

'''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


lowercase__            =     logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    ["""safety_checker""", """feature_extractor"""]


     def __init__(						self					:	int	,     lowercase_					:	AutoencoderKL	,     lowercase_					:	CLIPTextModel	,     lowercase_					:	CLIPTokenizer	,     lowercase_					:	UNetaDConditionModel	,     lowercase_					:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,     lowercase_					:	StableDiffusionSafetyChecker	,     lowercase_					:	CLIPImageProcessor	,     lowercase_					:	bool = True	,     )		->      List[str]:
      super().__init__()
      if hasattr(scheduler.config	,     'steps_offset'			) and scheduler.config.steps_offset != 1:
       UpperCAmelCase			:      Union[str, Any]		   =					(
           f"""The configuration file of this scheduler: {scheduler} is outdated.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'	,     lowercase_	,     standard_warn=lowercase_			)
       UpperCAmelCase			:      Optional[Any]		   =					dict(scheduler.config			)
       UpperCAmelCase			:      Union[str, Any]		   =					1
       UpperCAmelCase			:      Optional[Any]		   =					FrozenDict(lowercase_			)

      self.register_modules(
          vae=lowercase_	,     text_encoder=lowercase_	,     tokenizer=lowercase_	,     unet=lowercase_	,     scheduler=lowercase_	,     safety_checker=lowercase_	,     feature_extractor=lowercase_	,     )
      self.register_to_config(requires_safety_checker=lowercase_			)


     @property
     def   UpperCAmelCase_			(						self					:	str			)		->      Dict[str, Any]:
      return {k: getattr(self	,     lowercase_			) for k in self.config.keys() if not k.startswith('_'			)}


     def   UpperCAmelCase_			(						self					:	List[Any]	,     lowercase_					:	Optional[Union[str, int]] = "auto"			)		->      Optional[Any]:
      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       UpperCAmelCase			:      List[Any]		   =					self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      self.enable_attention_slicing(lowercase_			)


     @torch.no_grad()
     def   UpperCAmelCase_			(						self					:	List[str]	,     lowercase_					:	Union[str, List[str]]	,     lowercase_					:	Union[torch.FloatTensor, PIL.Image.Image]	,     lowercase_					:	Union[torch.FloatTensor, PIL.Image.Image]	,     lowercase_					:	float = 0.8	,     lowercase_					:	Optional[int] = 50	,     lowercase_					:	Optional[float] = 7.5	,     lowercase_					:	Optional[Union[str, List[str]]] = None	,     lowercase_					:	Optional[int] = 1	,     lowercase_					:	Optional[float] = 0.0	,     lowercase_					:	Optional[torch.Generator] = None	,     lowercase_					:	Optional[str] = "pil"	,     lowercase_					:	bool = True	,     lowercase_					:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,     lowercase_					:	int = 1	,     )		->      Dict:
      # 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=lowercase_	,     image=lowercase_	,     mask_image=lowercase_	,     strength=lowercase_	,     num_inference_steps=lowercase_	,     guidance_scale=lowercase_	,     negative_prompt=lowercase_	,     num_images_per_prompt=lowercase_	,     eta=lowercase_	,     generator=lowercase_	,     output_type=lowercase_	,     return_dict=lowercase_	,     callback=lowercase_	,     )


     @torch.no_grad()
     def   UpperCAmelCase_			(						self					:	Any	,     lowercase_					:	Union[str, List[str]]	,     lowercase_					:	Union[torch.FloatTensor, PIL.Image.Image]	,     lowercase_					:	float = 0.8	,     lowercase_					:	Optional[int] = 50	,     lowercase_					:	Optional[float] = 7.5	,     lowercase_					:	Optional[Union[str, List[str]]] = None	,     lowercase_					:	Optional[int] = 1	,     lowercase_					:	Optional[float] = 0.0	,     lowercase_					:	Optional[torch.Generator] = None	,     lowercase_					:	Optional[str] = "pil"	,     lowercase_					:	bool = True	,     lowercase_					:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,     lowercase_					:	int = 1	,     **lowercase_					:	Optional[int]	,     )		->      Optional[Any]:
      # 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=lowercase_	,     image=lowercase_	,     strength=lowercase_	,     num_inference_steps=lowercase_	,     guidance_scale=lowercase_	,     negative_prompt=lowercase_	,     num_images_per_prompt=lowercase_	,     eta=lowercase_	,     generator=lowercase_	,     output_type=lowercase_	,     return_dict=lowercase_	,     callback=lowercase_	,     callback_steps=lowercase_	,     )




     @torch.no_grad()
     def   UpperCAmelCase_			(						self					:	List[Any]	,     lowercase_					:	Union[str, List[str]]	,     lowercase_					:	int = 512	,     lowercase_					:	int = 512	,     lowercase_					:	int = 50	,     lowercase_					:	float = 7.5	,     lowercase_					:	Optional[Union[str, List[str]]] = None	,     lowercase_					:	Optional[int] = 1	,     lowercase_					:	float = 0.0	,     lowercase_					:	Optional[torch.Generator] = None	,     lowercase_					:	Optional[torch.FloatTensor] = None	,     lowercase_					:	Optional[str] = "pil"	,     lowercase_					:	bool = True	,     lowercase_					:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,     lowercase_					:	int = 1	,     )		->      List[str]:
      # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
      return StableDiffusionPipeline(**self.components			)(
          prompt=lowercase_	,     height=lowercase_	,     width=lowercase_	,     num_inference_steps=lowercase_	,     guidance_scale=lowercase_	,     negative_prompt=lowercase_	,     num_images_per_prompt=lowercase_	,     eta=lowercase_	,     generator=lowercase_	,     latents=lowercase_	,     output_type=lowercase_	,     return_dict=lowercase_	,     callback=lowercase_	,     callback_steps=lowercase_	,     )







'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    (LMSDiscreteScheduler,)
     UpperCAmelCase_		:      Any    						=    10


     def   UpperCAmelCase_			(						self					:	str	,     **lowercase_					:	List[str]			)		->      str:
      UpperCAmelCase			:      Optional[Any]		   =					{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_			)
      return config


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Tuple:
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase_			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Tuple:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]			):
       self.check_over_configs(beta_start=lowercase_	,     beta_end=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      for t in [0, 500, 800]:
       self.check_over_forward(time_step=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Tuple		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Any		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      str		   =					self.dummy_model()
      UpperCAmelCase			:      str		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Optional[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Union[str, Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Optional[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3


     def   UpperCAmelCase_			(						self					:	str			)		->      Dict:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Dict		   =					self.get_scheduler_config(prediction_type='v_prediction'			)
      UpperCAmelCase			:      Optional[int]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      Optional[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      List[str]		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      List[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 0.0017			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Dict:
      UpperCAmelCase			:      Dict		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Optional[int]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      List[str]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps	,     device=lowercase_			)

      UpperCAmelCase			:      Tuple		   =					self.dummy_model()
      UpperCAmelCase			:      Tuple		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase			:      Any		   =					sample.to(lowercase_			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Dict		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Tuple		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[Any]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3




     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Union[str, Any]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Optional[Any]		   =					scheduler_class(**lowercase_	,     use_karras_sigmas=lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps	,     device=lowercase_			)

      UpperCAmelCase			:      List[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      Dict		   =					self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
      UpperCAmelCase			:      Union[str, Any]		   =					sample.to(lowercase_			)

      for t in scheduler.timesteps:
       UpperCAmelCase			:      Optional[int]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[str]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[Any]		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      Any		   =					output.prev_sample

      UpperCAmelCase			:      List[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 3812.9927			) < 2E-2
      assert abs(result_mean.item() - 4.9648			) < 1E-3




