




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)
     a		:			Tuple							=	(ScoreSdeVeScheduler,)
     a		:			Dict							=	()







     @property
     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      4
          a      =      3
          a      =      8
          a      =      8

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

          return sample







     @property
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      4
          a      =      3
          a      =      8
          a      =      8

          a      =      batch_size * num_channels * height * width
          a      =      torch.arange(lowerCAmelCase__ )
          a      =      sample.reshape(lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__ )
          a      =      sample / num_elems
          a      =      sample.permute(3  ,		0  ,		1  ,		2 )

          return sample







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



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

          return model







     def 			lowerCAmelCase_				(	self  ,		**A )  ->    Dict:



          '''simple docstring'''



          a      =      {
              """num_train_timesteps""": 2000,
              """snr""": 0.1_5,
              """sigma_min""": 0.0_1,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }

          config.update(**lowerCAmelCase__ )
          return config







     def 			lowerCAmelCase_				(	self  ,		A=0  ,		**A )  ->    Optional[int]:



          '''simple docstring'''



          a      =      dict(self.forward_default_kwargs )

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

               a      =      self.get_scheduler_config(**lowerCAmelCase__ )
               a      =      scheduler_class(**lowerCAmelCase__ )

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase__ )
                    a      =      scheduler_class.from_pretrained(lowerCAmelCase__ )

               a      =      scheduler.step_pred(
                   lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		generator=torch.manual_seed(0 )  ,		**lowerCAmelCase__ ).prev_sample
               a      =      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      =      scheduler.step_correct(lowerCAmelCase__  ,		lowerCAmelCase__  ,		generator=torch.manual_seed(0 )  ,		**lowerCAmelCase__ ).prev_sample
               a      =      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 			lowerCAmelCase_				(	self  ,		A=0  ,		**A )  ->    str:



          '''simple docstring'''



          a      =      dict(self.forward_default_kwargs )
          kwargs.update(lowerCAmelCase__ )

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

               a      =      self.get_scheduler_config()
               a      =      scheduler_class(**lowerCAmelCase__ )

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase__ )
                    a      =      scheduler_class.from_pretrained(lowerCAmelCase__ )

               a      =      scheduler.step_pred(
                   lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		generator=torch.manual_seed(0 )  ,		**lowerCAmelCase__ ).prev_sample
               a      =      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      =      scheduler.step_correct(lowerCAmelCase__  ,		lowerCAmelCase__  ,		generator=torch.manual_seed(0 )  ,		**lowerCAmelCase__ ).prev_sample
               a      =      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 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]  ,		[1, 100, 1000] ):
               self.check_over_configs(sigma_min=lowerCAmelCase__  ,		sigma_max=lowerCAmelCase__ )







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          for t in [0.1, 0.5, 0.7_5]:
               self.check_over_forward(time_step=lowerCAmelCase__ )







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



          '''simple docstring'''



          a      =      dict(self.forward_default_kwargs )

          a      =      self.scheduler_classes[0]
          a      =      self.get_scheduler_config()
          a      =      scheduler_class(**lowerCAmelCase__ )

          a      =      3

          a      =      self.dummy_model()
          a      =      self.dummy_sample_deter

          scheduler.set_sigmas(lowerCAmelCase__ )
          scheduler.set_timesteps(lowerCAmelCase__ )
          a      =      torch.manual_seed(0 )

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

               for _ in range(scheduler.config.correct_steps ):
                    with torch.no_grad():
                         a      =      model(lowerCAmelCase__  ,		lowerCAmelCase__ )
                    a      =      scheduler.step_correct(lowerCAmelCase__  ,		lowerCAmelCase__  ,		generator=lowerCAmelCase__  ,		**lowerCAmelCase__ ).prev_sample

               with torch.no_grad():
                    a      =      model(lowerCAmelCase__  ,		lowerCAmelCase__ )

               a      =      scheduler.step_pred(lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__  ,		generator=lowerCAmelCase__  ,		**lowerCAmelCase__ )
               a      =      output.prev_sample, output.prev_sample_mean

          a      =      torch.sum(torch.abs(lowerCAmelCase__ ) )
          a      =      torch.mean(torch.abs(lowerCAmelCase__ ) )

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







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      dict(self.forward_default_kwargs )

          a      =      kwargs.pop("num_inference_steps"  ,		lowerCAmelCase__ )

          for scheduler_class in self.scheduler_classes:
               a      =      self.get_scheduler_config()
               a      =      scheduler_class(**lowerCAmelCase__ )

               a      =      self.dummy_sample
               a      =      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      =      num_inference_steps

               a      =      scheduler.step_pred(lowerCAmelCase__  ,		0  ,		lowerCAmelCase__  ,		generator=torch.manual_seed(0 )  ,		**lowerCAmelCase__ ).prev_sample
               a      =      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'''

from itertools import combinations, product







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     if xa == ya == 0 or xa == ya == 0:
          return False
     _UpperCAmelCase					:  Any         =    xa * xa + ya * ya
     _UpperCAmelCase					:  Any         =    xa * xa + ya * ya
     _UpperCAmelCase					:  Tuple         =    (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
     return (
         a_square + b_square == c_square
         or a_square + c_square == b_square
         or b_square + c_square == a_square
     )







def __A  (    lowerCAmelCase_ = 50       ):
     return sum(
         1
         for pta, pta in combinations(product(range(limit + 1       ) ,       repeat=2       ) ,       2       )
         if is_right(*lowerCAmelCase_ ,       *lowerCAmelCase_       )       )


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


