




'''simple docstring'''


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class     A					(	snake_case__					):
							lowercase_           =       (DPMSolverSDEScheduler,)
							lowercase_           =       10







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


														"""simple docstring"""


														_a			  =       {
														    "num_train_timesteps": 11_00,
														    "beta_start": 0.0_0_0_1,
														    "beta_end": 0.0_2,
														    "beta_schedule": "linear",
														    "noise_sampler_seed": 0,
														}

														config.update(**lowercase_      )
														return config







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


														"""simple docstring"""


														for timesteps in [10, 50, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowercase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							str:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]							,    [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																					self.check_over_configs(beta_start=lowercase_							,    beta_end=lowercase_      )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							List[Any]:


														"""simple docstring"""


														for schedule in ["linear", "scaled_linear"]:
																					self.check_over_configs(beta_schedule=lowercase_      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[Any]:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       self.scheduler_classes[0]
														_a			  =       self.get_scheduler_config()
														_a			  =       scheduler_class(**lowercase_      )

														scheduler.set_timesteps(self.num_inference_steps      )

														_a			  =       self.dummy_model()
														_a			  =       self.dummy_sample_deter * scheduler.init_noise_sigma
														_a			  =       sample.to(lowercase_      )

														for i, t in enumerate(scheduler.timesteps      ):
																					_a			  =       scheduler.scale_model_input(lowercase_							,    lowercase_      )

																					_a			  =       model(lowercase_							,    lowercase_      )

																					_a			  =       scheduler.step(lowercase_							,    lowercase_							,    lowercase_      )
																					_a			  =       output.prev_sample

														_a			  =       torch.sum(torch.abs(lowercase_      )      )
														_a			  =       torch.mean(torch.abs(lowercase_      )      )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7      ) < 1e-3
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2      ) < 1e-3
														else:
																					assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6      ) < 1e-3







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


														"""simple docstring"""


														_a			  =       self.scheduler_classes[0]
														_a			  =       self.get_scheduler_config(prediction_type='''v_prediction'''      )
														_a			  =       scheduler_class(**lowercase_      )

														scheduler.set_timesteps(self.num_inference_steps      )

														_a			  =       self.dummy_model()
														_a			  =       self.dummy_sample_deter * scheduler.init_noise_sigma
														_a			  =       sample.to(lowercase_      )

														for i, t in enumerate(scheduler.timesteps      ):
																					_a			  =       scheduler.scale_model_input(lowercase_							,    lowercase_      )

																					_a			  =       model(lowercase_							,    lowercase_      )

																					_a			  =       scheduler.step(lowercase_							,    lowercase_							,    lowercase_      )
																					_a			  =       output.prev_sample

														_a			  =       torch.sum(torch.abs(lowercase_      )      )
														_a			  =       torch.mean(torch.abs(lowercase_      )      )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3      ) < 1e-2
																					assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4      ) < 1e-3
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3      ) < 1e-2
																					assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7      ) < 1e-3
														else:
																					assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5      ) < 1e-2
																					assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1      ) < 1e-3







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self.scheduler_classes[0]
														_a			  =       self.get_scheduler_config()
														_a			  =       scheduler_class(**lowercase_      )

														scheduler.set_timesteps(self.num_inference_steps							,    device=lowercase_      )

														_a			  =       self.dummy_model()
														_a			  =       self.dummy_sample_deter.to(lowercase_      ) * scheduler.init_noise_sigma

														for t in scheduler.timesteps:
																					_a			  =       scheduler.scale_model_input(lowercase_							,    lowercase_      )

																					_a			  =       model(lowercase_							,    lowercase_      )

																					_a			  =       scheduler.step(lowercase_							,    lowercase_							,    lowercase_      )
																					_a			  =       output.prev_sample

														_a			  =       torch.sum(torch.abs(lowercase_      )      )
														_a			  =       torch.mean(torch.abs(lowercase_      )      )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5      ) < 1e-3
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1      ) < 1e-3
														else:
																					assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6      ) < 1e-3







							def      __lowerCAmelCase    (						self :						Dict      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.scheduler_classes[0]
														_a			  =       self.get_scheduler_config()
														_a			  =       scheduler_class(**lowercase_							,    use_karras_sigmas=lowercase_      )

														scheduler.set_timesteps(self.num_inference_steps							,    device=lowercase_      )

														_a			  =       self.dummy_model()
														_a			  =       self.dummy_sample_deter.to(lowercase_      ) * scheduler.init_noise_sigma
														_a			  =       sample.to(lowercase_      )

														for t in scheduler.timesteps:
																					_a			  =       scheduler.scale_model_input(lowercase_							,    lowercase_      )

																					_a			  =       model(lowercase_							,    lowercase_      )

																					_a			  =       scheduler.step(lowercase_							,    lowercase_							,    lowercase_      )
																					_a			  =       output.prev_sample

														_a			  =       torch.sum(torch.abs(lowercase_      )      )
														_a			  =       torch.mean(torch.abs(lowercase_      )      )

														if torch_device in ["mps"]:
																					assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1      ) < 1e-2
														elif torch_device in ["cuda"]:
																					assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1      ) < 1e-2
														else:
																					assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2      ) < 1e-2
																					assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1      ) < 1e-2







'''simple docstring'''


import math
import unittest
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


							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 :						Union[str, Any]      )    ->							Optional[Any]:


														"""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 :						Any      )    ->							Dict:


														"""simple docstring"""


														with self.assertRaises(lowerCAmelCase_      ):
																					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()

