


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						Any   =(KDPMaAncestralDiscreteScheduler,)
      UpperCamelCase__							:						Optional[Any]   =10






      def  lowerCamelCase (      self		:  Optional[int]    ,     **lowercase_		:  Any				)      ->		str:
             """simple docstring"""




             _lowerCamelCase :	Union[str, Any]						={
                 'num_train_timesteps': 1100,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_				)
             return config






      def  lowerCamelCase (      self		:  List[Any]				)      ->		List[Any]:
             """simple docstring"""




             for timesteps in [10, 50, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Optional[int]:
             """simple docstring"""




             for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,     [0.0002, 0.002, 0.02]				):
                    self.check_over_configs(beta_start=lowercase_    ,     beta_end=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Dict:
             """simple docstring"""




             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=lowercase_				)






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		int:
             """simple docstring"""




             if torch_device == "mps":
                    return
             _lowerCamelCase :	List[str]						=self.scheduler_classes[0]
             _lowerCamelCase :	int						=self.get_scheduler_config()
             _lowerCamelCase :	int						=scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps				)

             _lowerCamelCase :	str						=torch.manual_seed(0				)

             _lowerCamelCase :	str						=self.dummy_model()
             _lowerCamelCase :	Optional[Any]						=self.dummy_sample_deter * scheduler.init_noise_sigma
             _lowerCamelCase :	Dict						=sample.to(lowercase_				)

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

                    _lowerCamelCase :	Optional[int]						=model(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	str						=scheduler.step(lowercase_    ,     lowercase_    ,     lowercase_    ,     generator=lowercase_				)
                    _lowerCamelCase :	str						=output.prev_sample

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

             assert abs(result_sum.item() - 13849.3877				) < 1E-2
             assert abs(result_mean.item() - 18.0331				) < 5E-3






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		int:
             """simple docstring"""




             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowercase_				)






      def  lowerCamelCase (      self		:  Tuple				)      ->		int:
             """simple docstring"""




             if torch_device == "mps":
                    return
             _lowerCamelCase :	List[Any]						=self.scheduler_classes[0]
             _lowerCamelCase :	Tuple						=self.get_scheduler_config(prediction_type='v_prediction'				)
             _lowerCamelCase :	Tuple						=scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps				)

             _lowerCamelCase :	int						=self.dummy_model()
             _lowerCamelCase :	Dict						=self.dummy_sample_deter * scheduler.init_noise_sigma
             _lowerCamelCase :	List[str]						=sample.to(lowercase_				)

             _lowerCamelCase :	str						=torch.manual_seed(0				)

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

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

                    _lowerCamelCase :	List[str]						=scheduler.step(lowercase_    ,     lowercase_    ,     lowercase_    ,     generator=lowercase_				)
                    _lowerCamelCase :	List[Any]						=output.prev_sample

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

             assert abs(result_sum.item() - 328.9970				) < 1E-2
             assert abs(result_mean.item() - 0.4284				) < 1E-3






      def  lowerCamelCase (      self		:  List[Any]				)      ->		Optional[int]:
             """simple docstring"""




             if torch_device == "mps":
                    return
             _lowerCamelCase :	List[Any]						=self.scheduler_classes[0]
             _lowerCamelCase :	Optional[Any]						=self.get_scheduler_config()
             _lowerCamelCase :	Dict						=scheduler_class(**lowercase_				)

             scheduler.set_timesteps(self.num_inference_steps    ,     device=lowercase_				)
             _lowerCamelCase :	Any						=torch.manual_seed(0				)

             _lowerCamelCase :	Tuple						=self.dummy_model()
             _lowerCamelCase :	str						=self.dummy_sample_deter.to(lowercase_				) * scheduler.init_noise_sigma

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

                    _lowerCamelCase :	int						=model(lowercase_    ,     lowercase_				)

                    _lowerCamelCase :	int						=scheduler.step(lowercase_    ,     lowercase_    ,     lowercase_    ,     generator=lowercase_				)
                    _lowerCamelCase :	Optional[int]						=output.prev_sample

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

             assert abs(result_sum.item() - 13849.3818				) < 1E-1
             assert abs(result_mean.item() - 18.0331				) < 1E-3






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline


