


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,
)


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

__snake_case   							=						"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=8 )					-> Any:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase=5_12					,  _lowercase=5_12 )					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			pil_image.resize((w, h)					,  resample=Image.BICUBIC					,  reducing_gap=1 )
     __UpperCamelCase =			np.array(pil_image.convert('RGB' ) )
     __UpperCamelCase =			arr.astype(np.floataa ) / 1_27.5 - 1
     __UpperCamelCase =			np.transpose(_lowercase					,  [2, 0, 1] )
     __UpperCamelCase =			torch.from_numpy(_lowercase ).unsqueeze(0 )
     return image



class 	__lowerCamelCase			(__SCREAMING_SNAKE_CASE						):







       def __init__(			self: int,A_: List[Any],A_: Dict,A_: List[str],):






            '''simple docstring'''




            super().__init__()

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







       def 		snake_case_	(			self: Optional[int],A_: Dict,A_: Dict,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			min(int(num_inference_steps * strength					),_A					)

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

            return timesteps, num_inference_steps - t_start







       def 		snake_case_	(			self: int,A_: Dict,A_: Any,A_: List[str],A_: Tuple,A_: Tuple,A_: Optional[int],A_: List[Any]=None					):






            '''simple docstring'''




            if not isinstance(_A,(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(_A					)}'''					)

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

            __UpperCamelCase =			batch_size * num_images_per_prompt

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

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

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

                 __UpperCamelCase =			self.movq.config.scaling_factor * init_latents

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

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

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

            __UpperCamelCase =			init_latents

            return latents







       def 		snake_case_	(			self: List[Any],A_: str=0					):






            '''simple docstring'''




            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError('Please install accelerate via `pip install accelerate`'					)

            __UpperCamelCase =			torch.device(F'''cuda:{gpu_id}'''					)

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







       def 		snake_case_	(			self: Optional[int],A_: Optional[int]=0					):






            '''simple docstring'''




            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.'					)

            __UpperCamelCase =			torch.device(F'''cuda:{gpu_id}'''					)

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

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

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







       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            if not hasattr(self.unet,'_hf_hook'					):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(_A,'_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(_A					)
       def __call__(			self: Tuple,A_: List[Any],A_: Dict,A_: List[str],A_: Optional[int] = 512,A_: List[str] = 512,A_: Optional[int] = 100,A_: str = 4.0,A_: Dict = 0.3,A_: Tuple = 1,A_: List[Any] = None,A_: Tuple = "pil",A_: Union[str, Any] = True,):






            '''simple docstring'''




            __UpperCamelCase =			self._execution_device

            __UpperCamelCase =			guidance_scale > 1.0

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

            if do_classifier_free_guidance:
                 __UpperCamelCase =			image_embeds.repeat_interleave(_A,dim=0					)
                 __UpperCamelCase =			negative_image_embeds.repeat_interleave(_A,dim=0					)

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

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

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

            __UpperCamelCase =			self.movq.encode(_A					)["""latents"""]
            __UpperCamelCase =			latents.repeat_interleave(_A,dim=0					)
            self.scheduler.set_timesteps(_A,device=_A					)
            __UpperCamelCase =			self.get_timesteps(_A,_A,_A					)
            __UpperCamelCase =			timesteps[:1].repeat(batch_size * num_images_per_prompt					)
            __UpperCamelCase =			downscale_height_and_width(_A,_A,self.movq_scale_factor					)
            __UpperCamelCase =			self.prepare_latents(
                _A,_A,_A,_A,image_embeds.dtype,_A,_A					)
            for i, t in enumerate(self.progress_bar(_A					)					):
                 # expand the latents if we are doing classifier free guidance
                 __UpperCamelCase =			torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

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

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

                 # compute the previous noisy sample x_t -> x_t-1
                 __UpperCamelCase =			self.scheduler.step(
                     _A,_A,_A,generator=_A,)[0]

            # post-processing
            __UpperCamelCase =			self.movq.decode(_A,force_not_quantize=_A					)["""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"]:
                 __UpperCamelCase =			image * 0.5 + 0.5
                 __UpperCamelCase =			image.clamp(0,1					)
                 __UpperCamelCase =			image.cpu().permute(0,2,3,1					).float().numpy()

            if output_type == "pil":
                 __UpperCamelCase =			self.numpy_to_pil(_A					)

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=_A					)









"""simple docstring"""


from math import ceil, sqrt

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 100_0000     ):
						UpperCamelCase				:		int				   =			0

						for outer_width in range(3							,     (limit // 4) + 2     ):
												if outer_width**2 > limit:
																		UpperCamelCase				:		Optional[Any]				   =			max(ceil(sqrt(outer_width**2 - limit     )     )							,     1     )
												else:
																		UpperCamelCase				:		str				   =			1
												if (outer_width - hole_width_lower_bound) % 2:
																		hole_width_lower_bound += 1

												answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

						return answer


if __name__ == "__main__":
		print(f'''{solution() = }''')



