




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 						__lowercase		(A__							):




      """simple docstring"""





      _snake_case							     =						(DPMSolverSinglestepScheduler,)
      _snake_case							     =						(("""num_inference_steps""", 25),)

      def 	UpperCAmelCase      (      self				,      **A	)		->				List[Any]:
         snake_case      :				str				=       {
             "num_train_timesteps": 1_0_0_0,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "solver_order": 2,
             "prediction_type": "epsilon",
             "thresholding": False,
             "sample_max_value": 1.0,
             "algorithm_type": "dpmsolver++",
             "solver_type": "midpoint",
             "lambda_min_clipped": -float("""inf"""	),
             "variance_type": None,
         }

         config.update(**_A	)
         return config

      def 	UpperCAmelCase      (      self				,      A=0				,      **A	)		->				Tuple:
         snake_case      :				List[Any]				=       dict(self.forward_default_kwargs	)
         snake_case      :				Tuple				=       kwargs.pop("""num_inference_steps"""				,      _A	)
         snake_case      :				Dict				=       self.dummy_sample
         snake_case      :				Optional[Any]				=       0.1 * sample
         snake_case      :				List[Any]				=       [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            snake_case      :				Tuple				=       self.get_scheduler_config(**_A	)
            snake_case      :				Union[str, Any]				=       scheduler_class(**_A	)
            scheduler.set_timesteps(_A	)
            # copy over dummy past residuals
            snake_case      :				Union[str, Any]				=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(_A	)
               snake_case      :				Optional[int]				=       scheduler_class.from_pretrained(_A	)
               new_scheduler.set_timesteps(_A	)
               # copy over dummy past residuals
               snake_case      :				List[Any]				=       dummy_past_residuals[: new_scheduler.config.solver_order]

            snake_case      :				str				=       sample, sample
            for t in range(_A				,      time_step + scheduler.config.solver_order + 1	):
               snake_case      :				str				=       scheduler.step(_A				,      _A				,      _A				,      **_A	).prev_sample
               snake_case      :				Union[str, Any]				=       new_scheduler.step(_A				,      _A				,      _A				,      **_A	).prev_sample

               assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         pass

      def 	UpperCAmelCase      (      self				,      A=0				,      **A	)		->				Any:
         snake_case      :				List[str]				=       dict(self.forward_default_kwargs	)
         snake_case      :				Tuple				=       kwargs.pop("""num_inference_steps"""				,      _A	)
         snake_case      :				int				=       self.dummy_sample
         snake_case      :				int				=       0.1 * sample
         snake_case      :				Optional[Any]				=       [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            snake_case      :				Tuple				=       self.get_scheduler_config()
            snake_case      :				Optional[int]				=       scheduler_class(**_A	)
            scheduler.set_timesteps(_A	)

            # copy over dummy past residuals (must be after setting timesteps)
            snake_case      :				Union[str, Any]				=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(_A	)
               snake_case      :				Dict				=       scheduler_class.from_pretrained(_A	)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(_A	)

               # copy over dummy past residual (must be after setting timesteps)
               snake_case      :				Optional[int]				=       dummy_past_residuals[: new_scheduler.config.solver_order]

            snake_case      :				List[Any]				=       scheduler.step(_A				,      _A				,      _A				,      **_A	).prev_sample
            snake_case      :				Dict				=       new_scheduler.step(_A				,      _A				,      _A				,      **_A	).prev_sample

            assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

      def 	UpperCAmelCase      (      self				,      A=None				,      **A	)		->				str:






         if scheduler is None:
            snake_case      :				Dict				=       self.scheduler_classes[0]
            snake_case      :				Optional[Any]				=       self.get_scheduler_config(**_A	)
            snake_case      :				int				=       scheduler_class(**_A	)

         snake_case      :				Tuple				=       self.scheduler_classes[0]
         snake_case      :				int				=       self.get_scheduler_config(**_A	)
         snake_case      :				str				=       scheduler_class(**_A	)

         snake_case      :				List[Any]				=       1_0
         snake_case      :				Any				=       self.dummy_model()
         snake_case      :				Optional[int]				=       self.dummy_sample_deter
         scheduler.set_timesteps(_A	)

         for i, t in enumerate(scheduler.timesteps	):
            snake_case      :				int				=       model(_A				,      _A	)
            snake_case      :				List[Any]				=       scheduler.step(_A				,      _A				,      _A	).prev_sample

         return sample

      def 	UpperCAmelCase      (      self	)		->				List[str]:
         snake_case      :				List[str]				=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
         snake_case      :				Union[str, Any]				=       5_0
         snake_case      :				Tuple				=       self.dummy_model()
         snake_case      :				Tuple				=       self.dummy_sample_deter
         scheduler.set_timesteps(_A	)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]	):
            snake_case      :				Any				=       model(_A				,      _A	)
            snake_case      :				Tuple				=       scheduler.step(_A				,      _A				,      _A	).prev_sample

         snake_case      :				str				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.25_74	) < 1e-3

      def 	UpperCAmelCase      (      self	)		->				Any:






         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=_A	)

      def 	UpperCAmelCase      (      self	)		->				Any:
         snake_case      :				Dict				=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
         snake_case      :				Tuple				=       self.full_loop(scheduler=_A	)
         snake_case      :				List[Any]				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.27_91	) < 1e-3

         snake_case      :				Tuple				=       DEISMultistepScheduler.from_config(scheduler.config	)
         snake_case      :				str				=       DPMSolverMultistepScheduler.from_config(scheduler.config	)
         snake_case      :				List[str]				=       UniPCMultistepScheduler.from_config(scheduler.config	)
         snake_case      :				List[str]				=       DPMSolverSinglestepScheduler.from_config(scheduler.config	)

         snake_case      :				int				=       self.full_loop(scheduler=_A	)
         snake_case      :				int				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.27_91	) < 1e-3

      def 	UpperCAmelCase      (      self	)		->				List[str]:
         self.check_over_configs(thresholding=_A	)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=_A				,      prediction_type=_A				,      sample_max_value=_A				,      algorithm_type="""dpmsolver++"""				,      solver_order=_A				,      solver_type=_A				,      )

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






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

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






         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=_A				,      solver_type=_A				,      prediction_type=_A				,      algorithm_type=_A				,      )
                     snake_case      :				str				=       self.full_loop(
                         solver_order=_A				,      solver_type=_A				,      prediction_type=_A				,      algorithm_type=_A				,      )
                     assert not torch.isnan(_A	).any(), "Samples have nan numbers"

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         self.check_over_configs(lower_order_final=_A	)
         self.check_over_configs(lower_order_final=_A	)

      def 	UpperCAmelCase      (      self	)		->				int:
         self.check_over_configs(lambda_min_clipped=-float("""inf"""	)	)
         self.check_over_configs(lambda_min_clipped=-5.1	)

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         self.check_over_configs(variance_type=_A	)
         self.check_over_configs(variance_type="""learned_range"""	)

      def 	UpperCAmelCase      (      self	)		->				int:






         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=_A				,      time_step=0	)

      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         snake_case      :				Dict				=       self.full_loop()
         snake_case      :				Union[str, Any]				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.27_91	) < 1e-3

      def 	UpperCAmelCase      (      self	)		->				int:
         snake_case      :				Dict				=       self.full_loop(use_karras_sigmas=_A	)
         snake_case      :				Any				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.22_48	) < 1e-3

      def 	UpperCAmelCase      (      self	)		->				Dict:
         snake_case      :				Optional[Any]				=       self.full_loop(prediction_type="""v_prediction"""	)
         snake_case      :				List[Any]				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.14_53	) < 1e-3

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         snake_case      :				List[str]				=       self.full_loop(prediction_type="""v_prediction"""				,      use_karras_sigmas=_A	)
         snake_case      :				Union[str, Any]				=       torch.mean(torch.abs(_A	)	)

         assert abs(result_mean.item() - 0.06_49	) < 1e-3

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         snake_case      :				Optional[int]				=       self.scheduler_classes[0]
         snake_case      :				Optional[Any]				=       self.get_scheduler_config(thresholding=_A				,      dynamic_thresholding_ratio=0	)
         snake_case      :				Any				=       scheduler_class(**_A	)

         snake_case      :				Any				=       1_0
         snake_case      :				Union[str, Any]				=       self.dummy_model()
         snake_case      :				Dict				=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(_A	)

         for i, t in enumerate(scheduler.timesteps	):
            snake_case      :				Any				=       model(_A				,      _A	)
            snake_case      :				Dict				=       scheduler.step(_A				,      _A				,      _A	).prev_sample

         assert sample.dtype == torch.floataa






def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    int(lowerCAmelCase	)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    divmod(lowerCAmelCase   , 2	)
		return binary_recursive(lowerCAmelCase	) + str(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    str(lowerCAmelCase	).strip()
		if not number:
				raise ValueError("No input value was provided"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    "-" if number.startswith("-"	) else ""
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    number.lstrip("-"	)
		if not number.isnumeric():
				raise ValueError("Input value is not an integer"	)
		return f'{negative}0b{binary_recursive(int(lowerCAmelCase	)	)}'


if __name__ == "__main__":
		from doctest import testmod

		testmod()
