import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     (UnCLIPScheduler,)







							def  UpperCamelCase_						(   self      :     Any ,			**_A      :     int		):
												_UpperCamelCase			     =     {
												    '''num_train_timesteps''': 1000,
												    '''variance_type''': '''fixed_small_log''',
												    '''clip_sample''': True,
												    '''clip_sample_range''': 1.0,
												    '''prediction_type''': '''epsilon''',
												}

												config.update(**_A		)
												return config







							def  UpperCamelCase_						(   self      :     Tuple		):
												for timesteps in [1, 5, 100, 1000]:
																	self.check_over_configs(num_train_timesteps=_A		)







							def  UpperCamelCase_						(   self      :     int		):
												for variance in ["fixed_small_log", "learned_range"]:
																	self.check_over_configs(variance_type=_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												for clip_sample_range in [1, 5, 10, 20]:
																	self.check_over_configs(clip_sample_range=_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												for prediction_type in ["epsilon", "sample"]:
																	self.check_over_configs(prediction_type=_A		)







							def  UpperCamelCase_						(   self      :     str		):
												for time_step in [0, 500, 999]:
																	for prev_timestep in [None, 5, 100, 250, 500, 750]:
																						if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																						self.check_over_forward(time_step=_A ,			prev_timestep=_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(variance_type='''fixed_small_log'''		)
												_UpperCamelCase			     =     scheduler_class(**_A		)

												assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000e-10		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.054_9625		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.999_4987		)		) < 1e-5







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config(variance_type='''learned_range'''		)
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase			     =     0.5

												assert scheduler._get_variance(1 ,			predicted_variance=_A		) - -10.171_2790 < 1e-5
												assert scheduler._get_variance(487 ,			predicted_variance=_A		) - -5.799_8052 < 1e-5
												assert scheduler._get_variance(999 ,			predicted_variance=_A		) - -0.001_0011 < 1e-5







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config()
												_UpperCamelCase			     =     scheduler_class(**_A		)

												_UpperCamelCase			     =     scheduler.timesteps

												_UpperCamelCase			     =     self.dummy_model()
												_UpperCamelCase			     =     self.dummy_sample_deter
												_UpperCamelCase			     =     torch.manual_seed(0		)

												for i, t in enumerate(_A		):
																	# 1. predict noise residual
																	_UpperCamelCase			     =     model(_A ,			_A		)

																	# 2. predict previous mean of sample x_t-1
																	_UpperCamelCase			     =     scheduler.step(_A ,			_A ,			_A ,			generator=_A		).prev_sample

																	_UpperCamelCase			     =     pred_prev_sample

												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

												assert abs(result_sum.item() - 252.268_2495		) < 1e-2
												assert abs(result_mean.item() - 0.328_4743		) < 1e-3







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     self.scheduler_classes[0]
												_UpperCamelCase			     =     self.get_scheduler_config()
												_UpperCamelCase			     =     scheduler_class(**_A		)

												scheduler.set_timesteps(25		)

												_UpperCamelCase			     =     scheduler.timesteps

												_UpperCamelCase			     =     self.dummy_model()
												_UpperCamelCase			     =     self.dummy_sample_deter
												_UpperCamelCase			     =     torch.manual_seed(0		)

												for i, t in enumerate(_A		):
																	# 1. predict noise residual
																	_UpperCamelCase			     =     model(_A ,			_A		)

																	if i + 1 == timesteps.shape[0]:
																						_UpperCamelCase			     =     None
																	else:
																						_UpperCamelCase			     =     timesteps[i + 1]

																	# 2. predict previous mean of sample x_t-1
																	_UpperCamelCase			     =     scheduler.step(
																	    _A ,			_A ,			_A ,			prev_timestep=_A ,			generator=_A		).prev_sample

																	_UpperCamelCase			     =     pred_prev_sample

												_UpperCamelCase			     =     torch.sum(torch.abs(_A		)		)
												_UpperCamelCase			     =     torch.mean(torch.abs(_A		)		)

												assert abs(result_sum.item() - 258.204_4983		) < 1e-2
												assert abs(result_mean.item() - 0.336_2038		) < 1e-3







							def  UpperCamelCase_						(   self      :     Any		):
												pass







							def  UpperCamelCase_						(   self      :     Dict		):
												pass







"""simple docstring"""



def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







    return int((input_a, input_a).count(0		) == 0		)







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


if __name__ == "__main__":
       test_and_gate()
       print(and_gate(1, 0))
       print(and_gate(0, 0))
       print(and_gate(0, 1))
       print(and_gate(1, 1))




