






'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  (PNDMScheduler,)
	SCREAMING_SNAKE_CASE_					:      Tuple        =  (('num_inference_steps', 5_0),)
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  Union[str, Any]:
							lowercase_      :   Optional[Any]        =			{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**_lowercase       )
							return config
	def        lowerCamelCase__		(							self  ,      _lowercase=0  ,      **_lowercase       )						->  Dict:
							lowercase_      :   Optional[int]        =			dict(self.forward_default_kwargs       )
							lowercase_      :   Any        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )
							lowercase_      :   Any        =			self.dummy_sample
							lowercase_      :   List[Any]        =			0.1 * sample
							lowercase_      :   Tuple        =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
													lowercase_      :   Optional[int]        =			self.get_scheduler_config(**_lowercase       )
													lowercase_      :   Union[str, Any]        =			scheduler_class(**_lowercase       )
													scheduler.set_timesteps(_lowercase       )
													# copy over dummy past residuals
													lowercase_      :   str        =			dummy_past_residuals[:]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_lowercase       )
																			lowercase_      :   Optional[Any]        =			scheduler_class.from_pretrained(_lowercase       )
																			new_scheduler.set_timesteps(_lowercase       )
																			# copy over dummy past residuals
																			lowercase_      :   Dict        =			dummy_past_residuals[:]

													lowercase_      :   List[Any]        =			scheduler.step_prk(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Optional[Any]        =			new_scheduler.step_prk(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

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

													lowercase_      :   Any        =			scheduler.step_plms(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Dict        =			new_scheduler.step_plms(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

													assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							pass
	def        lowerCamelCase__		(							self  ,      _lowercase=0  ,      **_lowercase       )						->  Tuple:
							lowercase_      :   Optional[Any]        =			dict(self.forward_default_kwargs       )
							lowercase_      :   Union[str, Any]        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )
							lowercase_      :   Dict        =			self.dummy_sample
							lowercase_      :   List[str]        =			0.1 * sample
							lowercase_      :   Union[str, Any]        =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
													lowercase_      :   int        =			self.get_scheduler_config()
													lowercase_      :   List[Any]        =			scheduler_class(**_lowercase       )
													scheduler.set_timesteps(_lowercase       )

													# copy over dummy past residuals (must be after setting timesteps)
													lowercase_      :   Optional[Any]        =			dummy_past_residuals[:]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_lowercase       )
																			lowercase_      :   Optional[Any]        =			scheduler_class.from_pretrained(_lowercase       )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(_lowercase       )

																			# copy over dummy past residual (must be after setting timesteps)
																			lowercase_      :   Optional[Any]        =			dummy_past_residuals[:]

													lowercase_      :   List[Any]        =			scheduler.step_prk(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Optional[int]        =			new_scheduler.step_prk(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

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

													lowercase_      :   Any        =			scheduler.step_plms(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Any        =			new_scheduler.step_plms(_lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       ).prev_sample

													assert torch.sum(torch.abs(output - new_output       )       ) < 1E-5, "Scheduler outputs are not identical"
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  str:
							lowercase_      :   Optional[Any]        =			self.scheduler_classes[0]
							lowercase_      :   List[Any]        =			self.get_scheduler_config(**_lowercase       )
							lowercase_      :   Any        =			scheduler_class(**_lowercase       )

							lowercase_      :   Optional[Any]        =			10
							lowercase_      :   int        =			self.dummy_model()
							lowercase_      :   str        =			self.dummy_sample_deter
							scheduler.set_timesteps(_lowercase       )

							for i, t in enumerate(scheduler.prk_timesteps       ):
													lowercase_      :   Optional[int]        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   Dict        =			scheduler.step_prk(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps       ):
													lowercase_      :   Tuple        =			model(_lowercase  ,      _lowercase       )
													lowercase_      :   Any        =			scheduler.step_plms(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample

							return sample
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   int        =			dict(self.forward_default_kwargs       )

							lowercase_      :   Optional[Any]        =			kwargs.pop('num_inference_steps'  ,      _lowercase       )

							for scheduler_class in self.scheduler_classes:
													lowercase_      :   Dict        =			self.get_scheduler_config()
													lowercase_      :   Any        =			scheduler_class(**_lowercase       )

													lowercase_      :   int        =			self.dummy_sample
													lowercase_      :   Tuple        =			0.1 * sample

													if num_inference_steps is not None and hasattr(_lowercase  ,      'set_timesteps'       ):
																			scheduler.set_timesteps(_lowercase       )
													elif num_inference_steps is not None and not hasattr(_lowercase  ,      'set_timesteps'       ):
																			lowercase_      :   Dict        =			num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													lowercase_      :   List[Any]        =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
													lowercase_      :   Optional[int]        =			dummy_past_residuals[:]

													lowercase_      :   Any        =			scheduler.step_prk(_lowercase  ,      0  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Optional[int]        =			scheduler.step_prk(_lowercase  ,      1  ,      _lowercase  ,      **_lowercase       ).prev_sample

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

													lowercase_      :   Optional[int]        =			scheduler.step_plms(_lowercase  ,      0  ,      _lowercase  ,      **_lowercase       ).prev_sample
													lowercase_      :   Any        =			scheduler.step_plms(_lowercase  ,      1  ,      _lowercase  ,      **_lowercase       ).prev_sample

													self.assertEqual(output_a.shape  ,      sample.shape       )
													self.assertEqual(output_a.shape  ,      output_a.shape       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							for timesteps in [100, 1000]:
													self.check_over_configs(num_train_timesteps=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=_lowercase       )

							lowercase_      :   Optional[int]        =			self.scheduler_classes[0]
							lowercase_      :   List[Any]        =			self.get_scheduler_config(steps_offset=1       )
							lowercase_      :   Optional[Any]        =			scheduler_class(**_lowercase       )
							scheduler.set_timesteps(10       )
							assert torch.equal(
							    scheduler.timesteps  ,      torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]       )  ,      )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							for beta_start, beta_end in zip([0.00_01, 0.0_01]  ,      [0.0_02, 0.02]       ):
													self.check_over_configs(beta_start=_lowercase  ,      beta_end=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  int:
							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Any:
							for t in [1, 5, 10]:
													self.check_over_forward(time_step=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Dict:
							for t, num_inference_steps in zip([1, 5, 10]  ,      [10, 50, 100]       ):
													self.check_over_forward(num_inference_steps=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							lowercase_      :   Optional[int]        =			27

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

													lowercase_      :   Optional[Any]        =			self.get_scheduler_config()
													lowercase_      :   Dict        =			scheduler_class(**_lowercase       )

													scheduler.set_timesteps(_lowercase       )

													# before power of 3 fix, would error on first step, so we only need to do two
													for i, t in enumerate(scheduler.prk_timesteps[:2]       ):
																			lowercase_      :   int        =			scheduler.step_prk(_lowercase  ,      _lowercase  ,      _lowercase       ).prev_sample
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							with self.assertRaises(_lowercase       ):
													lowercase_      :   Any        =			self.scheduler_classes[0]
													lowercase_      :   Optional[int]        =			self.get_scheduler_config()
													lowercase_      :   Optional[int]        =			scheduler_class(**_lowercase       )

													scheduler.step_plms(self.dummy_sample  ,      1  ,      self.dummy_sample       ).prev_sample
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   List[Any]        =			self.full_loop()
							lowercase_      :   Tuple        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   int        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_sum.item() - 1_98.13_18       ) < 1E-2
							assert abs(result_mean.item() - 0.25_80       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Tuple:
							lowercase_      :   Dict        =			self.full_loop(prediction_type='v_prediction'       )
							lowercase_      :   List[str]        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   int        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_sum.item() - 67.39_86       ) < 1E-2
							assert abs(result_mean.item() - 0.08_78       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  int:
							# We specify different beta, so that the first alpha is 0.99
							lowercase_      :   List[str]        =			self.full_loop(set_alpha_to_one=_lowercase  ,      beta_start=0.01       )
							lowercase_      :   Tuple        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   List[str]        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_sum.item() - 2_30.03_99       ) < 1E-2
							assert abs(result_mean.item() - 0.29_95       ) < 1E-3







	def        lowerCamelCase__		(							self       )						->  List[Any]:
							# We specify different beta, so that the first alpha is 0.99
							lowercase_      :   Union[str, Any]        =			self.full_loop(set_alpha_to_one=_lowercase  ,      beta_start=0.01       )
							lowercase_      :   Optional[int]        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   List[Any]        =			torch.mean(torch.abs(_lowercase       )       )

							assert abs(result_sum.item() - 1_86.94_82       ) < 1E-2
							assert abs(result_mean.item() - 0.24_34       ) < 1E-3








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	int						,			a		:	int							)			->			int:





						"""simple docstring"""
						while second != 0:
												lowercase_      :   Any        =			first & second
												first ^= second
												lowercase_      :   List[str]        =			c << 1
						return first


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A:      Union[str, Any]  				=				int(input("Enter the first number: ").strip())
						A:      Union[str, Any]  				=				int(input("Enter the second number: ").strip())
						print(f"""{add(first, second) = }""")
