




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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       AutoencoderKL       ,				snake_case    :       CLIPTextModel       ,				snake_case    :       CLIPTokenizer       ,				snake_case    :       UNetaDConditionModel       ,				snake_case    :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]       ,				):
          super().__init__()

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







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       Union[str, Any] ):
          with torch.no_grad():
              __UpperCamelCase     						=  self.vae.encode(tfms.ToTensor()(snake_case ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
              __UpperCamelCase     						=  0.1_82_15 * latent.latent_dist.sample()
          return latent







      def 					snake_case		(   self    :       int       ,				snake_case    :       Optional[Any] ):
          __UpperCamelCase     						=  (1 / 0.1_82_15) * latent
          with torch.no_grad():
              __UpperCamelCase     						=  self.vae.decode(snake_case ).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 					snake_case		(   self    :       int       ,				snake_case    :       Any ):
          __UpperCamelCase     						=  self.tokenizer(
              snake_case       ,				padding='''max_length'''       ,				max_length=self.tokenizer.model_max_length       ,				truncation=snake_case       ,				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=snake_case       ,				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    :       Dict       ,				snake_case    :       Image.Image       ,				snake_case    :       str       ,				snake_case    :       float = 0.3       ,				snake_case    :       float = 0.6       ,				snake_case    :       float = 0.5       ,				snake_case    :       int = 42       ,				snake_case    :       int = 50       ,				snake_case    :       float = 7.5       ,				):
          __UpperCamelCase     						=  steps - int(kmin * steps )
          __UpperCamelCase     						=  steps - int(kmax * steps )

          __UpperCamelCase     						=  self.prep_text(snake_case )

          self.scheduler.set_timesteps(snake_case )

          __UpperCamelCase						,					__UpperCamelCase     						=  img.size
          __UpperCamelCase     						=  self.encode(snake_case )

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

          __UpperCamelCase     						=  self.scheduler.add_noise(
              snake_case       ,				snake_case       ,				timesteps=self.scheduler.timesteps[tmax]       ,				)

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

          __UpperCamelCase     						=  self.scheduler.scale_model_input(snake_case       ,				self.scheduler.timesteps[tmax] )

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

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

          __UpperCamelCase     						=  self.scheduler.step(snake_case       ,				self.scheduler.timesteps[tmax]       ,				snake_case ).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(
                          snake_case       ,				snake_case       ,				timesteps=snake_case       ,				)

                      __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(snake_case       ,				snake_case )

                  with torch.no_grad():
                      __UpperCamelCase     						=  self.unet(
                          snake_case       ,				snake_case       ,				encoder_hidden_states=snake_case       ,				).sample

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

                  __UpperCamelCase     						=  self.scheduler.step(snake_case       ,				snake_case       ,				snake_case ).prev_sample

          return self.decode(snake_case )













"""simple docstring"""





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= c_float

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       return sizeof(self.value						)







def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 if dimension <= 0:
  __SCREAMING_SNAKE_CASE :			str												=       fixed_dimension

 dimension -= num_token_to_add
 return dimension




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							ParameterFormat		):

 '''simple docstring'''






 return num_parameters * dtype.size






def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

 try:
  return AutoProcessor.from_pretrained(lowercase_		)
 except (ValueError, OSError, KeyError):
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       None, None
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoTokenizer.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoFeatureExtractor.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass

  if tokenizer is not None and feature_extractor is not None:
   raise ValueError(
       F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''		)
  elif tokenizer is None and feature_extractor is None:
   return None
  elif tokenizer is not None:
   return tokenizer
  else:
   return feature_extractor

