





"""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,
)


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

a		:				Optional[int]  			=    '''
    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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]					, _lowercase		:					List[Any]					, _lowercase		:					Optional[int]=8      )				->List[str]:






	'''simple docstring'''






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







class  __UpperCamelCase       (		a__		):






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

								self.register_modules(
								    unet=lowerCAmelCase__   ,      scheduler=lowerCAmelCase__   ,      movq=lowerCAmelCase__   ,      )
								a   :	str			=       2 ** (len(self.movq.config.block_out_channels   ) - 1)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								if latents is None:
									a   :	List[Any]			=       randn_tensor(lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      device=lowerCAmelCase__   ,      dtype=lowerCAmelCase__   )
								else:
									if latents.shape != shape:
										raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
									a   :	str			=       latents.to(lowerCAmelCase__   )

								a   :	List[Any]			=       latents * scheduler.init_noise_sigma
								return latents






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

								a   :	Optional[int]			=       torch.device(f"""cuda:{gpu_id}"""   )

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






							def 					__a     (     self   ,      lowerCAmelCase__=0   )			->				Tuple:
								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."   )

								a   :	Union[str, Any]			=       torch.device(f"""cuda:{gpu_id}"""   )

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

								a   :	List[Any]			=       None
								for cpu_offloaded_model in [self.unet, self.movq]:
									a, a   :	Any			=       cpu_offload_with_hook(lowerCAmelCase__   ,      lowerCAmelCase__   ,      prev_module_hook=lowerCAmelCase__   )

								# We'll offload the last model manually.
								a   :	Union[str, Any]			=       hook






							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 					__a     (     self   )			->				Optional[int]:
								if not hasattr(self.unet   ,      "_hf_hook"   ):
									return self.device
								for module in self.unet.modules():
									if (
									    hasattr(lowerCAmelCase__   ,      "_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(lowerCAmelCase__   )
							def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = 512   ,      lowerCAmelCase__ = 512   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 4.0   ,      lowerCAmelCase__ = 1   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = "pil"   ,      lowerCAmelCase__ = True   ,      )			->				List[str]:
								a   :	Any			=       self._execution_device

								a   :	List[str]			=       guidance_scale > 1.0

								if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	str			=       torch.cat(lowerCAmelCase__   ,      dim=0   )
								a   :	List[Any]			=       image_embeds.shape[0] * num_images_per_prompt
								if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	List[str]			=       torch.cat(lowerCAmelCase__   ,      dim=0   )

								if do_classifier_free_guidance:
									a   :	Optional[int]			=       image_embeds.repeat_interleave(lowerCAmelCase__   ,      dim=0   )
									a   :	Any			=       negative_image_embeds.repeat_interleave(lowerCAmelCase__   ,      dim=0   )

								a   :	int			=       torch.cat([negative_image_embeds, image_embeds]   ,      dim=0   ).to(dtype=self.unet.dtype   ,      device=lowerCAmelCase__   )

								self.scheduler.set_timesteps(lowerCAmelCase__   ,      device=lowerCAmelCase__   )
								a   :	Any			=       self.scheduler.timesteps

								a   :	Any			=       self.unet.config.in_channels

								a, a   :	Dict			=       downscale_height_and_width(lowerCAmelCase__   ,      lowerCAmelCase__   ,      self.movq_scale_factor   )

								# create initial latent
								a   :	Union[str, Any]			=       self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)   ,      image_embeds.dtype   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      self.scheduler   ,      )

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

									a   :	Any			=       {"image_embeds": image_embeds}
									a   :	List[Any]			=       self.unet(
									    sample=lowerCAmelCase__   ,      timestep=lowerCAmelCase__   ,      encoder_hidden_states=lowerCAmelCase__   ,      added_cond_kwargs=lowerCAmelCase__   ,      return_dict=lowerCAmelCase__   ,      )[0]

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

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

								if output_type == "pil":
									a   :	Optional[int]			=       self.numpy_to_pil(lowerCAmelCase__   )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__   )









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


