

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 						lowercase_					(						__lowercase  ):
      UpperCamelCase_    :	int		   =       (DDIMScheduler,)
      UpperCamelCase_    :	List[Any]		   =       (("eta", 0.0), ("num_inference_steps", 5_0))



      def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      **A__   :       Union[str, Any]	)					->  int:
        _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   :       Optional[int]    ,      **A__   :       Optional[Any]	)					->  List[str]:
        _snake_case		=							self.scheduler_classes[0]
        _snake_case		=							self.get_scheduler_config(**A__	)
        _snake_case		=							scheduler_class(**A__	)

        _snake_case,  _snake_case		=							10, 0.0

        _snake_case		=							self.dummy_model()
        _snake_case		=							self.dummy_sample_deter

        scheduler.set_timesteps(A__	)

        for t in scheduler.timesteps:
          _snake_case		=							model(A__    ,      A__	)
          _snake_case		=							scheduler.step(A__    ,      A__    ,      A__    ,      A__	).prev_sample

        return sample



      def 		UpperCamelCase_	(    self   :       int	)					->  List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=A__	)



      def 		UpperCamelCase_	(    self   :       Dict	)					->  Union[str, Any]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=A__	)

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



      def 		UpperCamelCase_	(    self   :       int	)					->  Dict:
        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   :       Optional[int]	)					->  Any:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=A__	)



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Union[str, Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=A__	)



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=A__	)



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Any:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=A__	)



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  str:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=A__	)



      def 		UpperCamelCase_	(    self   :       int	)					->  Union[str, 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   :       Tuple	)					->  str:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=A__	)



      def 		UpperCamelCase_	(    self   :       Any	)					->  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   :       str	)					->  List[str]:
        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   :       Any	)					->  Optional[int]:
        _snake_case		=							self.scheduler_classes[0]
        _snake_case		=							self.get_scheduler_config()
        _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   :       Optional[Any]	)					->  int:
        _snake_case		=							self.full_loop()

        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _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   :       str	)					->  Optional[Any]:
        _snake_case		=							self.full_loop(prediction_type='''v_prediction'''	)

        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _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   :       List[Any]	)					->  List[str]:
        # We specify different beta, so that the first alpha is 0.99
        _snake_case		=							self.full_loop(set_alpha_to_one=A__    ,      beta_start=0.01	)
        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _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   :       Tuple	)					->  int:
        # We specify different beta, so that the first alpha is 0.99
        _snake_case		=							self.full_loop(set_alpha_to_one=A__    ,      beta_start=0.01	)
        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _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




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

