

from typing import Dict, Optional, Tuple

import torch







def _a				( SCREAMING_SNAKE_CASE	:    torch.Tensor	)					->					torch.Tensor:

 """simple docstring"""






 __lowerCAmelCase:     List[str]     			=   boundaries[1] - boundaries[0]
 __lowerCAmelCase:     int     			=   boundaries + step / 2
 __lowerCAmelCase:     List[str]     			=   torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1	)]					,      dim=0	)
 return bin_centers







def _a				( SCREAMING_SNAKE_CASE	:    torch.Tensor					,      SCREAMING_SNAKE_CASE	:    torch.Tensor					,      )					->					Tuple[torch.Tensor, torch.Tensor]:

 """simple docstring"""






 __lowerCAmelCase:     Tuple     			=   _calculate_bin_centers(SCREAMING_SNAKE_CASE	)
 return (
     torch.sum(aligned_distance_error_probs * bin_centers					,      dim=-1	),
     bin_centers[-1],
 )







def _a				( SCREAMING_SNAKE_CASE	:    torch.Tensor					,      SCREAMING_SNAKE_CASE	:    int = 31					,      SCREAMING_SNAKE_CASE	:    int = 64					,      **SCREAMING_SNAKE_CASE	:    Dict					,      )					->					Dict[str, torch.Tensor]:

 """simple docstring"""






 __lowerCAmelCase:     Tuple     			=   torch.linspace(0					,      SCREAMING_SNAKE_CASE					,      steps=(no_bins - 1)					,      device=logits.device	)

 __lowerCAmelCase:     Optional[int]     			=   torch.nn.functional.softmax(SCREAMING_SNAKE_CASE					,      dim=-1	)
 __lowerCAmelCase:     List[str]     			=   _calculate_expected_aligned_error(
     alignment_confidence_breaks=SCREAMING_SNAKE_CASE					,      aligned_distance_error_probs=SCREAMING_SNAKE_CASE					,      )

 return {
     "aligned_confidence_probs": aligned_confidence_probs,
     "predicted_aligned_error": predicted_aligned_error,
     "max_predicted_aligned_error": max_predicted_aligned_error,
 }







def _a				( SCREAMING_SNAKE_CASE	:    torch.Tensor					,      SCREAMING_SNAKE_CASE	:    Optional[torch.Tensor] = None					,      SCREAMING_SNAKE_CASE	:    int = 31					,      SCREAMING_SNAKE_CASE	:    int = 64					,      SCREAMING_SNAKE_CASE	:    float = 1E-8					,      **SCREAMING_SNAKE_CASE	:    List[Any]					,      )					->					torch.Tensor:

 """simple docstring"""






 if residue_weights is None:
  __lowerCAmelCase:     Optional[int]     			=   logits.new_ones(logits.shape[-2]	)

 __lowerCAmelCase:     Any     			=   torch.linspace(0					,      SCREAMING_SNAKE_CASE					,      steps=(no_bins - 1)					,      device=logits.device	)

 __lowerCAmelCase:     Union[str, Any]     			=   _calculate_bin_centers(SCREAMING_SNAKE_CASE	)
 torch.sum(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     int     			=   logits.shape[-2]
 __lowerCAmelCase:     Any     			=   max(SCREAMING_SNAKE_CASE					,      19	)

 __lowerCAmelCase:     Any     			=   1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

 __lowerCAmelCase:     int     			=   torch.nn.functional.softmax(SCREAMING_SNAKE_CASE					,      dim=-1	)

 __lowerCAmelCase:     List[str]     			=   1.0 / (1 + (bin_centers**2) / (da**2))
 __lowerCAmelCase:     str     			=   torch.sum(probs * tm_per_bin					,      dim=-1	)

 __lowerCAmelCase:     Tuple     			=   residue_weights / (eps + residue_weights.sum())
 __lowerCAmelCase:     str     			=   torch.sum(predicted_tm_term * normed_residue_mask					,      dim=-1	)

 __lowerCAmelCase:     int     			=   per_alignment * residue_weights

 __lowerCAmelCase:     Tuple     			=   (weighted == torch.max(SCREAMING_SNAKE_CASE	)).nonzero()[0]
 return per_alignment[tuple(SCREAMING_SNAKE_CASE	)]








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 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

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


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   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
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

         __lowerCAmelCase:     List[Any]     			=   (mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         __lowerCAmelCase:     Optional[Any]     			=   torch.cat([input] * 2      )

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

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





