

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 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a , __a , __a , __a , )				->      Dict:






  """simple docstring"""





  super().__init__()

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




 def 						UpperCamelCase__							(self , __a				)				->      Union[str, Any]:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      Any:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self , __a				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       self.tokenizer(
      __a , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=__a , return_tensors='pt' , )

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

  UpperCAmelCase__           =       self.tokenizer(
      '' , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=__a , return_tensors='pt' , )

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

  return torch.cat([uncond_embedding, text_embedding]				)




 def __call__(self , __a , __a , __a = 0.3 , __a = 0.6 , __a = 0.5 , __a = 42 , __a = 50 , __a = 7.5 , )				->      Image.Image:






  """simple docstring"""





  UpperCAmelCase__           =       steps - int(kmin * steps				)
  UpperCAmelCase__           =       steps - int(kmax * steps				)

  UpperCAmelCase__           =       self.prep_text(__a				)

  self.scheduler.set_timesteps(__a				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       img.size
  UpperCAmelCase__           =       self.encode(__a				)

  torch.manual_seed(__a				)
  UpperCAmelCase__           =       torch.randn(
      (1, self.unet.config.in_channels, height // 8, width // 8) , ).to(self.device				)

  UpperCAmelCase__           =       self.scheduler.add_noise(
      __a , __a , timesteps=self.scheduler.timesteps[tmax] , )

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

  UpperCAmelCase__           =       self.scheduler.scale_model_input(__a , self.scheduler.timesteps[tmax]				)

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

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

  UpperCAmelCase__           =       self.scheduler.step(__a , self.scheduler.timesteps[tmax] , __a				).prev_sample

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

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

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

    UpperCAmelCase__           =       self.scheduler.scale_model_input(__a , __a				)

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

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

    UpperCAmelCase__           =       self.scheduler.step(__a , __a , __a				).prev_sample

  return self.decode(__a				)






from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 		UpperCamelCase_(    snake_case__:						Optional[int]     ) ->				Tuple:
 if is_torch_available():
  import torch

  if isinstance(snake_case__						,					torch.Tensor     ):
   return True

 return isinstance(snake_case__						,					np.ndarray     )







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       fields(self				)

  # Safety and consistency checks
  if not len(__a				):
   raise ValueError(F"{self.__class__.__name__} has no fields."				)

  UpperCAmelCase__           =       getattr(self , class_fields[0].name				)
  UpperCAmelCase__           =       all(getattr(self , field.name				) is None for field in class_fields[1:]				)

  if other_fields_are_none and isinstance(__a , __a				):
   for key, value in first_field.items():
    UpperCAmelCase__           =       value
  else:
   for field in class_fields:
    UpperCAmelCase__           =       getattr(self , field.name				)
    if v is not None:
     UpperCAmelCase__           =       v




 def __delitem__(self , *__a , **__a				)				->      Optional[int]:






  """simple docstring"""





  raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      str:






  """simple docstring"""





  raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      Optional[int]:






  """simple docstring"""





  raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance."				)




 def 						UpperCamelCase__							(self , *__a , **__a				)				->      Tuple:






  """simple docstring"""





  raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance."				)




 def __getitem__(self , __a				)				->      Tuple:






  """simple docstring"""





  if isinstance(__a , __a				):
   UpperCAmelCase__           =       dict(self.items()				)
   return inner_dict[k]
  else:
   return self.to_tuple()[k]




 def __setattr__(self , __a , __a				)				->      Any:






  """simple docstring"""





  if name in self.keys() and value is not None:
   # Don't call self.__setitem__ to avoid recursion errors
   super().__setitem__(__a , __a				)
  super().__setattr__(__a , __a				)




 def __setitem__(self , __a , __a				)				->      Union[str, Any]:






  """simple docstring"""





  super().__setitem__(__a , __a				)
  # Don't call self.__setattr__ to avoid recursion errors
  super().__setattr__(__a , __a				)




 def 						UpperCamelCase__							(self				)				->      Tuple[Any]:






  """simple docstring"""





  return tuple(self[k] for k in self.keys()				)



