






"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 						snake_case  (  unittest.TestCase       ):
    __magic_name__         =     (ScoreSdeVeScheduler,)
    __magic_name__         =     ()





    @property
    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			int				=   4
      a     :			Dict				=   3
      a     :			Tuple				=   8
      a     :			Optional[int]				=   8

      a     :			Tuple				=   torch.rand((batch_size, num_channels, height, width)			)

      return sample





    @property
    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			str				=   4
      a     :			str				=   3
      a     :			Any				=   8
      a     :			int				=   8

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

      return sample





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      def model(A	:					Union[str, Any]				,		A	:					str				,		*A	:					Any			):
        return sample * t / (t + 1)

      return model





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		**A	:					Optional[Any]			):


      '''simple docstring'''




      a     :			Any				=   {
          'num_train_timesteps': 2_0_0_0,
          'snr': 0.15,
          'sigma_min': 0.01,
          'sigma_max': 1_3_4_8,
          'sampling_eps': 1E-5,
      }

      config.update(**__UpperCAmelCase			)
      return config





    def 				lowerCamelCase__      (							self	:					int				,		A	:					Union[str, Any]=0				,		**A	:					str			):


      '''simple docstring'''




      a     :			int				=   dict(self.forward_default_kwargs			)

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

        a     :			List[Any]				=   self.get_scheduler_config(**__UpperCAmelCase			)
        a     :			int				=   scheduler_class(**__UpperCAmelCase			)

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__UpperCAmelCase			)
          a     :			List[str]				=   scheduler_class.from_pretrained(__UpperCAmelCase			)

        a     :			List[str]				=   scheduler.step_pred(
            __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample
        a     :			str				=   new_scheduler.step_pred(
            __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample

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

        a     :			Tuple				=   scheduler.step_correct(__UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample
        a     :			str				=   new_scheduler.step_correct(
            __UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample

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





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					int=0				,		**A	:					Tuple			):


      '''simple docstring'''




      a     :			List[Any]				=   dict(self.forward_default_kwargs			)
      kwargs.update(__UpperCAmelCase			)

      for scheduler_class in self.scheduler_classes:
        a     :			List[Any]				=   self.dummy_sample
        a     :			int				=   0.1 * sample

        a     :			Any				=   self.get_scheduler_config()
        a     :			Any				=   scheduler_class(**__UpperCAmelCase			)

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__UpperCAmelCase			)
          a     :			Optional[int]				=   scheduler_class.from_pretrained(__UpperCAmelCase			)

        a     :			Tuple				=   scheduler.step_pred(
            __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample
        a     :			Optional[Any]				=   new_scheduler.step_pred(
            __UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample

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

        a     :			Dict				=   scheduler.step_correct(__UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample
        a     :			Union[str, Any]				=   new_scheduler.step_correct(
            __UpperCAmelCase				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample

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





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for timesteps in [1_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=__UpperCAmelCase			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]				,		[1, 1_0_0, 1_0_0_0]			):
        self.check_over_configs(sigma_min=__UpperCAmelCase				,		sigma_max=__UpperCAmelCase			)





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Union[str, Any]				=   dict(self.forward_default_kwargs			)

      a     :			Dict				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Optional[int]				=   scheduler_class(**__UpperCAmelCase			)

      a     :			Dict				=   3

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter

      scheduler.set_sigmas(__UpperCAmelCase			)
      scheduler.set_timesteps(__UpperCAmelCase			)
      a     :			List[Any]				=   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[Any]				=   model(__UpperCAmelCase				,		__UpperCAmelCase			)
          a     :			Tuple				=   scheduler.step_correct(__UpperCAmelCase				,		__UpperCAmelCase				,		generator=__UpperCAmelCase				,		**__UpperCAmelCase			).prev_sample

        with torch.no_grad():
          a     :			str				=   model(__UpperCAmelCase				,		__UpperCAmelCase			)

        a     :			Optional[Any]				=   scheduler.step_pred(__UpperCAmelCase				,		__UpperCAmelCase				,		__UpperCAmelCase				,		generator=__UpperCAmelCase				,		**__UpperCAmelCase			)
        a,     a     :			Any				=   output.prev_sample, output.prev_sample_mean

      a     :			int				=   torch.sum(torch.abs(__UpperCAmelCase			)			)
      a     :			List[str]				=   torch.mean(torch.abs(__UpperCAmelCase			)			)

      assert np.isclose(result_sum.item()				,		1_4_3_7_2_7_5_8_5_2_8.0			)
      assert np.isclose(result_mean.item()				,		1_8_7_1_4_5_3_0.0			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




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

      a     :			Any				=   kwargs.pop('num_inference_steps'				,		__UpperCAmelCase			)

      for scheduler_class in self.scheduler_classes:
        a     :			int				=   self.get_scheduler_config()
        a     :			Any				=   scheduler_class(**__UpperCAmelCase			)

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

        if num_inference_steps is not None and hasattr(__UpperCAmelCase				,		'set_timesteps'			):
          scheduler.set_timesteps(__UpperCAmelCase			)
        elif num_inference_steps is not None and not hasattr(__UpperCAmelCase				,		'set_timesteps'			):
          a     :			Any				=   num_inference_steps

        a     :			Tuple				=   scheduler.step_pred(__UpperCAmelCase				,		0				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample
        a     :			Union[str, Any]				=   scheduler.step_pred(__UpperCAmelCase				,		1				,		__UpperCAmelCase				,		generator=torch.manual_seed(0			)				,		**__UpperCAmelCase			).prev_sample

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









def        __a     (    lowerCAmelCase_   :					list				)      -> bool:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						if len(lowerCAmelCase_				) == 1 and series[0] != 0:
												return True
						UpperCAmelCase_=   []
						UpperCAmelCase_=   len(lowerCAmelCase_				)
						for i in range(0			,lowerCAmelCase_				):
												if series[i] == 0:
																		raise ValueError("""Input series cannot have 0 as an element"""				)
												rec_series.append(1 / series[i]				)
						UpperCAmelCase_=   rec_series[1] - rec_series[0]
						for index in range(2			,lowerCAmelCase_				):
												if rec_series[index] - rec_series[index - 1] != common_diff:
																		return False
						return True



def        __a     (    lowerCAmelCase_   :					list				)      -> float:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""				)
						if len(lowerCAmelCase_				) == 0:
												raise ValueError("""Input list must be a non empty list"""				)
						UpperCAmelCase_=   0
						for val in series:
												answer += 1 / val
						return len(lowerCAmelCase_				) / answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()




