


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__   	=							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=8						)     ->			int:







 """simple docstring"""


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







class       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     )     ->   Tuple:
      super().__init__()

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      if latents is None:
       _lowercase							      =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}"       )
       _lowercase							      =latents.to(UpperCAmelCase       )

      _lowercase							      =latents * scheduler.init_noise_sigma
      return latents





     def      __A					(self		,     UpperCAmelCase=0       )     ->   Tuple:
      if is_accelerate_available():
       from accelerate import cpu_offload
      else:
       raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

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

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





     def      __A					(self		,     UpperCAmelCase=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.'''       )

      _lowercase							      =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)

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

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





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def      __A					(self       )     ->   Tuple:
      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		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase = 5_1_2		,     UpperCAmelCase = 5_1_2		,     UpperCAmelCase = 1_0_0		,     UpperCAmelCase = 4.0		,     UpperCAmelCase = 1		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "pil"		,     UpperCAmelCase = True		,     )     ->   Any:
      _lowercase							      =self._execution_device

      _lowercase							      =guidance_scale > 1.0

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

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

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

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

      _lowercase							      =self.unet.config.in_channels

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

      # create initial latent
      _lowercase							      =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
       _lowercase							      =torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

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

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

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

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase       )





def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 if b == 0:
  return 1
 if (b % 2) == 0:
  return actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
 else:
  return a * actual_power(__snake_case     ,						int(b / 2						)						) * actual_power(__snake_case     ,						int(b / 2						)						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if b < 0:
  return 1 / actual_power(__snake_case     ,						__snake_case						)
 return actual_power(__snake_case     ,						__snake_case						)


if __name__ == "__main__":
  print(power(-2, -3))

