'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

lowerCAmelCase_ : Tuple						  =      "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"


def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						str=8				)	->							Any:
      '''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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Union[str, Any]=512 ,		__lowerCamelCase			:						Tuple=512				)	->							int:
      '''simple docstring'''


      lowercase_     =      pil_image.resize((w, h) ,		resample=Image.BICUBIC ,		reducing_gap=1				)
      lowercase_     =      np.array(pil_image.convert("RGB"				)				)
      lowercase_     =      arr.astype(np.floataa				) / 1_2_7.5 - 1
      lowercase_     =      np.transpose(lowercase__ ,		[2, 0, 1]				)
      lowercase_     =      torch.from_numpy(lowercase__				).unsqueeze(0				)
      return image






class 							lowercase						(						__snake_case ):


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					Any:
         super().__init__()

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


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Any)      ->					Optional[Any]:
         lowercase_     =      min(int(num_inference_steps * strength)   ,		__UpperCamelCase)

         lowercase_     =      max(num_inference_steps - init_timestep   ,		0)
         lowercase_     =      self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	int=None)      ->					List[Any]:


         if not isinstance(__UpperCamelCase   ,		(torch.Tensor, PIL.Image.Image, list)):
               raise ValueError(
                   F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCamelCase)}')

         lowercase_     =      image.to(device=__UpperCamelCase   ,		dtype=__UpperCamelCase)

         lowercase_     =      batch_size * num_images_per_prompt

         if image.shape[1] == 4:
               lowercase_     =      image

         else:
               if isinstance(__UpperCamelCase   ,		__UpperCamelCase) and len(__UpperCamelCase) != batch_size:
                     raise ValueError(
                         F'You have passed a list of generators of length {len(__UpperCamelCase)}, but requested an effective batch'
                         F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

               elif isinstance(__UpperCamelCase   ,		__UpperCamelCase):
                     lowercase_     =      [
                         self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__UpperCamelCase)
                     ]
                     lowercase_     =      torch.cat(__UpperCamelCase   ,		dim=0)
               else:
                     lowercase_     =      self.movq.encode(__UpperCamelCase).latent_dist.sample(__UpperCamelCase)

               lowercase_     =      self.movq.config.scaling_factor * init_latents

         lowercase_     =      torch.cat([init_latents]   ,		dim=0)

         lowercase_     =      init_latents.shape
         lowercase_     =      randn_tensor(__UpperCamelCase   ,		generator=__UpperCamelCase   ,		device=__UpperCamelCase   ,		dtype=__UpperCamelCase)

         # get latents
         lowercase_     =      self.scheduler.add_noise(__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase)

         lowercase_     =      init_latents

         return latents


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	Optional[Any]=0)      ->					str:


         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 __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	Any=0)      ->					int:


         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 __UpperCAmelCase       (   self				:	List[str])      ->					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				:	List[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	int = 512   ,		__lowerCAmelCase				:	int = 512   ,		__lowerCAmelCase				:	Dict = 100   ,		__lowerCAmelCase				:	Optional[int] = 4.0   ,		__lowerCAmelCase				:	str = 0.3   ,		__lowerCAmelCase				:	Union[str, Any] = 1   ,		__lowerCAmelCase				:	Dict = None   ,		__lowerCAmelCase				:	Optional[int] = "pil"   ,		__lowerCAmelCase				:	str = True   ,		)      ->					str:
         lowercase_     =      self._execution_device

         lowercase_     =      guidance_scale > 1.0

         if isinstance(__UpperCamelCase   ,		__UpperCamelCase):
               lowercase_     =      torch.cat(__UpperCamelCase   ,		dim=0)
         lowercase_     =      image_embeds.shape[0]
         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)

         if not isinstance(__UpperCamelCase   ,		__UpperCamelCase):
               lowercase_     =      [image]
         if not all(isinstance(__UpperCamelCase   ,		(PIL.Image.Image, torch.Tensor)) for i in image):
               raise ValueError(
                   F'Input is in incorrect format: {[type(__UpperCamelCase) for i in image]}. Currently, we only support  PIL image and pytorch tensor')

         lowercase_     =      torch.cat([prepare_image(__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase) for i in image]   ,		dim=0)
         lowercase_     =      image.to(dtype=image_embeds.dtype   ,		device=__UpperCamelCase)

         lowercase_     =      self.movq.encode(__UpperCamelCase)["latents"]
         lowercase_     =      latents.repeat_interleave(__UpperCamelCase   ,		dim=0)
         self.scheduler.set_timesteps(__UpperCamelCase   ,		device=__UpperCamelCase)
         lowercase_ ,							lowercase_     =      self.get_timesteps(__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase)
         lowercase_     =      timesteps[:1].repeat(batch_size * num_images_per_prompt)
         lowercase_ ,							lowercase_     =      downscale_height_and_width(__UpperCamelCase   ,		__UpperCamelCase   ,		self.movq_scale_factor)
         lowercase_     =      self.prepare_latents(
             __UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		image_embeds.dtype   ,		__UpperCamelCase   ,		__UpperCamelCase)
         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)



'''simple docstring'''

def __a (	__lowerCamelCase			:						int = 200				)	->							int:
      '''simple docstring'''


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

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


if __name__ == "__main__":
       assert solution(200) == 73_682






