






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= (PNDMScheduler,)
				lowercase__       							= (("""num_inference_steps""", 50),)




				def        lowercase__     (  self		:					Any,						**lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    {
								    '''num_train_timesteps''': 1_000,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**lowerCamelCase						)
								return config




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					int=0,						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)
								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)
								lowercase__     				=    self.dummy_sample
								lowercase__     				=    0.1 * sample
								lowercase__     				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
												lowercase__     				=    scheduler_class(**lowerCamelCase						)
												scheduler.set_timesteps(lowerCamelCase						)
												# copy over dummy past residuals
												lowercase__     				=    dummy_past_residuals[:]

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

												lowercase__     				=    scheduler.step_prk(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    new_scheduler.step_prk(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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

												lowercase__     				=    scheduler.step_plms(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    new_scheduler.step_plms(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					Dict=0,						**lowerCamelCase		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)
								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)
								lowercase__     				=    self.dummy_sample
								lowercase__     				=    0.1 * sample
								lowercase__     				=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config()
												lowercase__     				=    scheduler_class(**lowerCamelCase						)
												scheduler.set_timesteps(lowerCamelCase						)

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase						)
																lowercase__     				=    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)
																lowercase__     				=    dummy_past_residuals[:]

												lowercase__     				=    scheduler.step_prk(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    new_scheduler.step_prk(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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

												lowercase__     				=    scheduler.step_plms(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    new_scheduler.step_plms(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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




				def        lowercase__     (  self		:					List[Any],						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(**lowerCamelCase						)
								lowercase__     				=    scheduler_class(**lowerCamelCase						)

								lowercase__     				=    10
								lowercase__     				=    self.dummy_model()
								lowercase__     				=    self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase						)

								for i, t in enumerate(scheduler.prk_timesteps						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step_prk(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps						):
												lowercase__     				=    model(lowerCamelCase,						lowerCamelCase						)
												lowercase__     				=    scheduler.step_plms(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample

								return sample




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    dict(self.forward_default_kwargs						)

								lowercase__     				=    kwargs.pop('''num_inference_steps''',						lowerCamelCase						)

								for scheduler_class in self.scheduler_classes:
												lowercase__     				=    self.get_scheduler_config()
												lowercase__     				=    scheduler_class(**lowerCamelCase						)

												lowercase__     				=    self.dummy_sample
												lowercase__     				=    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'''						):
																lowercase__     				=    num_inference_steps

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

												lowercase__     				=    scheduler.step_prk(lowerCamelCase,						0,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    scheduler.step_prk(lowerCamelCase,						1,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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

												lowercase__     				=    scheduler.step_plms(lowerCamelCase,						0,						lowerCamelCase,						**lowerCamelCase						).prev_sample
												lowercase__     				=    scheduler.step_plms(lowerCamelCase,						1,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=lowerCamelCase						)

								lowercase__     				=    self.scheduler_classes[0]
								lowercase__     				=    self.get_scheduler_config(steps_offset=1						)
								lowercase__     				=    scheduler_class(**lowerCamelCase						)
								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        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								for beta_start, beta_end in zip([0.0001, 0.001],						[0.002, 0.02]						):
												self.check_over_configs(beta_start=lowerCamelCase,						beta_end=lowerCamelCase						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								for t in [1, 5, 10]:
												self.check_over_forward(time_step=lowerCamelCase						)




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],						[10, 50, 100]						):
												self.check_over_forward(num_inference_steps=lowerCamelCase						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
								lowercase__     				=    27

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

												lowercase__     				=    self.get_scheduler_config()
												lowercase__     				=    scheduler_class(**lowerCamelCase						)

												scheduler.set_timesteps(lowerCamelCase						)

												# 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__     				=    scheduler.step_prk(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						).prev_sample




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								with self.assertRaises(lowerCamelCase						):
												lowercase__     				=    self.scheduler_classes[0]
												lowercase__     				=    self.get_scheduler_config()
												lowercase__     				=    scheduler_class(**lowerCamelCase						)

												scheduler.step_plms(self.dummy_sample,						1,						self.dummy_sample						).prev_sample




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop()
								lowercase__     				=    torch.sum(torch.abs(lowerCamelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_sum.item() - 198.1318						) < 1E-2
								assert abs(result_mean.item() - 0.2580						) < 1E-3




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.full_loop(prediction_type='''v_prediction'''						)
								lowercase__     				=    torch.sum(torch.abs(lowerCamelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_sum.item() - 67.3986						) < 1E-2
								assert abs(result_mean.item() - 0.0878						) < 1E-3




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								# We specify different beta, so that the first alpha is 0.99
								lowercase__     				=    self.full_loop(set_alpha_to_one=lowerCamelCase,						beta_start=0.01						)
								lowercase__     				=    torch.sum(torch.abs(lowerCamelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_sum.item() - 230.0399						) < 1E-2
								assert abs(result_mean.item() - 0.2995						) < 1E-3




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								# We specify different beta, so that the first alpha is 0.99
								lowercase__     				=    self.full_loop(set_alpha_to_one=lowerCamelCase,						beta_start=0.01						)
								lowercase__     				=    torch.sum(torch.abs(lowerCamelCase						)						)
								lowercase__     				=    torch.mean(torch.abs(lowerCamelCase						)						)

								assert abs(result_sum.item() - 186.9482						) < 1E-2
								assert abs(result_mean.item() - 0.2434						) < 1E-3












def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''

				if exponent == 1:
								return base
				if exponent % 2 == 0:
								lowercase__     				=    _modexpt(lowerCamelCase_	,     exponent // 2	,     lowerCamelCase_       ) % modulo_value
								return (x * x) % modulo_value
				else:
								return (base * _modexpt(lowerCamelCase_	,     exponent - 1	,     lowerCamelCase_       )) % modulo_value






def 						a    (		lowerCamelCase_ = 1777	,     lowerCamelCase_ = 1855	,     lowerCamelCase_ = 8       ):






				'''simple docstring'''
				lowercase__     				=    base
				for _ in range(1	,     lowerCamelCase_       ):
								lowercase__     				=    _modexpt(lowerCamelCase_	,     lowerCamelCase_	,     10**digits       )

				return result


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




