

"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Any          =	(DPMSolverMultistepScheduler,)
       __UpperCamelCase				:			str          =	(('num_inference_steps', 25),)






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							{
            "num_train_timesteps": 1_000,
            "beta_start": 0.0_001,
            "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",
            "lower_order_final": False,
            "lambda_min_clipped": -float("inf"      ),
            "variance_type": None,
        }

        config.update(**snake_case      )
        return config






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=0 ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							dict(self.forward_default_kwargs      )
        lowerCAmelCase__      :							int    =							kwargs.pop("num_inference_steps" ,				snake_case      )
        lowerCAmelCase__      :							Dict    =							self.dummy_sample
        lowerCAmelCase__      :							int    =							0.1 * sample
        lowerCAmelCase__      :							Dict    =							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
         lowerCAmelCase__      :							Optional[int]    =							self.get_scheduler_config(**snake_case      )
         lowerCAmelCase__      :							Any    =							scheduler_class(**snake_case      )
         scheduler.set_timesteps(snake_case      )
         # copy over dummy past residuals
         lowerCAmelCase__      :							Any    =							dummy_past_residuals[: scheduler.config.solver_order]

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

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

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=0 ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							dict(self.forward_default_kwargs      )
        lowerCAmelCase__      :							Optional[int]    =							kwargs.pop("num_inference_steps" ,				snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							self.dummy_sample
        lowerCAmelCase__      :							int    =							0.1 * sample
        lowerCAmelCase__      :							Any    =							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
         lowerCAmelCase__      :							int    =							self.get_scheduler_config()
         lowerCAmelCase__      :							str    =							scheduler_class(**snake_case      )
         scheduler.set_timesteps(snake_case      )

         # copy over dummy past residuals (must be after setting timesteps)
         lowerCAmelCase__      :							Optional[int]    =							dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(snake_case      )
          lowerCAmelCase__      :							List[str]    =							scheduler_class.from_pretrained(snake_case      )
          # copy over dummy past residuals
          new_scheduler.set_timesteps(snake_case      )

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

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

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if scheduler is None:
         lowerCAmelCase__      :							Dict    =							self.scheduler_classes[0]
         lowerCAmelCase__      :							Union[str, Any]    =							self.get_scheduler_config(**snake_case      )
         lowerCAmelCase__      :							List[Any]    =							scheduler_class(**snake_case      )

        lowerCAmelCase__      :							Optional[Any]    =							10
        lowerCAmelCase__      :							List[str]    =							self.dummy_model()
        lowerCAmelCase__      :							List[str]    =							self.dummy_sample_deter
        scheduler.set_timesteps(snake_case      )

        for i, t in enumerate(scheduler.timesteps      ):
         lowerCAmelCase__      :							Tuple    =							model(snake_case ,				snake_case      )
         lowerCAmelCase__      :							Optional[Any]    =							scheduler.step(snake_case ,				snake_case ,				snake_case      ).prev_sample

        return sample






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							dict(self.forward_default_kwargs      )

        lowerCAmelCase__      :							str    =							kwargs.pop("num_inference_steps" ,				snake_case      )

        for scheduler_class in self.scheduler_classes:
         lowerCAmelCase__      :							str    =							self.get_scheduler_config()
         lowerCAmelCase__      :							Optional[int]    =							scheduler_class(**snake_case      )

         lowerCAmelCase__      :							int    =							self.dummy_sample
         lowerCAmelCase__      :							Union[str, Any]    =							0.1 * sample

         if num_inference_steps is not None and hasattr(snake_case ,				"set_timesteps"      ):
          scheduler.set_timesteps(snake_case      )
         elif num_inference_steps is not None and not hasattr(snake_case ,				"set_timesteps"      ):
          lowerCAmelCase__      :							Optional[int]    =							num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         lowerCAmelCase__      :							List[str]    =							[residual + 0.2, residual + 0.15, residual + 0.10]
         lowerCAmelCase__      :							Union[str, Any]    =							dummy_past_residuals[: scheduler.config.solver_order]

         lowerCAmelCase__      :							str    =							scheduler.timesteps[5]
         lowerCAmelCase__      :							Tuple    =							scheduler.timesteps[6]

         lowerCAmelCase__      :							Any    =							scheduler.step(snake_case ,				snake_case ,				snake_case ,				**snake_case      ).prev_sample
         lowerCAmelCase__      :							Union[str, Any]    =							scheduler.step(snake_case ,				snake_case ,				snake_case ,				**snake_case      ).prev_sample

         self.assertEqual(output_a.shape ,				sample.shape      )
         self.assertEqual(output_a.shape ,				output_a.shape      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for timesteps in [25, 50, 100, 999, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.check_over_configs(thresholding=snake_case      )
        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=snake_case ,				prediction_type=snake_case ,				sample_max_value=snake_case ,				algorithm_type="dpmsolver++" ,				solver_order=snake_case ,				solver_type=snake_case ,				)






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.check_over_configs(lower_order_final=snake_case      )
        self.check_over_configs(lower_order_final=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.check_over_configs(lambda_min_clipped=-float("inf"      )      )
        self.check_over_configs(lambda_min_clipped=-5.1      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.check_over_configs(variance_type=snake_case      )
        self.check_over_configs(variance_type="learned_range"      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
         self.check_over_forward(num_inference_steps=snake_case ,				time_step=0      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.full_loop()
        lowerCAmelCase__      :							int    =							torch.mean(torch.abs(snake_case      )      )

        assert abs(result_mean.item() - 0.3_301      ) < 1e-3






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.full_loop(thresholding=snake_case ,				dynamic_thresholding_ratio=0.87 ,				sample_max_value=0.5      )
        lowerCAmelCase__      :							Optional[int]    =							torch.mean(torch.abs(snake_case      )      )

        assert abs(result_mean.item() - 1.1_364      ) < 1e-3






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.full_loop(prediction_type="v_prediction"      )
        lowerCAmelCase__      :							Union[str, Any]    =							torch.mean(torch.abs(snake_case      )      )

        assert abs(result_mean.item() - 0.2_251      ) < 1e-3






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							self.full_loop(prediction_type="v_prediction" ,				use_karras_sigmas=snake_case      )
        lowerCAmelCase__      :							str    =							torch.mean(torch.abs(snake_case      )      )

        assert abs(result_mean.item() - 0.2_096      ) < 1e-3






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							DPMSolverMultistepScheduler(**self.get_scheduler_config()      )
        lowerCAmelCase__      :							Any    =							self.full_loop(scheduler=snake_case      )
        lowerCAmelCase__      :							Any    =							torch.mean(torch.abs(snake_case      )      )

        assert abs(result_mean.item() - 0.3_301      ) < 1e-3

        lowerCAmelCase__      :							Any    =							DPMSolverSinglestepScheduler.from_config(scheduler.config      )
        lowerCAmelCase__      :							Union[str, Any]    =							UniPCMultistepScheduler.from_config(scheduler.config      )
        lowerCAmelCase__      :							Union[str, Any]    =							DEISMultistepScheduler.from_config(scheduler.config      )
        lowerCAmelCase__      :							str    =							DPMSolverMultistepScheduler.from_config(scheduler.config      )

        lowerCAmelCase__      :							Optional[Any]    =							self.full_loop(scheduler=snake_case      )
        lowerCAmelCase__      :							int    =							torch.mean(torch.abs(snake_case      )      )

        assert abs(result_mean.item() - 0.3_301      ) < 1e-3






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.scheduler_classes[0]
        lowerCAmelCase__      :							Tuple    =							self.get_scheduler_config(thresholding=snake_case ,				dynamic_thresholding_ratio=0      )
        lowerCAmelCase__      :							List[Any]    =							scheduler_class(**snake_case      )

        lowerCAmelCase__      :							Optional[Any]    =							10
        lowerCAmelCase__      :							Union[str, Any]    =							self.dummy_model()
        lowerCAmelCase__      :							Dict    =							self.dummy_sample_deter.half()
        scheduler.set_timesteps(snake_case      )

        for i, t in enumerate(scheduler.timesteps      ):
         lowerCAmelCase__      :							Tuple    =							model(snake_case ,				snake_case      )
         lowerCAmelCase__      :							Union[str, Any]    =							scheduler.step(snake_case ,				snake_case ,				snake_case      ).prev_sample

        assert sample.dtype == torch.floataa






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        for scheduler_class in self.scheduler_classes:
         lowerCAmelCase__      :							Optional[Any]    =							self.get_scheduler_config(**snake_case      )
         lowerCAmelCase__      :							Optional[int]    =							scheduler_class(**snake_case      )

         scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
         assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps









"""simple docstring"""

def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 return 1 if input_a == 0 else 0



def 			SCREAMING_SNAKE_CASE       (   ) ->	None:
 assert not_gate(0      ) == 1
 assert not_gate(1      ) == 0


if __name__ == "__main__":
    print(not_gate(0))
    print(not_gate(1))






