

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 				A_					(						unittest.TestCase   ):
     # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
     _lowercase     :	Optional[int]        =      (ScoreSdeVeScheduler,)
     _lowercase     :	List[Any]        =      ()


     @property
     def     UpperCAmelCase    (				self     :						Dict   )				->      int:
      __lowerCAmelCase:  Optional[int]        = 4
      __lowerCAmelCase:  Tuple        = 3
      __lowerCAmelCase:  List[Any]        = 8
      __lowerCAmelCase:  List[Any]        = 8

      __lowerCAmelCase:  str        = torch.rand((batch_size, num_channels, height, width)   )

      return sample


     @property
     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      str:
      __lowerCAmelCase:  Tuple        = 4
      __lowerCAmelCase:  Union[str, Any]        = 3
      __lowerCAmelCase:  Union[str, Any]        = 8
      __lowerCAmelCase:  List[str]        = 8

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

      return sample


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      List[Any]:
      def model(UpperCAmelCase     :						str     ,    UpperCAmelCase     :						Any     ,    *UpperCAmelCase     :						List[str]   ):
       return sample * t / (t + 1)

      return model


     def     UpperCAmelCase    (				self     :						int     ,    **UpperCAmelCase     :						Tuple   )				->      Union[str, Any]:
      __lowerCAmelCase:  Optional[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     UpperCAmelCase    (				self     :						Dict     ,    UpperCAmelCase     :						Tuple=0     ,    **UpperCAmelCase     :						Dict   )				->      Optional[Any]:
      __lowerCAmelCase:  int        = dict(self.forward_default_kwargs   )

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

       __lowerCAmelCase:  Dict        = self.get_scheduler_config(**UpperCAmelCase   )
       __lowerCAmelCase:  str        = scheduler_class(**UpperCAmelCase   )

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

       __lowerCAmelCase:  Dict        = scheduler.step_pred(
           UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    generator=torch.manual_seed(0   )     ,    **UpperCAmelCase   ).prev_sample
       __lowerCAmelCase:  List[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"

       __lowerCAmelCase:  Any        = scheduler.step_correct(UpperCAmelCase     ,    UpperCAmelCase     ,    generator=torch.manual_seed(0   )     ,    **UpperCAmelCase   ).prev_sample
       __lowerCAmelCase:  Optional[int]        = 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     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						Tuple=0     ,    **UpperCAmelCase     :						List[str]   )				->      List[Any]:
      __lowerCAmelCase:  str        = dict(self.forward_default_kwargs   )
      kwargs.update(UpperCAmelCase   )

      for scheduler_class in self.scheduler_classes:
       __lowerCAmelCase:  str        = self.dummy_sample
       __lowerCAmelCase:  Any        = 0.1 * sample

       __lowerCAmelCase:  int        = self.get_scheduler_config()
       __lowerCAmelCase:  Optional[Any]        = scheduler_class(**UpperCAmelCase   )

       with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCAmelCase   )
        __lowerCAmelCase:  int        = scheduler_class.from_pretrained(UpperCAmelCase   )

       __lowerCAmelCase:  List[str]        = scheduler.step_pred(
           UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    generator=torch.manual_seed(0   )     ,    **UpperCAmelCase   ).prev_sample
       __lowerCAmelCase:  int        = 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"

       __lowerCAmelCase:  List[Any]        = scheduler.step_correct(UpperCAmelCase     ,    UpperCAmelCase     ,    generator=torch.manual_seed(0   )     ,    **UpperCAmelCase   ).prev_sample
       __lowerCAmelCase:  Optional[int]        = 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     UpperCAmelCase    (				self     :						Dict   )				->      str:
      for timesteps in [1_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      Dict:
      for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]     ,    [1, 1_0_0, 1_0_0_0]   ):
       self.check_over_configs(sigma_min=UpperCAmelCase     ,    sigma_max=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Dict   )				->      Tuple:
      for t in [0.1, 0.5, 0.75]:
       self.check_over_forward(time_step=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      List[Any]:
      __lowerCAmelCase:  Union[str, Any]        = dict(self.forward_default_kwargs   )

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

      __lowerCAmelCase:  Optional[int]        = 3

      __lowerCAmelCase:  Tuple        = self.dummy_model()
      __lowerCAmelCase:  Union[str, Any]        = self.dummy_sample_deter

      scheduler.set_sigmas(UpperCAmelCase   )
      scheduler.set_timesteps(UpperCAmelCase   )
      __lowerCAmelCase:  Dict        = torch.manual_seed(0   )

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

       for _ in range(scheduler.config.correct_steps   ):
        with torch.no_grad():
         __lowerCAmelCase:  Tuple        = model(UpperCAmelCase     ,    UpperCAmelCase   )
        __lowerCAmelCase:  str        = scheduler.step_correct(UpperCAmelCase     ,    UpperCAmelCase     ,    generator=UpperCAmelCase     ,    **UpperCAmelCase   ).prev_sample

       with torch.no_grad():
        __lowerCAmelCase:  Union[str, Any]        = model(UpperCAmelCase     ,    UpperCAmelCase   )

       __lowerCAmelCase:  Tuple        = scheduler.step_pred(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    generator=UpperCAmelCase     ,    **UpperCAmelCase   )
       __lowerCAmelCase      ,				__lowerCAmelCase:  List[str]        = output.prev_sample, output.prev_sample_mean

      __lowerCAmelCase:  Optional[int]        = torch.sum(torch.abs(UpperCAmelCase   )   )
      __lowerCAmelCase:  Optional[int]        = torch.mean(torch.abs(UpperCAmelCase   )   )

      assert np.isclose(result_sum.item()     ,    14372758528.0   )
      assert np.isclose(result_mean.item()     ,    18714530.0   )


     def     UpperCAmelCase    (				self     :						Any   )				->      Dict:
      __lowerCAmelCase:  Tuple        = dict(self.forward_default_kwargs   )

      __lowerCAmelCase:  Optional[int]        = kwargs.pop('num_inference_steps'     ,    UpperCAmelCase   )

      for scheduler_class in self.scheduler_classes:
       __lowerCAmelCase:  Tuple        = self.get_scheduler_config()
       __lowerCAmelCase:  Dict        = scheduler_class(**UpperCAmelCase   )

       __lowerCAmelCase:  List[str]        = self.dummy_sample
       __lowerCAmelCase:  List[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'   ):
        __lowerCAmelCase:  Optional[int]        = num_inference_steps

       __lowerCAmelCase:  Any        = scheduler.step_pred(UpperCAmelCase     ,    0     ,    UpperCAmelCase     ,    generator=torch.manual_seed(0   )     ,    **UpperCAmelCase   ).prev_sample
       __lowerCAmelCase:  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					(			SCREAMING_SNAKE_CASE						:     int						)       ->       int:

 """simple docstring"""






 return n * (2 * n - 1)







def _a					(			SCREAMING_SNAKE_CASE						:     int						)       ->       bool:

 """simple docstring"""






 __lowerCAmelCase:  List[Any]        = (1 + 24 * n) ** 0.5
 return ((1 + root) / 6) % 1 == 0







def _a					(			SCREAMING_SNAKE_CASE						:     int = 1_44						)       ->       int:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = start
 __lowerCAmelCase:  List[str]        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 while not is_pentagonal(SCREAMING_SNAKE_CASE						):
  n += 1
  __lowerCAmelCase:  Any        = hexagonal_num(SCREAMING_SNAKE_CASE						)
 return num


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





