




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class       a__	( UpperCamelCase__				):
     a		:      List[str]		 =     (DEISMultistepScheduler,)
     a		:      Union[str, Any]		 =     (('num_inference_steps', 25),)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   List[Any]:



          '''simple docstring'''



          a     =				{
              "num_train_timesteps": 1000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "solver_order": 2,
          }

          config.update(**__lowerCAmelCase			)
          return config







     def       lowerCAmelCase_    (     self  ,		A=0  ,		**A			)  ->   List[Any]:



          '''simple docstring'''



          a     =				dict(self.forward_default_kwargs			)
          a     =				kwargs.pop("num_inference_steps"  ,		__lowerCAmelCase			)
          a     =				self.dummy_sample
          a     =				0.1 * sample
          a     =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
               a     =				self.get_scheduler_config(**__lowerCAmelCase			)
               a     =				scheduler_class(**__lowerCAmelCase			)
               scheduler.set_timesteps(__lowerCAmelCase			)
               # copy over dummy past residuals
               a     =				dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(__lowerCAmelCase			)
                    a     =				scheduler_class.from_pretrained(__lowerCAmelCase			)
                    new_scheduler.set_timesteps(__lowerCAmelCase			)
                    # copy over dummy past residuals
                    a     =				dummy_past_residuals[: new_scheduler.config.solver_order]

               a  ,				a     =				sample, sample
               for t in range(__lowerCAmelCase  ,		time_step + scheduler.config.solver_order + 1			):
                    a     =				scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase  ,		**__lowerCAmelCase			).prev_sample
                    a     =				new_scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase  ,		**__lowerCAmelCase			).prev_sample

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







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



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self  ,		A=0  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				dict(self.forward_default_kwargs			)
          a     =				kwargs.pop("num_inference_steps"  ,		__lowerCAmelCase			)
          a     =				self.dummy_sample
          a     =				0.1 * sample
          a     =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

          for scheduler_class in self.scheduler_classes:
               a     =				self.get_scheduler_config()
               a     =				scheduler_class(**__lowerCAmelCase			)
               scheduler.set_timesteps(__lowerCAmelCase			)

               # copy over dummy past residuals (must be after setting timesteps)
               a     =				dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(__lowerCAmelCase			)
                    a     =				scheduler_class.from_pretrained(__lowerCAmelCase			)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(__lowerCAmelCase			)

                    # copy over dummy past residual (must be after setting timesteps)
                    a     =				dummy_past_residuals[: new_scheduler.config.solver_order]

               a     =				scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase  ,		**__lowerCAmelCase			).prev_sample
               a     =				new_scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase  ,		**__lowerCAmelCase			).prev_sample

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







     def       lowerCAmelCase_    (     self  ,		A=None  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          if scheduler is None:
               a     =				self.scheduler_classes[0]
               a     =				self.get_scheduler_config(**__lowerCAmelCase			)
               a     =				scheduler_class(**__lowerCAmelCase			)

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

          a     =				10
          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter
          scheduler.set_timesteps(__lowerCAmelCase			)

          for i, t in enumerate(scheduler.timesteps			):
               a     =				model(__lowerCAmelCase  ,		__lowerCAmelCase			)
               a     =				scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase			).prev_sample

          return sample







     def       lowerCAmelCase_    (     self			)  ->   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

               # copy over dummy past residuals (must be done after set_timesteps)
               a     =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
               a     =				dummy_past_residuals[: scheduler.config.solver_order]

               a     =				scheduler.timesteps[5]
               a     =				scheduler.timesteps[6]

               a     =				scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase  ,		**__lowerCAmelCase			).prev_sample
               a     =				scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase  ,		**__lowerCAmelCase			).prev_sample

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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				DEISMultistepScheduler(**self.get_scheduler_config()			)
          a     =				self.full_loop(scheduler=__lowerCAmelCase			)
          a     =				torch.mean(torch.abs(__lowerCAmelCase			)			)

          assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

          a     =				DPMSolverSinglestepScheduler.from_config(scheduler.config			)
          a     =				DPMSolverMultistepScheduler.from_config(scheduler.config			)
          a     =				UniPCMultistepScheduler.from_config(scheduler.config			)
          a     =				DEISMultistepScheduler.from_config(scheduler.config			)

          a     =				self.full_loop(scheduler=__lowerCAmelCase			)
          a     =				torch.mean(torch.abs(__lowerCAmelCase			)			)

          assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCAmelCase			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          self.check_over_configs(thresholding=__lowerCAmelCase			)
          for order in [1, 2, 3]:
               for solver_type in ["logrho"]:
                    for threshold in [0.5, 1.0, 2.0]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  thresholding=__lowerCAmelCase  ,		prediction_type=__lowerCAmelCase  ,		sample_max_value=__lowerCAmelCase  ,		algorithm_type="deis"  ,		solver_order=__lowerCAmelCase  ,		solver_type=__lowerCAmelCase  ,		)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCAmelCase			)







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



          '''simple docstring'''



          for algorithm_type in ["deis"]:
               for solver_type in ["logrho"]:
                    for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                              self.check_over_configs(
                                  solver_order=__lowerCAmelCase  ,		solver_type=__lowerCAmelCase  ,		prediction_type=__lowerCAmelCase  ,		algorithm_type=__lowerCAmelCase  ,		)
                              a     =				self.full_loop(
                                  solver_order=__lowerCAmelCase  ,		solver_type=__lowerCAmelCase  ,		prediction_type=__lowerCAmelCase  ,		algorithm_type=__lowerCAmelCase  ,		)
                              assert not torch.isnan(__lowerCAmelCase			).any(), "Samples have nan numbers"







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          self.check_over_configs(lower_order_final=__lowerCAmelCase			)
          self.check_over_configs(lower_order_final=__lowerCAmelCase			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=__lowerCAmelCase  ,		time_step=0			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.full_loop()
          a     =				torch.mean(torch.abs(__lowerCAmelCase			)			)

          assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.full_loop(prediction_type="v_prediction"			)
          a     =				torch.mean(torch.abs(__lowerCAmelCase			)			)

          assert abs(result_mean.item() - 0.0_9_1			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(thresholding=__lowerCAmelCase  ,		dynamic_thresholding_ratio=0			)
          a     =				scheduler_class(**__lowerCAmelCase			)

          a     =				10
          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter.half()
          scheduler.set_timesteps(__lowerCAmelCase			)

          for i, t in enumerate(scheduler.timesteps			):
               a     =				model(__lowerCAmelCase  ,		__lowerCAmelCase			)
               a     =				scheduler.step(__lowerCAmelCase  ,		__lowerCAmelCase  ,		__lowerCAmelCase			).prev_sample

          assert sample.dtype == torch.floataa







import math
import unittest

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     assert isinstance(__UpperCamelCase		, __UpperCamelCase) and (
         number >= 0
     ), "'number' must been an int and positive"

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5		, int(math.sqrt(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True




class       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          self.assertTrue(is_prime(2			)			)
          self.assertTrue(is_prime(3			)			)
          self.assertTrue(is_prime(5			)			)
          self.assertTrue(is_prime(7			)			)
          self.assertTrue(is_prime(11			)			)
          self.assertTrue(is_prime(13			)			)
          self.assertTrue(is_prime(17			)			)
          self.assertTrue(is_prime(19			)			)
          self.assertTrue(is_prime(23			)			)
          self.assertTrue(is_prime(29			)			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          with self.assertRaises(A			):
               is_prime(-19			)
          self.assertFalse(
              is_prime(0			)  ,		"Zero doesn't have any positive factors, primes must have exactly two."  ,		)
          self.assertFalse(
              is_prime(1			)  ,		"One only has 1 positive factor, primes must have exactly two."  ,		)
          self.assertFalse(is_prime(2 * 2			)			)
          self.assertFalse(is_prime(2 * 3			)			)
          self.assertFalse(is_prime(3 * 3			)			)
          self.assertFalse(is_prime(3 * 5			)			)
          self.assertFalse(is_prime(3 * 5 * 7			)			)


if __name__ == "__main__":
   unittest.main()

