



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__					(		_lowerCamelCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			List[Any] =				(UniPCMultistepScheduler,)
    __lowerCamelCase      :			Dict =				(("num_inference_steps", 25),)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        A			:	Optional[int]										=       {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
            """solver_type""": """bh2""",
        }

        config.update(**lowerCamelCase__		)
        return config






    def       _lowerCAmelCase (	self,		lowerCamelCase__=0,		**lowerCamelCase__		):
        A			:	int										=       dict(self.forward_default_kwargs		)
        A			:	str										=       kwargs.pop("""num_inference_steps""",		lowerCamelCase__		)
        A			:	Optional[int]										=       self.dummy_sample
        A			:	Union[str, Any]										=       0.1 * sample
        A			:	List[str]										=       [residual + 0.2, residual + 0.15, residual + 0.10]

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowerCamelCase__		)
                A			:	Union[str, Any]										=       scheduler_class.from_pretrained(lowerCamelCase__		)
                new_scheduler.set_timesteps(lowerCamelCase__		)
                # copy over dummy past residuals
                A			:	int										=       dummy_past_residuals[: new_scheduler.config.solver_order]

            A					,							A			:	Dict										=       sample, sample
            for t in range(lowerCamelCase__,		time_step + scheduler.config.solver_order + 1		):
                A			:	Dict										=       scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
                A			:	List[str]										=       new_scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample

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






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

        for scheduler_class in self.scheduler_classes:
            A			:	int										=       self.get_scheduler_config()
            A			:	Optional[Any]										=       scheduler_class(**lowerCamelCase__		)
            scheduler.set_timesteps(lowerCamelCase__		)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowerCamelCase__		)
                A			:	str										=       scheduler_class.from_pretrained(lowerCamelCase__		)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(lowerCamelCase__		)

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

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

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__=None,		**lowerCamelCase__		):


        if scheduler is None:
            A			:	List[str]										=       self.scheduler_classes[0]
            A			:	List[Any]										=       self.get_scheduler_config(**lowerCamelCase__		)
            A			:	Optional[Any]										=       scheduler_class(**lowerCamelCase__		)

        A			:	int										=       self.scheduler_classes[0]
        A			:	int										=       self.get_scheduler_config(**lowerCamelCase__		)
        A			:	Dict										=       scheduler_class(**lowerCamelCase__		)

        A			:	Optional[Any]										=       10
        A			:	Tuple										=       self.dummy_model()
        A			:	List[Any]										=       self.dummy_sample_deter
        scheduler.set_timesteps(lowerCamelCase__		)

        for i, t in enumerate(scheduler.timesteps		):
            A			:	str										=       model(lowerCamelCase__,		lowerCamelCase__		)
            A			:	Tuple										=       scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       dict(self.forward_default_kwargs		)

        A			:	List[Any]										=       kwargs.pop("""num_inference_steps""",		lowerCamelCase__		)

        for scheduler_class in self.scheduler_classes:
            A			:	Tuple										=       self.get_scheduler_config()
            A			:	int										=       scheduler_class(**lowerCamelCase__		)

            A			:	Union[str, Any]										=       self.dummy_sample
            A			:	List[str]										=       0.1 * sample

            if num_inference_steps is not None and hasattr(lowerCamelCase__,		"""set_timesteps"""		):
                scheduler.set_timesteps(lowerCamelCase__		)
            elif num_inference_steps is not None and not hasattr(lowerCamelCase__,		"""set_timesteps"""		):
                A			:	int										=       num_inference_steps

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

            A			:	List[Any]										=       scheduler.timesteps[5]
            A			:	Dict										=       scheduler.timesteps[6]

            A			:	int										=       scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample
            A			:	List[Any]										=       scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		).prev_sample

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






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       UniPCMultistepScheduler(**self.get_scheduler_config()		)
        A			:	Dict										=       self.full_loop(scheduler=lowerCamelCase__		)
        A			:	Any										=       torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_mean.item() - 0.2464		) < 1e-3

        A			:	Union[str, Any]										=       DPMSolverSinglestepScheduler.from_config(scheduler.config		)
        A			:	Optional[Any]										=       DEISMultistepScheduler.from_config(scheduler.config		)
        A			:	Dict										=       DPMSolverMultistepScheduler.from_config(scheduler.config		)
        A			:	Tuple										=       UniPCMultistepScheduler.from_config(scheduler.config		)

        A			:	List[Any]										=       self.full_loop(scheduler=lowerCamelCase__		)
        A			:	List[str]										=       torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_mean.item() - 0.2464		) < 1e-3






    def       _lowerCAmelCase (	self		):


        for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        self.check_over_configs(thresholding=lowerCamelCase__		)
        for order in [1, 2, 3]:
            for solver_type in ["bh1", "bh2"]:
                for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            thresholding=lowerCamelCase__,		prediction_type=lowerCamelCase__,		sample_max_value=lowerCamelCase__,		solver_order=lowerCamelCase__,		solver_type=lowerCamelCase__,		)






    def       _lowerCAmelCase (	self		):


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






    def       _lowerCAmelCase (	self		):


        for solver_type in ["bh1", "bh2"]:
            for order in [1, 2, 3]:
                for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        solver_order=lowerCamelCase__,		solver_type=lowerCamelCase__,		prediction_type=lowerCamelCase__,		)
                    A			:	Dict										=       self.full_loop(
                        solver_order=lowerCamelCase__,		solver_type=lowerCamelCase__,		prediction_type=lowerCamelCase__,		)
                    assert not torch.isnan(lowerCamelCase__		).any(), "Samples have nan numbers"






    def       _lowerCAmelCase (	self		):
        self.check_over_configs(lower_order_final=lowerCamelCase__		)
        self.check_over_configs(lower_order_final=lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):


        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=lowerCamelCase__,		time_step=0		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       self.full_loop()
        A			:	Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_mean.item() - 0.2464		) < 1e-3






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       self.full_loop(prediction_type="""v_prediction"""		)
        A			:	Any										=       torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_mean.item() - 0.1014		) < 1e-3






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.scheduler_classes[0]
        A			:	int										=       self.get_scheduler_config(thresholding=lowerCamelCase__,		dynamic_thresholding_ratio=0		)
        A			:	List[Any]										=       scheduler_class(**lowerCamelCase__		)

        A			:	Dict										=       10
        A			:	List[str]										=       self.dummy_model()
        A			:	Tuple										=       self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCamelCase__		)

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

        assert sample.dtype == torch.floataa






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):


        for scheduler_class in self.scheduler_classes:
            A			:	Dict										=       self.get_scheduler_config(**lowerCamelCase__		)
            A			:	Dict										=       scheduler_class(**lowerCamelCase__		)

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







from random import randint, random




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase = False				,					_lowerCAmelCase = False				,					_lowerCAmelCase = 5				,					)	->  list:



    """simple docstring"""






    A			:	Any										=       [[-1] * number_of_cells]  # Create a highway without any car
    A			:	Tuple										=       0
    A			:	Dict										=       max(_lowerCAmelCase				,					0		)
    while i < number_of_cells:
        A			:	Any										=       (
            randint(0				,					_lowerCAmelCase		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1				,					max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	List[str]										=       0
    A			:	Dict										=       highway_now[car_index + 1 :]
    for cell in range(len(_lowerCAmelCase		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(_lowerCAmelCase				,					-1		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  list:



    """simple docstring"""






    A			:	str										=       len(_lowerCAmelCase		)
    # Beforce calculations, the highway is empty
    A			:	Any										=       [-1] * number_of_cells

    for car_index in range(_lowerCAmelCase		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:	str										=       min(highway_now[car_index] + 1				,					_lowerCAmelCase		)
            # Number of empty cell before the next car
            A			:	Optional[int]										=       get_distance(_lowerCAmelCase				,					_lowerCAmelCase		) - 1
            # We can't have the car causing an accident
            A			:	Any										=       min(next_highway[car_index]				,					_lowerCAmelCase		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:	Optional[Any]										=       max(next_highway[car_index] - 1				,					0		)
    return next_highway




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  list:



    """simple docstring"""






    A			:	Any										=       len(highway[0]		)

    for i in range(_lowerCAmelCase		):
        A			:	Optional[int]										=       update(highway[i]				,					_lowerCAmelCase				,					_lowerCAmelCase		)
        A			:	Tuple										=       [-1] * number_of_cells

        for car_index in range(_lowerCAmelCase		):
            A			:	Dict										=       next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:	Optional[int]										=       (car_index + speed) % number_of_cells
                # Commit the change of position
                A			:	Dict										=       speed
        highway.append(_lowerCAmelCase		)

    return highway


if __name__ == "__main__":
     import doctest

     doctest.testmod()


