






'''simple docstring'''







def  lowerCAmelCase__		(					a_  :     list[int]			)   -> tuple[int, int]:
       # base case
       if len(a_			) == 1:
              return a[0], a[0]
       else:
              # split A into half.
              UpperCAmelCase__	:   List[Any]												=	a[: len(a_			) // 2]
              UpperCAmelCase__	:   str												=	a[len(a_			) // 2 :]

              # 2 sub problems, 1/2 of original size.
              UpperCAmelCase__							,							UpperCAmelCase__	:   Optional[Any]												=	max_difference(a_			)
              UpperCAmelCase__							,							UpperCAmelCase__	:   Union[str, Any]												=	max_difference(a_			)

              # get min of first and max of second
              # linear time
              UpperCAmelCase__	:   int												=	min(a_			)
              UpperCAmelCase__	:   Union[str, Any]												=	max(a_			)

              # 3 cases, either (small1, big1),
              # (min_first, max_second), (small2, big2)
              # constant comparisons
              if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
                     return smalla, biga
              elif biga - smalla > max_second - min_first:
                     return smalla, biga
              else:
                     return min_first, max_second


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		):
             super().__init__()

             self.register_modules(vae=_UpperCAmelCase  ,		text_encoder=_UpperCAmelCase  ,		tokenizer=_UpperCAmelCase  ,		unet=_UpperCAmelCase  ,		scheduler=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with torch.no_grad():
                    UpperCAmelCase__	:   Dict												=	self.vae.encode(tfms.ToTensor()(_UpperCAmelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
                    UpperCAmelCase__	:   int												=	0.1_82_15 * latent.latent_dist.sample()
             return latent




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   int												=	(1 / 0.1_82_15) * latent
             with torch.no_grad():
                    UpperCAmelCase__	:   Optional[Any]												=	self.vae.decode(_UpperCAmelCase						).sample
             UpperCAmelCase__	:   List[str]												=	(img / 2 + 0.5).clamp(0  ,		1						)
             UpperCAmelCase__	:   List[Any]												=	img.detach().cpu().permute(0  ,		2  ,		3  ,		1						).numpy()
             UpperCAmelCase__	:   Union[str, Any]												=	(img * 255).round().astype('''uint8'''						)
             return Image.fromarray(img[0]						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 _UpperCAmelCase  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   Tuple												=	self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

             UpperCAmelCase__	:   Dict												=	self.tokenizer(
                 ''''''  ,		padding='''max_length'''  ,		max_length=self.tokenizer.model_max_length  ,		truncation=_UpperCAmelCase  ,		return_tensors='''pt'''  ,		)

             UpperCAmelCase__	:   List[Any]												=	self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

             return torch.cat([uncond_embedding, text_embedding]						)




      def __call__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = 0.3  ,		_UpperCAmelCase = 0.6  ,		_UpperCAmelCase = 0.5  ,		_UpperCAmelCase = 42  ,		_UpperCAmelCase = 50  ,		_UpperCAmelCase = 7.5  ,		):
             UpperCAmelCase__	:   Any												=	steps - int(kmin * steps						)
             UpperCAmelCase__	:   Any												=	steps - int(kmax * steps						)

             UpperCAmelCase__	:   List[str]												=	self.prep_text(_UpperCAmelCase						)

             self.scheduler.set_timesteps(_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[str]												=	img.size
             UpperCAmelCase__	:   str												=	self.encode(_UpperCAmelCase						)

             torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)  ,		).to(self.device						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler.add_noise(
                 _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=self.scheduler.timesteps[tmax]  ,		)

             UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

             UpperCAmelCase__	:   Dict												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]						)

             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	self.unet(
                        _UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

             UpperCAmelCase__							,							UpperCAmelCase__	:   List[Any]												=	pred.chunk(2						)
             UpperCAmelCase__	:   List[str]												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

             UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		self.scheduler.timesteps[tmax]  ,		_UpperCAmelCase						).prev_sample

             for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
                    if i > tmax:
                           if i < tmin:  # layout generation phase
                                  UpperCAmelCase__	:   Dict												=	self.scheduler.add_noise(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		timesteps=_UpperCAmelCase  ,		)

                                  UpperCAmelCase__	:   Union[str, Any]												=	(mix_factor * latents) + (
                                      1 - mix_factor
                                  ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                  UpperCAmelCase__	:   Tuple												=	torch.cat([input] * 2						)

                           else:  # content generation phase
                                  UpperCAmelCase__	:   Any												=	torch.cat([latents] * 2						)

                           UpperCAmelCase__	:   str												=	self.scheduler.scale_model_input(_UpperCAmelCase  ,		_UpperCAmelCase						)

                           with torch.no_grad():
                                  UpperCAmelCase__	:   Any												=	self.unet(
                                      _UpperCAmelCase  ,		_UpperCAmelCase  ,		encoder_hidden_states=_UpperCAmelCase  ,		).sample

                           UpperCAmelCase__							,							UpperCAmelCase__	:   Tuple												=	pred.chunk(2						)
                           UpperCAmelCase__	:   str												=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

                           UpperCAmelCase__	:   Union[str, Any]												=	self.scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return self.decode(_UpperCAmelCase						)