
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)
      _UpperCAmelCase       :					List[Any]					 =    (ScoreSdeVeScheduler,)
      _UpperCAmelCase       :					Optional[int]					 =    ()
      @property
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Union[str, Any]           =   4
       lowerCamelCase	:			Optional[int]           =   3
       lowerCamelCase	:			Any           =   8
       lowerCamelCase	:			str           =   8

       lowerCamelCase	:			Dict           =   torch.rand((batch_size, num_channels, height, width)   )

       return sample
      @property
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Dict           =   4
       lowerCamelCase	:			Any           =   3
       lowerCamelCase	:			str           =   8
       lowerCamelCase	:			List[Any]           =   8

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

       return sample
      def 		UpperCamelCase__							(   self   ):
       def model(__magic_name__					,						__magic_name__					,						*__magic_name__   ):
        return sample * t / (t + 1)

       return model
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       lowerCamelCase	:			List[str]           =   {
           """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(**__magic_name__   )
       return config
      def 		UpperCamelCase__							(   self					,						__magic_name__=0					,						**__magic_name__   ):
       lowerCamelCase	:			Tuple           =   dict(self.forward_default_kwargs   )

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

        lowerCamelCase	:			Optional[Any]           =   self.get_scheduler_config(**__magic_name__   )
        lowerCamelCase	:			List[Any]           =   scheduler_class(**__magic_name__   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__magic_name__   )
         lowerCamelCase	:			Tuple           =   scheduler_class.from_pretrained(__magic_name__   )

        lowerCamelCase	:			Any           =   scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			Union[str, Any]           =   new_scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

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

        lowerCamelCase	:			Optional[Any]           =   scheduler.step_correct(__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			Any           =   new_scheduler.step_correct(
            __magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"
      def 		UpperCamelCase__							(   self					,						__magic_name__=0					,						**__magic_name__   ):
       lowerCamelCase	:			List[str]           =   dict(self.forward_default_kwargs   )
       kwargs.update(__magic_name__   )

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

        lowerCamelCase	:			Dict           =   self.get_scheduler_config()
        lowerCamelCase	:			Union[str, Any]           =   scheduler_class(**__magic_name__   )

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__magic_name__   )
         lowerCamelCase	:			Tuple           =   scheduler_class.from_pretrained(__magic_name__   )

        lowerCamelCase	:			Optional[Any]           =   scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			str           =   new_scheduler.step_pred(
            __magic_name__					,						__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

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

        lowerCamelCase	:			Any           =   scheduler.step_correct(__magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			List[str]           =   new_scheduler.step_correct(
            __magic_name__					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

        assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler correction are not identical"
      def 		UpperCamelCase__							(   self   ):
       for timesteps in [1_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]					,						[1, 1_0_0, 1_0_0_0]   ):
        self.check_over_configs(sigma_min=__magic_name__					,						sigma_max=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       for t in [0.1, 0.5, 0.75]:
        self.check_over_forward(time_step=__magic_name__   )
      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			List[str]           =   dict(self.forward_default_kwargs   )

       lowerCamelCase	:			List[str]           =   self.scheduler_classes[0]
       lowerCamelCase	:			Union[str, Any]           =   self.get_scheduler_config()
       lowerCamelCase	:			List[str]           =   scheduler_class(**__magic_name__   )

       lowerCamelCase	:			Any           =   3

       lowerCamelCase	:			str           =   self.dummy_model()
       lowerCamelCase	:			Dict           =   self.dummy_sample_deter

       scheduler.set_sigmas(__magic_name__   )
       scheduler.set_timesteps(__magic_name__   )
       lowerCamelCase	:			str           =   torch.manual_seed(0   )

       for i, t in enumerate(scheduler.timesteps   ):
        lowerCamelCase	:			Union[str, Any]           =   scheduler.sigmas[i]

        for _ in range(scheduler.config.correct_steps   ):
         with torch.no_grad():
          lowerCamelCase	:			int           =   model(__magic_name__					,						__magic_name__   )
         lowerCamelCase	:			str           =   scheduler.step_correct(__magic_name__					,						__magic_name__					,						generator=__magic_name__					,						**__magic_name__   ).prev_sample

        with torch.no_grad():
         lowerCamelCase	:			Optional[Any]           =   model(__magic_name__					,						__magic_name__   )

        lowerCamelCase	:			str           =   scheduler.step_pred(__magic_name__					,						__magic_name__					,						__magic_name__					,						generator=__magic_name__					,						**__magic_name__   )
        lowerCamelCase    ,					lowerCamelCase	:			Tuple           =   output.prev_sample, output.prev_sample_mean

       lowerCamelCase	:			int           =   torch.sum(torch.abs(__magic_name__   )   )
       lowerCamelCase	:			Optional[Any]           =   torch.mean(torch.abs(__magic_name__   )   )

       assert np.isclose(result_sum.item()					,						14_372_758_528.0   )
       assert np.isclose(result_mean.item()					,						18_714_530.0   )






      def 		UpperCamelCase__							(   self   ):
       lowerCamelCase	:			Union[str, Any]           =   dict(self.forward_default_kwargs   )

       lowerCamelCase	:			Optional[Any]           =   kwargs.pop("""num_inference_steps"""					,						__magic_name__   )

       for scheduler_class in self.scheduler_classes:
        lowerCamelCase	:			Dict           =   self.get_scheduler_config()
        lowerCamelCase	:			Optional[int]           =   scheduler_class(**__magic_name__   )

        lowerCamelCase	:			str           =   self.dummy_sample
        lowerCamelCase	:			Tuple           =   0.1 * sample

        if num_inference_steps is not None and hasattr(__magic_name__					,						"""set_timesteps"""   ):
         scheduler.set_timesteps(__magic_name__   )
        elif num_inference_steps is not None and not hasattr(__magic_name__					,						"""set_timesteps"""   ):
         lowerCamelCase	:			List[Any]           =   num_inference_steps

        lowerCamelCase	:			str           =   scheduler.step_pred(__magic_name__					,						0					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample
        lowerCamelCase	:			Any           =   scheduler.step_pred(__magic_name__					,						1					,						__magic_name__					,						generator=torch.manual_seed(0   )					,						**__magic_name__   ).prev_sample

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






def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if n < 0:
  return 0
 # table[i] represents the number of ways to get to amount i
 lowerCamelCase	:			int           =   [0] * (n + 1)

 # There is exactly 1 way to get to zero(You pick no coins).
 lowerCamelCase	:			Any           =   1

 # Pick all coins one by one and update table[] values
 # after the index greater than or equal to the value of the
 # picked coin
 for coin_val in s:
  for j in range(lowerCamelCase,			n + 1							):
   table[j] += table[j - coin_val]

 return table[n]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




