

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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase     =    {
    '''configuration_fsmt''': ['''FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FSMTConfig'''],
    '''tokenization_fsmt''': ['''FSMTTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _UpperCamelCase     =    ['''FSMTForConditionalGeneration''', '''FSMTModel''', '''PretrainedFSMTModel''']


if TYPE_CHECKING:
 from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
 from .tokenization_fsmt import FSMTTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
 import sys

 _UpperCamelCase     =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



