
"""simple docstring"""



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				(DDPMScheduler,)







    def      _snake_case					(			self			:       List[str]     ,					**__UpperCamelCase			:       List[Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   {
            """num_train_timesteps""": 10_00,
            """beta_start""": 0.0_0_0_1,
            """beta_end""": 0.0_2,
            """beta_schedule""": """linear""",
            """variance_type""": """fixed_small""",
            """clip_sample""": True,
        }

        config.update(**__UpperCamelCase     )
        return config







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






        '''simple docstring'''






        for timesteps in [1, 5, 1_00, 10_00]:
            self.check_over_configs(num_train_timesteps=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->List[Any]:






        '''simple docstring'''






        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]     ,					[0.0_0_2, 0.0_2, 0.2, 2]     ):
            self.check_over_configs(beta_start=__UpperCamelCase     ,					beta_end=__UpperCamelCase     )







    def      _snake_case					(			self			:       int     )		->Tuple:






        '''simple docstring'''






        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Optional[Any]:






        '''simple docstring'''






        for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=__UpperCamelCase     )







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






        '''simple docstring'''






        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=__UpperCamelCase     )







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






        '''simple docstring'''






        self.check_over_configs(thresholding=__UpperCamelCase     )
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=__UpperCamelCase     ,					prediction_type=__UpperCamelCase     ,					sample_max_value=__UpperCamelCase     ,					)







    def      _snake_case					(			self			:       int     )		->List[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       int     )		->int:






        '''simple docstring'''






        for t in [0, 5_00, 9_99]:
            self.check_over_forward(time_step=__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config()
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_0_9_7_9     )     ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.0_2     )     ) < 1e-5







    def      _snake_case					(			self			:       List[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config()
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        _UpperCAmelCase          =   len(__UpperCamelCase     )

        _UpperCAmelCase          =   self.dummy_model()
        _UpperCAmelCase          =   self.dummy_sample_deter
        _UpperCAmelCase          =   torch.manual_seed(0     )

        for t in reversed(range(__UpperCamelCase     )     ):
            # 1. predict noise residual
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					__UpperCamelCase     )

            # 2. predict previous mean of sample x_t-1
            _UpperCAmelCase          =   scheduler.step(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					generator=__UpperCamelCase     ).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            _UpperCAmelCase          =   pred_prev_sample

        _UpperCAmelCase          =   torch.sum(torch.abs(__UpperCamelCase     )     )
        _UpperCAmelCase          =   torch.mean(torch.abs(__UpperCamelCase     )     )

        assert abs(result_sum.item() - 2_5_8.9_6_0_6     ) < 1e-2
        assert abs(result_mean.item() - 0.3_3_7_2     ) < 1e-3







    def      _snake_case					(			self			:       Optional[int]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config(prediction_type="""v_prediction"""     )
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        _UpperCAmelCase          =   len(__UpperCamelCase     )

        _UpperCAmelCase          =   self.dummy_model()
        _UpperCAmelCase          =   self.dummy_sample_deter
        _UpperCAmelCase          =   torch.manual_seed(0     )

        for t in reversed(range(__UpperCamelCase     )     ):
            # 1. predict noise residual
            _UpperCAmelCase          =   model(__UpperCamelCase     ,					__UpperCamelCase     )

            # 2. predict previous mean of sample x_t-1
            _UpperCAmelCase          =   scheduler.step(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     ,					generator=__UpperCamelCase     ).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            _UpperCAmelCase          =   pred_prev_sample

        _UpperCAmelCase          =   torch.sum(torch.abs(__UpperCamelCase     )     )
        _UpperCAmelCase          =   torch.mean(torch.abs(__UpperCamelCase     )     )

        assert abs(result_sum.item() - 2_0_2.0_2_9_6     ) < 1e-2
        assert abs(result_mean.item() - 0.2_6_3_1     ) < 1e-3







    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config()
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        _UpperCAmelCase          =   [1_00, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=__UpperCamelCase     )

        _UpperCAmelCase          =   scheduler.timesteps

        for i, timestep in enumerate(__UpperCamelCase     ):
            if i == len(__UpperCamelCase     ) - 1:
                _UpperCAmelCase          =   -1
            else:
                _UpperCAmelCase          =   timesteps[i + 1]

            _UpperCAmelCase          =   scheduler.previous_timestep(__UpperCamelCase     )
            _UpperCAmelCase          =   prev_t.item()

            self.assertEqual(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config()
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        _UpperCAmelCase          =   [1_00, 87, 50, 51, 0]

        with self.assertRaises(__UpperCamelCase     ,					msg="""`custom_timesteps` must be in descending order."""     ):
            scheduler.set_timesteps(timesteps=__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config()
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        _UpperCAmelCase          =   [1_00, 87, 50, 1, 0]
        _UpperCAmelCase          =   len(__UpperCamelCase     )

        with self.assertRaises(__UpperCamelCase     ,					msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""     ):
            scheduler.set_timesteps(num_inference_steps=__UpperCamelCase     ,					timesteps=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.scheduler_classes[0]
        _UpperCAmelCase          =   self.get_scheduler_config()
        _UpperCAmelCase          =   scheduler_class(**__UpperCamelCase     )

        _UpperCAmelCase          =   [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            __UpperCamelCase     ,					msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""     ,					):
            scheduler.set_timesteps(timesteps=__UpperCamelCase     )

"""simple docstring"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

    _UpperCAmelCase          =   x
    _UpperCAmelCase          =   y
    for step in range(_A      ):  # noqa: B007
        _UpperCAmelCase          =   a * a - b * b + x
        _UpperCAmelCase          =   2 * a * b + y
        _UpperCAmelCase          =   a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_5_5      ) for i in colorsys.hsv_to_rgb(_A					,      1					,      1      )      )


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

    _UpperCAmelCase          =   Image.new("""RGB"""					,      (image_width, image_height)      )
    _UpperCAmelCase          =   img.load()

    # loop through the image-coordinates
    for image_x in range(_A      ):
        for image_y in range(_A      ):
            # determine the figure-coordinates based on the image-coordinates
            _UpperCAmelCase          =   figure_width / image_width * image_height
            _UpperCAmelCase          =   figure_center_x + (image_x / image_width - 0.5) * figure_width
            _UpperCAmelCase          =   figure_center_y + (image_y / image_height - 0.5) * figure_height

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                _UpperCAmelCase          =   get_color_coded_rgb(_A      )
            else:
                _UpperCAmelCase          =   get_black_and_white_rgb(_A      )

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a					:	List[str]							=		get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()