code: 
  '''simple docstring'''
  
  
  
  from typing import Optional
  
  import torch
  from PIL import Image
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils import (
      deprecate,
  )
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = 0.93       ,     lowerCamelCase__ = True       ,     )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       mixing_coeff
  									lowercase__							 =       leapfrog_steps
  
  									super().__init__()
  									self.register_modules(
  									    vae=lowerCamelCase__       ,     text_encoder=lowerCamelCase__       ,     tokenizer=lowerCamelCase__       ,     unet=lowerCamelCase__       ,     scheduler=lowerCamelCase__       ,     )
  
  									lowercase__							 =       2 ** (len(self.vae.config.block_out_channels       ) - 1)
  									lowercase__							 =       VaeImageProcessor(vae_scale_factor=self.vae_scale_factor       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.tokenizer(
  									    lowerCamelCase__       ,     padding="""max_length"""       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowerCamelCase__       ,     return_tensors="""pt"""       ,     )
  
  									lowercase__							 =       self.text_encoder(text_inputs.input_ids.to(self.device       )       ).last_hidden_state
  
  									lowercase__							 =       prompt_embeds.to(dtype=self.text_encoder.dtype       ,     device=self.device       )
  
  									if do_classifier_free_guidance:
  															lowercase__							 =       """""" if negative_prompt is None else negative_prompt
  
  															lowercase__							 =       self.tokenizer(
  															    lowerCamelCase__       ,     padding="""max_length"""       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowerCamelCase__       ,     return_tensors="""pt"""       ,     )
  
  															lowercase__							 =       self.text_encoder(uncond_input.input_ids.to(self.device       )       ).last_hidden_state
  
  															lowercase__							 =       torch.cat([negative_prompt_embeds, prompt_embeds]       )
  
  									return prompt_embeds
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.mixing_coeff * x + (1 - self.mixing_coeff) * y
  									lowercase__							 =       self.mixing_coeff * y + (1 - self.mixing_coeff) * x
  
  									return [x, y]
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
  									lowercase__							 =       (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff
  
  									return [x, y]
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       int(lowerCamelCase__       )
  
  									lowercase__							 =       self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod
  
  									return alpha_prod, 1 - alpha_prod
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
  									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )
  									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )
  
  									lowercase__							 =       (alpha_prod_t_prev / alpha_prod_t) ** 0.5
  									lowercase__							 =       -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
  
  									lowercase__							 =       (base - b_t * model_output) / a_t
  
  									return model_input, next_model_input.to(base.dtype       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
  									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )
  									lowercase__ ,						lowercase__							 =       self._get_alpha_and_beta(lowerCamelCase__       )
  
  									lowercase__							 =       (alpha_prod_t_prev / alpha_prod_t) ** 0.5
  									lowercase__							 =       -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
  									lowercase__							 =       a_t * base + b_t * model_output
  
  									return model_input, next_model_input.to(base.dtype       )
  
  
  
  			@torch.no_grad()
  			def A__ (					self       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       1 / self.vae.config.scaling_factor * latents
  									lowercase__							 =       self.vae.decode(lowerCamelCase__       ).sample
  									lowercase__							 =       (image / 2 + 0.5).clamp(0       ,     1       )
  									return image
  
  
  
  			@torch.no_grad()
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       guidance_scale > 1.0
  
  									lowercase__							 =       image.to(device=self.device       ,     dtype=text_embeds.dtype       )
  									lowercase__							 =       self.vae.encode(lowerCamelCase__       ).latent_dist.sample(lowerCamelCase__       )
  
  									lowercase__							 =       self.vae.config.scaling_factor * latent
  
  									lowercase__							 =       [latent.clone(), latent.clone()]
  
  									for i, t in tqdm(enumerate(lowerCamelCase__       )       ,     total=len(lowerCamelCase__       )       ):
  															lowercase__							 =       self.noise_mixing_layer(x=coupled_latents[0]       ,     y=coupled_latents[1]       )
  
  															# j - model_input index, k - base index
  															for j in range(2       ):
  																					lowercase__							 =       j ^ 1
  
  																					if self.leapfrog_steps:
  																											if i % 2 == 0:
  																																	lowercase__ ,						lowercase__							 =       j, k
  
  																					lowercase__							 =       coupled_latents[j]
  																					lowercase__							 =       coupled_latents[k]
  
  																					lowercase__							 =       torch.cat([model_input] * 2       ) if do_classifier_free_guidance else model_input
  
  																					lowercase__							 =       self.unet(lowerCamelCase__       ,     lowerCamelCase__       ,     encoder_hidden_states=lowerCamelCase__       ).sample
  
  																					if do_classifier_free_guidance:
  																											lowercase__ ,						lowercase__							 =       noise_pred.chunk(2       )
  																											lowercase__							 =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
  																					lowercase__ ,						lowercase__							 =       self.noise_step(
  																					    base=lowerCamelCase__       ,     model_input=lowerCamelCase__       ,     model_output=lowerCamelCase__       ,     timestep=lowerCamelCase__       ,     )
  
  																					lowercase__							 =       model_input
  
  									return coupled_latents
  
  
  
  			@torch.no_grad()
  			def __call__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = 3.0       ,     lowerCamelCase__ = 50       ,     lowerCamelCase__ = 0.8       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "pil"       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       guidance_scale > 1.0
  
  									lowercase__							 =       self.image_processor.preprocess(lowerCamelCase__       )
  
  									lowercase__							 =       self._encode_prompt(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       self._encode_prompt(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									self.scheduler.set_timesteps(lowerCamelCase__       ,     self.device       )
  
  									lowercase__							 =       num_inference_steps - int(num_inference_steps * strength       )
  									lowercase__							 =       self.scheduler.timesteps[t_limit:]
  									lowercase__							 =       fwd_timesteps.flip(0       )
  
  									lowercase__							 =       self.prepare_latents(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									for i, t in tqdm(enumerate(lowerCamelCase__       )       ,     total=len(lowerCamelCase__       )       ):
  															# j - model_input index, k - base index
  															for k in range(2       ):
  																					lowercase__							 =       k ^ 1
  
  																					if self.leapfrog_steps:
  																											if i % 2 == 1:
  																																	lowercase__ ,						lowercase__							 =       j, k
  
  																					lowercase__							 =       coupled_latents[j]
  																					lowercase__							 =       coupled_latents[k]
  
  																					lowercase__							 =       torch.cat([model_input] * 2       ) if do_classifier_free_guidance else model_input
  
  																					lowercase__							 =       self.unet(lowerCamelCase__       ,     lowerCamelCase__       ,     encoder_hidden_states=lowerCamelCase__       ).sample
  
  																					if do_classifier_free_guidance:
  																											lowercase__ ,						lowercase__							 =       noise_pred.chunk(2       )
  																											lowercase__							 =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
  																					lowercase__ ,						lowercase__							 =       self.denoise_step(
  																					    base=lowerCamelCase__       ,     model_input=lowerCamelCase__       ,     model_output=lowerCamelCase__       ,     timestep=lowerCamelCase__       ,     )
  
  																					lowercase__							 =       model_input
  
  															lowercase__							 =       self.denoise_mixing_layer(x=coupled_latents[0]       ,     y=coupled_latents[1]       )
  
  									# either one is fine
  									lowercase__							 =       coupled_latents[0]
  
  									if output_type not in ["latent", "pt", "np", "pil"]:
  															lowercase__							 =       (
  															    F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
  															    """`pil`, `np`, `pt`, `latent`"""
  															)
  															deprecate("""Unsupported output_type"""       ,     """1.0.0"""       ,     lowerCamelCase__       ,     standard_warn=lowerCamelCase__       )
  															lowercase__							 =       """np"""
  
  									if output_type == "latent":
  															lowercase__							 =       final_latent
  									else:
  															lowercase__							 =       self.decode_latents(lowerCamelCase__       )
  															lowercase__							 =       self.image_processor.postprocess(lowerCamelCase__       ,     output_type=lowerCamelCase__       )
  
  									return image
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  