




'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class     SCREAMING_SNAKE_CASE						(	unittest.TestCase							):
      __magic_name__     :					Optional[Any]             =				(ScoreSdeVeScheduler,)
      __magic_name__     :					Dict             =				()






      @property
      def        lowercase_	(   self      :     Any					):


           '''simple docstring'''

           a_    :       List[Any]    	=     4
           a_    :       Tuple    	=     3
           a_    :       Optional[Any]    	=     8
           a_    :       Dict    	=     8

           a_    :       List[Any]    	=     torch.rand((batch_size, num_channels, height, width)					)

           return sample






      @property
      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           a_    :       Tuple    	=     4
           a_    :       Optional[Any]    	=     3
           a_    :       List[str]    	=     8
           a_    :       Dict    	=     8

           a_    :       List[str]    	=     batch_size * num_channels * height * width
           a_    :       Any    	=     torch.arange(_lowerCamelCase					)
           a_    :       Any    	=     sample.reshape(_lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase					)
           a_    :       Dict    	=     sample / num_elems
           a_    :       Optional[int]    	=     sample.permute(3	,	0	,	1	,	2					)

           return sample






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           def model(lowercase__      :     Any	,	lowercase__      :     Any	,	*lowercase__      :     Any					):
                return sample * t / (t + 1)

           return model






      def        lowercase_	(   self      :     Dict	,	**lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       List[str]    	=     {
               """num_train_timesteps""": 2000,
               """snr""": 0.15,
               """sigma_min""": 0.01,
               """sigma_max""": 1348,
               """sampling_eps""": 1e-5,
           }

           config.update(**_lowerCamelCase					)
           return config






      def        lowercase_	(   self      :     List[Any]	,	lowercase__      :     Tuple=0	,	**lowercase__      :     Optional[int]					):


           '''simple docstring'''

           a_    :       str    	=     dict(self.forward_default_kwargs					)

           for scheduler_class in self.scheduler_classes:
                a_    :       int    	=     self.dummy_sample
                a_    :       Tuple    	=     0.1 * sample

                a_    :       int    	=     self.get_scheduler_config(**_lowerCamelCase					)
                a_    :       Dict    	=     scheduler_class(**_lowerCamelCase					)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(_lowerCamelCase					)
                     a_    :       Union[str, Any]    	=     scheduler_class.from_pretrained(_lowerCamelCase					)

                a_    :       Tuple    	=     scheduler.step_pred(
                    _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample
                a_    :       Optional[int]    	=     new_scheduler.step_pred(
                    _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample

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

                a_    :       Optional[int]    	=     scheduler.step_correct(_lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample
                a_    :       Union[str, Any]    	=     new_scheduler.step_correct(
                    _lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample

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






      def        lowercase_	(   self      :     int	,	lowercase__      :     List[str]=0	,	**lowercase__      :     Dict					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     dict(self.forward_default_kwargs					)
           kwargs.update(_lowerCamelCase					)

           for scheduler_class in self.scheduler_classes:
                a_    :       List[str]    	=     self.dummy_sample
                a_    :       List[str]    	=     0.1 * sample

                a_    :       Optional[Any]    	=     self.get_scheduler_config()
                a_    :       Optional[Any]    	=     scheduler_class(**_lowerCamelCase					)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(_lowerCamelCase					)
                     a_    :       Union[str, Any]    	=     scheduler_class.from_pretrained(_lowerCamelCase					)

                a_    :       Optional[int]    	=     scheduler.step_pred(
                    _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample
                a_    :       Any    	=     new_scheduler.step_pred(
                    _lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample

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

                a_    :       Optional[Any]    	=     scheduler.step_correct(_lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample
                a_    :       Dict    	=     new_scheduler.step_correct(
                    _lowerCamelCase	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample

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






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase					)






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]	,	[1, 100, 1000]					):
                self.check_over_configs(sigma_min=_lowerCamelCase	,	sigma_max=_lowerCamelCase					)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase					)






      def        lowercase_	(   self      :     Any					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     dict(self.forward_default_kwargs					)

           a_    :       int    	=     self.scheduler_classes[0]
           a_    :       int    	=     self.get_scheduler_config()
           a_    :       Dict    	=     scheduler_class(**_lowerCamelCase					)

           a_    :       List[str]    	=     3

           a_    :       List[Any]    	=     self.dummy_model()
           a_    :       int    	=     self.dummy_sample_deter

           scheduler.set_sigmas(_lowerCamelCase					)
           scheduler.set_timesteps(_lowerCamelCase					)
           a_    :       Tuple    	=     torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
                a_    :       Tuple    	=     scheduler.sigmas[i]

                for _ in range(scheduler.config.correct_steps					):
                     with torch.no_grad():
                          a_    :       Optional[int]    	=     model(_lowerCamelCase	,	_lowerCamelCase					)
                     a_    :       List[str]    	=     scheduler.step_correct(_lowerCamelCase	,	_lowerCamelCase	,	generator=_lowerCamelCase	,	**_lowerCamelCase					).prev_sample

                with torch.no_grad():
                     a_    :       Tuple    	=     model(_lowerCamelCase	,	_lowerCamelCase					)

                a_    :       str    	=     scheduler.step_pred(_lowerCamelCase	,	_lowerCamelCase	,	_lowerCamelCase	,	generator=_lowerCamelCase	,	**_lowerCamelCase					)
                a_    :       List[str]    	=     output.prev_sample, output.prev_sample_mean

           a_    :       int    	=     torch.sum(torch.abs(_lowerCamelCase					)					)
           a_    :       str    	=     torch.mean(torch.abs(_lowerCamelCase					)					)

           assert np.isclose(result_sum.item()	,	143_7275_8528.0					)
           assert np.isclose(result_mean.item()	,	1871_4530.0					)






      def        lowercase_	(   self      :     int					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     dict(self.forward_default_kwargs					)

           a_    :       Union[str, Any]    	=     kwargs.pop("""num_inference_steps"""	,	_lowerCamelCase					)

           for scheduler_class in self.scheduler_classes:
                a_    :       Dict    	=     self.get_scheduler_config()
                a_    :       List[Any]    	=     scheduler_class(**_lowerCamelCase					)

                a_    :       str    	=     self.dummy_sample
                a_    :       Tuple    	=     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_    :       Dict    	=     num_inference_steps

                a_    :       str    	=     scheduler.step_pred(_lowerCamelCase	,	0	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample
                a_    :       int    	=     scheduler.step_pred(_lowerCamelCase	,	1	,	_lowerCamelCase	,	generator=torch.manual_seed(0					)	,	**_lowerCamelCase					).prev_sample

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






'''simple docstring'''


def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				int = 1000							):




     """simple docstring"""


     return sum(i for i in range(UpperCamelCase__							) if i % 3 == 0 or i % 5 == 0							)


if __name__ == "__main__":
 print(f"{solution() = }")
