



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				(PNDMScheduler,)
				__lowerCamelCase =				(('''num_inference_steps''', 50),)




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    {
									    """num_train_timesteps""": 1000,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**_snake_case		)
									return config




				def 							snake_case   (							self						,				_snake_case=0						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)
									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)
									_lowerCAmelCase						=    self.dummy_sample
									_lowerCAmelCase						=    0.1 * sample
									_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				def 							snake_case   (							self						,				_snake_case=0						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)
									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)
									_lowerCAmelCase						=    self.dummy_sample
									_lowerCAmelCase						=    0.1 * sample
									_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)
														scheduler.set_timesteps(_snake_case		)

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

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

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

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.scheduler_classes[0]
									_lowerCAmelCase						=    self.get_scheduler_config(**_snake_case		)
									_lowerCAmelCase						=    scheduler_class(**_snake_case		)

									_lowerCAmelCase						=    10
									_lowerCAmelCase						=    self.dummy_model()
									_lowerCAmelCase						=    self.dummy_sample_deter
									scheduler.set_timesteps(_snake_case		)

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

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

									return sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)

									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														_lowerCAmelCase						=    self.dummy_sample
														_lowerCAmelCase						=    0.1 * sample

														if num_inference_steps is not None and hasattr(_snake_case						,				"""set_timesteps"""		):
																			scheduler.set_timesteps(_snake_case		)
														elif num_inference_steps is not None and not hasattr(_snake_case						,				"""set_timesteps"""		):
																			_lowerCAmelCase						=    num_inference_steps

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

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				0						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				1						,				_snake_case						,				**_snake_case		).prev_sample

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

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				0						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				1						,				_snake_case						,				**_snake_case		).prev_sample

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=_snake_case		)

									_lowerCAmelCase						=    self.scheduler_classes[0]
									_lowerCAmelCase						=    self.get_scheduler_config(steps_offset=1		)
									_lowerCAmelCase						=    scheduler_class(**_snake_case		)
									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 							snake_case   (							self		):



									"""simple docstring"""





									for beta_start, beta_end in zip([0.0001, 0.001]						,				[0.002, 0.02]		):
														self.check_over_configs(beta_start=_snake_case						,				beta_end=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





									for t in [1, 5, 10]:
														self.check_over_forward(time_step=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for t, num_inference_steps in zip([1, 5, 10]						,				[10, 50, 100]		):
														self.check_over_forward(num_inference_steps=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    27

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

														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														scheduler.set_timesteps(_snake_case		)

														# 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]		):
																			_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case		).prev_sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									with self.assertRaises(_snake_case		):
														_lowerCAmelCase						=    self.scheduler_classes[0]
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop()
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(prediction_type="""v_prediction"""		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(set_alpha_to_one=_snake_case						,				beta_start=0.01		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(set_alpha_to_one=_snake_case						,				beta_start=0.01		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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










import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],[0.002, 0.02, 0.2, 2] ):
               self.check_over_configs(beta_start=__lowerCamelCase,beta_end=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



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



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0] ):
               self.check_over_forward(time_step=__lowerCamelCase,eta=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        A__											=						self.dummy_sample_deter + 0.1
        A__											=						self.dummy_sample_deter - 0.1

        A__											=						samplea.shape[0]
        A__											=						torch.stack([samplea, samplea, samplea],dim=0 )
        A__											=						torch.arange(__lowerCamelCase )[0:3, None].repeat(1,__lowerCamelCase )

        A__											=						model(samples.flatten(0,1 ),timesteps.flatten(0,1 ) )
        A__											=						scheduler.batch_step_no_noise(__lowerCamelCase,timesteps.flatten(0,1 ),samples.flatten(0,1 ),__lowerCamelCase )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





