


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					(DDIMScheduler,)
      snake_case__       =					(('''eta''', 0.0), ('''num_inference_steps''', 5_0))




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						={
             "num_train_timesteps": 10_00,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**A_)
         return config




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=self.scheduler_classes[0]
         UpperCamelCase__ :			str 						=self.get_scheduler_config(**A_)
         UpperCamelCase__ :			Dict 						=scheduler_class(**A_)

         UpperCamelCase__ :			Any 						=10, 0.0

         UpperCamelCase__ :			str 						=self.dummy_model()
         UpperCamelCase__ :			Union[str, Any] 						=self.dummy_sample_deter

         scheduler.set_timesteps(A_)

         for t in scheduler.timesteps:
            UpperCamelCase__ :			str 						=model(A_   ,			A_)
            UpperCamelCase__ :			List[str] 						=scheduler.step(A_   ,			A_   ,			A_   ,			A_).prev_sample

         return sample




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         for timesteps in [1_00, 5_00, 10_00]:
            self.check_over_configs(num_train_timesteps=A_)




      def    UpperCAmelCase							(		self)					->						Dict:







         """simple docstring"""







         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=A_)

         UpperCamelCase__ :			List[Any] 						=self.scheduler_classes[0]
         UpperCamelCase__ :			Any 						=self.get_scheduler_config(steps_offset=1)
         UpperCamelCase__ :			Any 						=scheduler_class(**A_)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps   ,			torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,			[0.0_02, 0.02, 0.2, 2]):
            self.check_over_configs(beta_start=A_   ,			beta_end=A_)




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=A_)




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=A_)




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=A_)




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=A_)




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         self.check_over_configs(thresholding=A_)
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=A_   ,			prediction_type=A_   ,			sample_max_value=A_   ,			)




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         for t in [1, 10, 49]:
            self.check_over_forward(time_step=A_)




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         for t, num_inference_steps in zip([1, 10, 50]   ,			[10, 50, 5_00]):
            self.check_over_forward(time_step=A_   ,			num_inference_steps=A_)




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         for t, eta in zip([1, 10, 49]   ,			[0.0, 0.5, 1.0]):
            self.check_over_forward(time_step=A_   ,			eta=A_)




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.scheduler_classes[0]
         UpperCamelCase__ :			List[Any] 						=self.get_scheduler_config()
         UpperCamelCase__ :			Tuple 						=scheduler_class(**A_)

         assert torch.sum(torch.abs(scheduler._get_variance(0   ,			0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20   ,			4_00) - 0.1_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80   ,			9_60) - 0.3_24_60)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0   ,			0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87   ,			4_86) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99   ,			9_98) - 0.02)) < 1E-5




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=self.full_loop()

         UpperCamelCase__ :			Tuple 						=torch.sum(torch.abs(A_))
         UpperCamelCase__ :			Optional[Any] 						=torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.full_loop(prediction_type="v_prediction")

         UpperCamelCase__ :			Optional[int] 						=torch.sum(torch.abs(A_))
         UpperCamelCase__ :			Optional[Any] 						=torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.full_loop(set_alpha_to_one=A_   ,			beta_start=0.01)
         UpperCamelCase__ :			Optional[int] 						=torch.sum(torch.abs(A_))
         UpperCamelCase__ :			Optional[Any] 						=torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.full_loop(set_alpha_to_one=A_   ,			beta_start=0.01)
         UpperCamelCase__ :			Tuple 						=torch.sum(torch.abs(A_))
         UpperCamelCase__ :			Any 						=torch.mean(torch.abs(A_))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






