
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =(DDIMScheduler,)
       UpperCAmelCase_				       =(("eta", 0.0), ("num_inference_steps", 50))



       def     _UpperCamelCase	(							self ,     **_A	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**_A	)
          return config



       def     _UpperCamelCase	(							self ,     **_A	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_	=      self.get_scheduler_config(**_A	)
          SCREAMING_SNAKE_CASE_	=      scheduler_class(**_A	)

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      10, 0.0

          SCREAMING_SNAKE_CASE_	=      self.dummy_model()
          SCREAMING_SNAKE_CASE_	=      self.dummy_sample_deter

          scheduler.set_timesteps(_A	)

          for t in scheduler.timesteps:
             SCREAMING_SNAKE_CASE_	=      model(_A ,     _A	)
             SCREAMING_SNAKE_CASE_	=      scheduler.step(_A ,     _A ,     _A ,     _A	).prev_sample

          return sample



       def     _UpperCamelCase	(							self	)				->				Dict:
          for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=_A	)



       def     _UpperCamelCase	(							self	)				->				Dict:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=_A	)

          SCREAMING_SNAKE_CASE_	=      self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_	=      self.get_scheduler_config(steps_offset=1	)
          SCREAMING_SNAKE_CASE_	=      scheduler_class(**_A	)
          scheduler.set_timesteps(5	)
          assert torch.equal(scheduler.timesteps ,     torch.LongTensor([801, 601, 401, 201, 1]	)	)



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,     [0.002, 0.02, 0.2, 2]	):
             self.check_over_configs(beta_start=_A ,     beta_end=_A	)



       def     _UpperCamelCase	(							self	)				->				Dict:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=_A	)



       def     _UpperCamelCase	(							self	)				->				List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_A	)



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=_A	)



       def     _UpperCamelCase	(							self	)				->				Tuple:
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=_A	)



       def     _UpperCamelCase	(							self	)				->				Tuple:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=_A	)



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          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	)				->				str:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=_A	)



       def     _UpperCamelCase	(							self	)				->				Any:
          for t, num_inference_steps in zip([1, 10, 50] ,     [10, 50, 500]	):
             self.check_over_forward(time_step=_A ,     num_inference_steps=_A	)



       def     _UpperCamelCase	(							self	)				->				Any:
          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	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_	=      self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_	=      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(420 ,     400	) - 0.1_4771	)	) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(980 ,     960	) - 0.3_2460	)	) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(0 ,     0	) - 0.0	)	) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487 ,     486	) - 0.0_0979	)	) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999 ,     998	) - 0.02	)	) < 1E-5



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      self.full_loop()

          SCREAMING_SNAKE_CASE_	=      torch.sum(torch.abs(_A	)	)
          SCREAMING_SNAKE_CASE_	=      torch.mean(torch.abs(_A	)	)

          assert abs(result_sum.item() - 172.0067	) < 1E-2
          assert abs(result_mean.item() - 0.22_3967	) < 1E-3



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      self.full_loop(prediction_type='''v_prediction'''	)

          SCREAMING_SNAKE_CASE_	=      torch.sum(torch.abs(_A	)	)
          SCREAMING_SNAKE_CASE_	=      torch.mean(torch.abs(_A	)	)

          assert abs(result_sum.item() - 52.5302	) < 1E-2
          assert abs(result_mean.item() - 0.0684	) < 1E-3



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          # We specify different beta, so that the first alpha is 0.99
          SCREAMING_SNAKE_CASE_	=      self.full_loop(set_alpha_to_one=_A ,     beta_start=0.01	)
          SCREAMING_SNAKE_CASE_	=      torch.sum(torch.abs(_A	)	)
          SCREAMING_SNAKE_CASE_	=      torch.mean(torch.abs(_A	)	)

          assert abs(result_sum.item() - 149.8295	) < 1E-2
          assert abs(result_mean.item() - 0.1951	) < 1E-3




       def     _UpperCamelCase	(							self	)				->				List[str]:
          # We specify different beta, so that the first alpha is 0.99
          SCREAMING_SNAKE_CASE_	=      self.full_loop(set_alpha_to_one=_A ,     beta_start=0.01	)
          SCREAMING_SNAKE_CASE_	=      torch.sum(torch.abs(_A	)	)
          SCREAMING_SNAKE_CASE_	=      torch.mean(torch.abs(_A	)	)

          assert abs(result_sum.item() - 149.0784	) < 1E-2
          assert abs(result_mean.item() - 0.1941	) < 1E-3




import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


