import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Union[str, Any]						=      (UniPCMultistepScheduler,)
    a__	:						int						=      (("""num_inference_steps""", 2_5),)







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.00_01,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''solver_type''': '''bh2''',
        }

        config.update(**__lowercase)
        return config







    def 			UpperCamelCase__   (	self , __lowercase=0 , **__lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Optional[Any]			  =		dict(self.forward_default_kwargs)
        __UpperCamelCase     :List[Any]			  =		kwargs.pop('''num_inference_steps''' , __lowercase)
        __UpperCamelCase     :Optional[int]			  =		self.dummy_sample
        __UpperCamelCase     :Any			  =		0.1 * sample
        __UpperCamelCase     :Optional[Any]			  =		[residual + 0.2, residual + 0.15, residual + 0.10]

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__lowercase)
                __UpperCamelCase     :Any			  =		scheduler_class.from_pretrained(__lowercase)
                new_scheduler.set_timesteps(__lowercase)
                # copy over dummy past residuals
                __UpperCamelCase     :Tuple			  =		dummy_past_residuals[: new_scheduler.config.solver_order]

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

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







    def 			UpperCamelCase__   (	self , __lowercase=0 , **__lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Tuple			  =		dict(self.forward_default_kwargs)
        __UpperCamelCase     :Optional[Any]			  =		kwargs.pop('''num_inference_steps''' , __lowercase)
        __UpperCamelCase     :Tuple			  =		self.dummy_sample
        __UpperCamelCase     :Tuple			  =		0.1 * sample
        __UpperCamelCase     :Any			  =		[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            __UpperCamelCase     :List[Any]			  =		self.get_scheduler_config()
            __UpperCamelCase     :Optional[Any]			  =		scheduler_class(**__lowercase)
            scheduler.set_timesteps(__lowercase)

            # copy over dummy past residuals (must be after setting timesteps)
            __UpperCamelCase     :List[str]			  =		dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(__lowercase)
                __UpperCamelCase     :Any			  =		scheduler_class.from_pretrained(__lowercase)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(__lowercase)

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

            __UpperCamelCase     :Optional[int]			  =		scheduler.step(__lowercase , __lowercase , __lowercase , **__lowercase).prev_sample
            __UpperCamelCase     :List[Any]			  =		new_scheduler.step(__lowercase , __lowercase , __lowercase , **__lowercase).prev_sample

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







    def 			UpperCamelCase__   (	self , __lowercase=None , **__lowercase)       ->	int:
        if scheduler is None:
            __UpperCamelCase     :Union[str, Any]			  =		self.scheduler_classes[0]
            __UpperCamelCase     :Optional[int]			  =		self.get_scheduler_config(**__lowercase)
            __UpperCamelCase     :Union[str, Any]			  =		scheduler_class(**__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		self.scheduler_classes[0]
        __UpperCamelCase     :Optional[int]			  =		self.get_scheduler_config(**__lowercase)
        __UpperCamelCase     :Tuple			  =		scheduler_class(**__lowercase)

        __UpperCamelCase     :Union[str, Any]			  =		10
        __UpperCamelCase     :Dict			  =		self.dummy_model()
        __UpperCamelCase     :Optional[Any]			  =		self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase)

        for i, t in enumerate(scheduler.timesteps):
            __UpperCamelCase     :List[str]			  =		model(__lowercase , __lowercase)
            __UpperCamelCase     :int			  =		scheduler.step(__lowercase , __lowercase , __lowercase).prev_sample

        return sample







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Any			  =		dict(self.forward_default_kwargs)

        __UpperCamelCase     :Tuple			  =		kwargs.pop('''num_inference_steps''' , __lowercase)

        for scheduler_class in self.scheduler_classes:
            __UpperCamelCase     :Dict			  =		self.get_scheduler_config()
            __UpperCamelCase     :Tuple			  =		scheduler_class(**__lowercase)

            __UpperCamelCase     :List[str]			  =		self.dummy_sample
            __UpperCamelCase     :Any			  =		0.1 * sample

            if num_inference_steps is not None and hasattr(__lowercase , '''set_timesteps'''):
                scheduler.set_timesteps(__lowercase)
            elif num_inference_steps is not None and not hasattr(__lowercase , '''set_timesteps'''):
                __UpperCamelCase     :str			  =		num_inference_steps

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

            __UpperCamelCase     :Union[str, Any]			  =		scheduler.timesteps[5]
            __UpperCamelCase     :Any			  =		scheduler.timesteps[6]

            __UpperCamelCase     :Optional[Any]			  =		scheduler.step(__lowercase , __lowercase , __lowercase , **__lowercase).prev_sample
            __UpperCamelCase     :List[Any]			  =		scheduler.step(__lowercase , __lowercase , __lowercase , **__lowercase).prev_sample

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







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        __UpperCamelCase     :Any			  =		UniPCMultistepScheduler(**self.get_scheduler_config())
        __UpperCamelCase     :Tuple			  =		self.full_loop(scheduler=__lowercase)
        __UpperCamelCase     :List[Any]			  =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.24_64) < 1E-3

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

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

        assert abs(result_mean.item() - 0.24_64) < 1E-3







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        for timesteps in [25, 50, 100, 999, 1_000]:
            self.check_over_configs(num_train_timesteps=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        self.check_over_configs(thresholding=__lowercase)
        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=__lowercase , prediction_type=__lowercase , sample_max_value=__lowercase , solver_order=__lowercase , solver_type=__lowercase , )







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	int:
        for solver_type in ["bh1", "bh2"]:
            for order in [1, 2, 3]:
                for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        solver_order=__lowercase , solver_type=__lowercase , prediction_type=__lowercase , )
                    __UpperCamelCase     :Dict			  =		self.full_loop(
                        solver_order=__lowercase , solver_type=__lowercase , prediction_type=__lowercase , )
                    assert not torch.isnan(__lowercase).any(), "Samples have nan numbers"







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        self.check_over_configs(lower_order_final=__lowercase)
        self.check_over_configs(lower_order_final=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
            self.check_over_forward(num_inference_steps=__lowercase , time_step=0)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :List[Any]			  =		self.full_loop()
        __UpperCamelCase     :Dict			  =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.24_64) < 1E-3







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :str			  =		self.full_loop(prediction_type='''v_prediction''')
        __UpperCamelCase     :Optional[Any]			  =		torch.mean(torch.abs(__lowercase))

        assert abs(result_mean.item() - 0.10_14) < 1E-3







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :str			  =		self.scheduler_classes[0]
        __UpperCamelCase     :List[Any]			  =		self.get_scheduler_config(thresholding=__lowercase , dynamic_thresholding_ratio=0)
        __UpperCamelCase     :Optional[Any]			  =		scheduler_class(**__lowercase)

        __UpperCamelCase     :Optional[int]			  =		10
        __UpperCamelCase     :Tuple			  =		self.dummy_model()
        __UpperCamelCase     :Optional[Any]			  =		self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowercase)

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

        assert sample.dtype == torch.floataa







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Any:
        for scheduler_class in self.scheduler_classes:
            __UpperCamelCase     :Tuple			  =		self.get_scheduler_config(**__lowercase)
            __UpperCamelCase     :List[Any]			  =		scheduler_class(**__lowercase)

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






def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





