
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Any        =    (EulerDiscreteScheduler,)
      A__	:			int        =    1_0




      def 		snake_case_			(      self		:       Optional[int]				,       **_snake_case		:       List[Any]				):
          __lowercase		:			str	    =       {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Optional[Any]				):
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,       [0.00_02, 0.0_02, 0.02]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			int	    =       self.get_scheduler_config()
          __lowercase		:			Optional[int]	    =       scheduler_class(**_snake_case				)

          scheduler.set_timesteps(self.num_inference_steps				)

          __lowercase		:			int	    =       torch.manual_seed(0				)

          __lowercase		:			str	    =       self.dummy_model()
          __lowercase		:			Any	    =       self.dummy_sample_deter * scheduler.init_noise_sigma
          __lowercase		:			int	    =       sample.to(_snake_case				)

          for i, t in enumerate(scheduler.timesteps				):
              __lowercase		:			Tuple	    =       scheduler.scale_model_input(_snake_case				,       _snake_case				)

              __lowercase		:			Any	    =       model(_snake_case				,       _snake_case				)

              __lowercase		:			Tuple	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				)
              __lowercase		:			List[Any]	    =       output.prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			int	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 10.08_07				) < 1E-2
          assert abs(result_mean.item() - 0.01_31				) < 1E-3




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			int	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			List[Any]	    =       scheduler_class(**_snake_case				)

          scheduler.set_timesteps(self.num_inference_steps				)

          __lowercase		:			Any	    =       torch.manual_seed(0				)

          __lowercase		:			Tuple	    =       self.dummy_model()
          __lowercase		:			Optional[int]	    =       self.dummy_sample_deter * scheduler.init_noise_sigma
          __lowercase		:			Optional[Any]	    =       sample.to(_snake_case				)

          for i, t in enumerate(scheduler.timesteps				):
              __lowercase		:			Dict	    =       scheduler.scale_model_input(_snake_case				,       _snake_case				)

              __lowercase		:			str	    =       model(_snake_case				,       _snake_case				)

              __lowercase		:			Any	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				)
              __lowercase		:			int	    =       output.prev_sample

          __lowercase		:			Optional[Any]	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[str]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 0.00_02				) < 1E-2
          assert abs(result_mean.item() - 2.2_6_7_6E-0_6				) < 1E-3




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			List[Any]	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

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

          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          __lowercase		:			Any	    =       self.dummy_model()
          __lowercase		:			List[str]	    =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __lowercase		:			Tuple	    =       sample.to(_snake_case				)

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

              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              __lowercase		:			Dict	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				)
              __lowercase		:			Dict	    =       output.prev_sample

          __lowercase		:			Tuple	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Dict	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 10.08_07				) < 1E-2
          assert abs(result_mean.item() - 0.01_31				) < 1E-3




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Dict	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			Any	    =       scheduler_class(**_snake_case				,       use_karras_sigmas=_snake_case				)

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

          __lowercase		:			Optional[Any]	    =       torch.manual_seed(0				)

          __lowercase		:			Optional[Any]	    =       self.dummy_model()
          __lowercase		:			int	    =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __lowercase		:			Optional[int]	    =       sample.to(_snake_case				)

          for t in scheduler.timesteps:
              __lowercase		:			Any	    =       scheduler.scale_model_input(_snake_case				,       _snake_case				)

              __lowercase		:			Union[str, Any]	    =       model(_snake_case				,       _snake_case				)

              __lowercase		:			Dict	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				)
              __lowercase		:			Tuple	    =       output.prev_sample

          __lowercase		:			Dict	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[str]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19				) < 1E-2
          assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63				) < 1E-3




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          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=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          self.check_over_configs(thresholding=_snake_case				)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=_snake_case				,       prediction_type=_snake_case				,       sample_max_value=_snake_case				,       )




      def 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Optional[int]	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

          for i, timestep in enumerate(_snake_case				):
              if i == len(_snake_case				) - 1:
                  __lowercase		:			Optional[Any]	    =       -1
              else:
                  __lowercase		:			List[str]	    =       timesteps[i + 1]

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       [100, 87, 50, 51, 0]

          with self.assertRaises(_snake_case				,       msg='''`custom_timesteps` must be in descending order.'''				):
              scheduler.set_timesteps(timesteps=_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			Union[str, Any]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       len(_snake_case				)

          with self.assertRaises(_snake_case				,       msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
              scheduler.set_timesteps(num_inference_steps=_snake_case				,       timesteps=_snake_case				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _snake_case				,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,       ):
              scheduler.set_timesteps(timesteps=_snake_case				)


