
"""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,
)


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

UpperCAmelCase__        =					"\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    _UpperCAmelCase (					__lowerCamelCase  :			List[Any]					,   __lowerCamelCase  :			Dict					,   __lowerCamelCase  :			str=8						)	->   List[str]:
  _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__							(		__snake_case      ):







     def __init__(				self     :     Dict       , _lowerCamelCase     :     Optional[int]       , _lowerCamelCase     :     List[Any]       , _lowerCamelCase     :     Optional[int]       , ):
       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 	lowercase      (				self     :     str       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     Optional[int]       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Any       , _lowerCamelCase     :     Any     ):
       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 	lowercase      (				self     :     List[Any]       , _lowerCamelCase     :     Optional[int]=0     ):
       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 	lowercase      (				self     :     Tuple       , _lowerCamelCase     :     List[str]=0     ):
       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 	lowercase      (				self     :     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     :     Dict       , _lowerCamelCase     :     Optional[Any]       , _lowerCamelCase     :     Any       , _lowerCamelCase     :     str = 512       , _lowerCamelCase     :     Tuple = 512       , _lowerCamelCase     :     Optional[int] = 100       , _lowerCamelCase     :     List[Any] = 4.0       , _lowerCamelCase     :     int = 1       , _lowerCamelCase     :     Any = None       , _lowerCamelCase     :     List[Any] = None       , _lowerCamelCase     :     List[Any] = "pil"       , _lowerCamelCase     :     str = True       , ):
       _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_     )










def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    if not isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        raise ValueError('check_bouncy() accepts only integer arguments'				)
    _A     =							str(_SCREAMING_SNAKE_CASE				)
    _A     =							''.join(sorted(_SCREAMING_SNAKE_CASE				)				)
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 99				)							-> int:




    """simple docstring"""




    if not 0 < percent < 100:
        raise ValueError('solution() only accepts values from 0 to 100'				)
    _A     =							0
    _A     =							1

    while True:
        if check_bouncy(_SCREAMING_SNAKE_CASE				):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(f"{solution(99)}")





