





'''simple docstring'''




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,
)


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

_SCREAMING_SNAKE_CASE							     =			"\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			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Union[str, Any]=8		)		->  Optional[int]:
   snake_case 						=   height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case 						=   width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __init__(   self							:			Union[str, Any]   ,       __snake_case							:			UNetaDConditionModel   ,       __snake_case							:			DDPMScheduler   ,       __snake_case							:			VQModel   ,       )->	Optional[Any]:
        super().__init__()

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



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			Dict   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Tuple						)->	Dict:
        if latents is None:
           snake_case 						=   randn_tensor(__snake_case   ,       generator=__snake_case   ,       device=__snake_case   ,       dtype=__snake_case						)
        else:
           if latents.shape != shape:
              raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
           snake_case 						=   latents.to(__snake_case						)

        snake_case 						=   latents * scheduler.init_noise_sigma
        return latents



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Dict=0						)->	Any:
        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

        snake_case 						=   torch.device(f'''cuda:{gpu_id}'''						)

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



     def      lowerCAmelCase			(   self							:			Dict   ,       __snake_case							:			Optional[int]=0						)->	str:
        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."""						)

        snake_case 						=   torch.device(f'''cuda:{gpu_id}'''						)

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

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

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



     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def      lowerCAmelCase			(   self							:			str						)->	Dict:
        if not hasattr(self.unet   ,       """_hf_hook"""						):
           return self.device
        for module in self.unet.modules():
           if (
               hasattr(__snake_case   ,       """_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(__snake_case						)
     def __call__(   self							:			Tuple   ,       __snake_case							:			Union[torch.FloatTensor, List[torch.FloatTensor]]   ,       __snake_case							:			Union[torch.FloatTensor, List[torch.FloatTensor]]   ,       __snake_case							:			int = 5_12   ,       __snake_case							:			int = 5_12   ,       __snake_case							:			int = 1_00   ,       __snake_case							:			float = 4.0   ,       __snake_case							:			int = 1   ,       __snake_case							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,       __snake_case							:			Optional[torch.FloatTensor] = None   ,       __snake_case							:			Optional[str] = "pil"   ,       __snake_case							:			bool = True   ,       )->	Tuple:
        snake_case 						=   self._execution_device

        snake_case 						=   guidance_scale > 1.0

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

        if do_classifier_free_guidance:
           snake_case 						=   image_embeds.repeat_interleave(__snake_case   ,       dim=0						)
           snake_case 						=   negative_image_embeds.repeat_interleave(__snake_case   ,       dim=0						)

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

        self.scheduler.set_timesteps(__snake_case   ,       device=__snake_case						)
        snake_case 						=   self.scheduler.timesteps

        snake_case 						=   self.unet.config.in_channels

        snake_case						,    snake_case 						=   downscale_height_and_width(__snake_case   ,       __snake_case   ,       self.movq_scale_factor						)

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

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

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

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

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

        if output_type == "pil":
           snake_case 						=   self.numpy_to_pil(__snake_case						)

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=__snake_case						)








'''simple docstring'''




def 	__lowerCamelCase			(      __lowerCAmelCase       :							int		)		->  int:
   assert isinstance(__lowerCAmelCase      ,    __lowerCAmelCase		), F'''The input value of [n={number}] is not an integer'''

   if number == 1:
      return 2
   elif number < 1:
      snake_case 						=   F'''The input value of [n={number}] has to be > 0'''
      raise ValueError(__lowerCAmelCase		)
   else:
      snake_case 						=   sylvester(number - 1		)
      snake_case 						=   num - 1
      snake_case 						=   num
      return lower * upper + 1


if __name__ == "__main__":
 print(F"""The 8th number in Sylvester's sequence: {sylvester(8)}""")

