCase					      =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor






class 						__magic_name__    (       __SCREAMING_SNAKE_CASE    ):


 def __init__(						self		: str			,						lowerCAmelCase__		: UNetaDConditionModel			,						lowerCAmelCase__		: DDPMScheduler			,						lowerCAmelCase__		: VQModel			,						)    ->      str:
   super().__init__()

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


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Optional[int]    )    ->      Dict:




   if latents is None:
     UpperCAmelCase					      =  randn_tensor(_a			,						generator=_a			,						device=_a			,						dtype=_a    )
   else:
     if latents.shape != shape:
       raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
     UpperCAmelCase					      =  latents.to(_a    )

   UpperCAmelCase					      =  latents * scheduler.init_noise_sigma
   return latents


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: Dict=0    )    ->      Optional[int]:




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

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

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


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: List[Any]=0    )    ->      Optional[Any]:




   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					      =  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)

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

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


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def _UpperCamelCase	(						self		: Tuple    )    ->      Tuple:




   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		: str			,						lowerCAmelCase__		: Union[torch.FloatTensor, List[torch.FloatTensor]]			,						lowerCAmelCase__		: Union[torch.FloatTensor, List[torch.FloatTensor]]			,						lowerCAmelCase__		: int = 5_1_2			,						lowerCAmelCase__		: int = 5_1_2			,						lowerCAmelCase__		: int = 1_0_0			,						lowerCAmelCase__		: float = 4.0			,						lowerCAmelCase__		: int = 1			,						lowerCAmelCase__		: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						lowerCAmelCase__		: Optional[torch.FloatTensor] = None			,						lowerCAmelCase__		: Optional[str] = "pil"			,						lowerCAmelCase__		: bool = True			,						)    ->      int:
   UpperCAmelCase					      =  self._execution_device

   UpperCAmelCase					      =  guidance_scale > 1.0

   if isinstance(_a			,						_a    ):
     UpperCAmelCase					      =  torch.cat(_a			,						dim=0    )
   UpperCAmelCase					      =  image_embeds.shape[0] * num_images_per_prompt
   if isinstance(_a			,						_a    ):
     UpperCAmelCase					      =  torch.cat(_a			,						dim=0    )

   if do_classifier_free_guidance:
     UpperCAmelCase					      =  image_embeds.repeat_interleave(_a			,						dim=0    )
     UpperCAmelCase					      =  negative_image_embeds.repeat_interleave(_a			,						dim=0    )

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

   self.scheduler.set_timesteps(_a			,						device=_a    )
   UpperCAmelCase					      =  self.scheduler.timesteps

   UpperCAmelCase					      =  self.unet.config.in_channels

   UpperCAmelCase					      =  downscale_height_and_width(_a			,						_a			,						self.movq_scale_factor    )

   # create initial latent
   UpperCAmelCase					      =  self.prepare_latents(
       (batch_size, num_channels_latents, height, width)			,						image_embeds.dtype			,						_a			,						_a			,						_a			,						self.scheduler			,						)

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

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

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

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase					      =  self.scheduler.step(
         _a			,						_a			,						_a			,						generator=_a			,						)[0]
   # post-processing
   UpperCAmelCase					      =  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"]:
     UpperCAmelCase					      =  image * 0.5 + 0.5
     UpperCAmelCase					      =  image.clamp(0			,						1    )
     UpperCAmelCase					      =  image.cpu().permute(0			,						2			,						3			,						1    ).float().numpy()

   if output_type == "pil":
     UpperCAmelCase					      =  self.numpy_to_pil(_a    )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=_a    )






def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  while num > 0:
    digit_sum += num % 10
    num //= 10
  return digit_sum
def      _lowerCAmelCase(	__A = 100			):
  UpperCAmelCase					      =  1
  UpperCAmelCase					      =  2

  for i in range(2   ,       max_n + 1			):
    UpperCAmelCase					      =  pre_numerator
    UpperCAmelCase					      =  2 * i // 3 if i % 3 == 0 else 1
    UpperCAmelCase					      =  cur_numerator
    UpperCAmelCase					      =  e_cont * pre_numerator + temp

  return sum_digits(__A			)


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



