code: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Any, Callable, Dict, List, Optional, Union
  
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionImgaImgPipeline,
      StableDiffusionInpaintPipelineLegacy,
      StableDiffusionPipeline,
      UNetaDConditionModel,
  )
  from diffusers.configuration_utils import FrozenDict
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.utils import deprecate, logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      int							  =    ['safety_checker', 'feature_extractor']
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = True    ,  )       ->  Tuple:
           super().__init__()
           if hasattr(scheduler.config    ,  'steps_offset'			) and scheduler.config.steps_offset != 1:
                __lowerCamelCase     :			Tuple              =	(
                    f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
                    f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
                    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
                    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
                    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
                    ' file'
                )
                deprecate('steps_offset!=1'    ,  '1.0.0'    ,  SCREAMING_SNAKE_CASE_    ,  standard_warn=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			str              =	dict(scheduler.config			)
                __lowerCamelCase     :			Optional[int]              =	1
                __lowerCamelCase     :			List[str]              =	FrozenDict(SCREAMING_SNAKE_CASE_			)
  
           self.register_modules(
               vae=SCREAMING_SNAKE_CASE_    ,  text_encoder=SCREAMING_SNAKE_CASE_    ,  tokenizer=SCREAMING_SNAKE_CASE_    ,  unet=SCREAMING_SNAKE_CASE_    ,  scheduler=SCREAMING_SNAKE_CASE_    ,  safety_checker=SCREAMING_SNAKE_CASE_    ,  feature_extractor=SCREAMING_SNAKE_CASE_    ,  )
           self.register_to_config(requires_safety_checker=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  Dict[str, Any]:
           return {k: getattr(self    ,  SCREAMING_SNAKE_CASE_			) for k in self.config.keys() if not k.startswith('_'			)}
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_ = "auto"			)       ->  Any:
           if slice_size == "auto":
                # half the attention head size is usually a good trade-off between
                # speed and memory
                __lowerCamelCase     :			Optional[int]              =	self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[Any]:
           self.enable_attention_slicing(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      @torch.no_grad()
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = 0.8    ,  SCREAMING_SNAKE_CASE_ = 50    ,  SCREAMING_SNAKE_CASE_ = 7.5    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = "pil"    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = 1    ,  )       ->  Any:
           # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
           return StableDiffusionInpaintPipelineLegacy(**self.components			)(
               prompt=SCREAMING_SNAKE_CASE_    ,  image=SCREAMING_SNAKE_CASE_    ,  mask_image=SCREAMING_SNAKE_CASE_    ,  strength=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=SCREAMING_SNAKE_CASE_    ,  guidance_scale=SCREAMING_SNAKE_CASE_    ,  negative_prompt=SCREAMING_SNAKE_CASE_    ,  num_images_per_prompt=SCREAMING_SNAKE_CASE_    ,  eta=SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  output_type=SCREAMING_SNAKE_CASE_    ,  return_dict=SCREAMING_SNAKE_CASE_    ,  callback=SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @torch.no_grad()
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = 0.8    ,  SCREAMING_SNAKE_CASE_ = 50    ,  SCREAMING_SNAKE_CASE_ = 7.5    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = "pil"    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = 1    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  str:
           # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
           return StableDiffusionImgaImgPipeline(**self.components			)(
               prompt=SCREAMING_SNAKE_CASE_    ,  image=SCREAMING_SNAKE_CASE_    ,  strength=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=SCREAMING_SNAKE_CASE_    ,  guidance_scale=SCREAMING_SNAKE_CASE_    ,  negative_prompt=SCREAMING_SNAKE_CASE_    ,  num_images_per_prompt=SCREAMING_SNAKE_CASE_    ,  eta=SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  output_type=SCREAMING_SNAKE_CASE_    ,  return_dict=SCREAMING_SNAKE_CASE_    ,  callback=SCREAMING_SNAKE_CASE_    ,  callback_steps=SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @torch.no_grad()
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 50    ,  SCREAMING_SNAKE_CASE_ = 7.5    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = "pil"    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = 1    ,  )       ->  Union[str, Any]:
           # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
           return StableDiffusionPipeline(**self.components			)(
               prompt=SCREAMING_SNAKE_CASE_    ,  height=SCREAMING_SNAKE_CASE_    ,  width=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=SCREAMING_SNAKE_CASE_    ,  guidance_scale=SCREAMING_SNAKE_CASE_    ,  negative_prompt=SCREAMING_SNAKE_CASE_    ,  num_images_per_prompt=SCREAMING_SNAKE_CASE_    ,  eta=SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  latents=SCREAMING_SNAKE_CASE_    ,  output_type=SCREAMING_SNAKE_CASE_    ,  return_dict=SCREAMING_SNAKE_CASE_    ,  callback=SCREAMING_SNAKE_CASE_    ,  callback_steps=SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Optional[Any]							  =    (KDPMaAncestralDiscreteScheduler,)
      lowerCamelCase						:      Tuple							  =    1_0
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			Union[str, Any]              =	{
               'num_train_timesteps': 11_00,
               'beta_start': 0.0_0_0_1,
               'beta_end': 0.0_2,
               'beta_schedule': 'linear',
           }
  
           config.update(**SCREAMING_SNAKE_CASE_			)
           return config
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
                self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_    ,  beta_end=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config()
           __lowerCamelCase     :			Optional[int]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			List[Any]              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Any              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Dict              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Union[str, Any]              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Any              =	output.prev_sample
  
           __lowerCamelCase     :			Tuple              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Optional[int]              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7			) < 1E-2
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 5E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Union[str, Any]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			Optional[Any]              =	self.get_scheduler_config(prediction_type='v_prediction'			)
           __lowerCamelCase     :			str              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps			)
  
           __lowerCamelCase     :			int              =	self.dummy_model()
           __lowerCamelCase     :			int              =	self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowerCamelCase     :			Dict              =	sample.to(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	torch.manual_seed(0			)
  
           for i, t in enumerate(scheduler.timesteps			):
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			str              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Tuple              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			int              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 3_2_8.9_9_7_0			) < 1E-2
           assert abs(result_mean.item() - 0.4_2_8_4			) < 1E-3
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           if torch_device == "mps":
                return
           __lowerCamelCase     :			Optional[int]              =	self.scheduler_classes[0]
           __lowerCamelCase     :			str              =	self.get_scheduler_config()
           __lowerCamelCase     :			Union[str, Any]              =	scheduler_class(**SCREAMING_SNAKE_CASE_			)
  
           scheduler.set_timesteps(self.num_inference_steps    ,  device=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			List[Any]              =	torch.manual_seed(0			)
  
           __lowerCamelCase     :			Optional[Any]              =	self.dummy_model()
           __lowerCamelCase     :			Dict              =	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
  
           for t in scheduler.timesteps:
                __lowerCamelCase     :			Any              =	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Optional[Any]              =	model(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
                __lowerCamelCase     :			Dict              =	scheduler.step(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_			)
                __lowerCamelCase     :			Union[str, Any]              =	output.prev_sample
  
           __lowerCamelCase     :			int              =	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
           __lowerCamelCase     :			Dict              =	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)
  
           assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8			) < 1E-1
           assert abs(result_mean.item() - 1_8.0_3_3_1			) < 1E-3
  
  
  
  
  
  
  