



"""simple docstring"""





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       (PNDMScheduler,)
		_lowerCamelCase			  =       (('''num_inference_steps''', 50),)





		def    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Tuple						=			{
								    """num_train_timesteps""": 1_0_0_0,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_lowercase   )
								return config





		def    UpperCAmelCase__							(					self							,    _lowercase=0							,    **_lowercase   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			dict(self.forward_default_kwargs   )
								snake_case_   : Any						=			kwargs.pop("""num_inference_steps"""							,    _lowercase   )
								snake_case_   : int						=			self.dummy_sample
								snake_case_   : Optional[Any]						=			0.1 * sample
								snake_case_   : Union[str, Any]						=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

														snake_case_   : int						=			scheduler.step_prk(_lowercase							,    _lowercase							,    _lowercase							,    **_lowercase   ).prev_sample
														snake_case_   : 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"

														snake_case_   : str						=			scheduler.step_plms(_lowercase							,    _lowercase							,    _lowercase							,    **_lowercase   ).prev_sample
														snake_case_   : Union[str, 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    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self							,    _lowercase=0							,    **_lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Dict						=			dict(self.forward_default_kwargs   )
								snake_case_   : Dict						=			kwargs.pop("""num_inference_steps"""							,    _lowercase   )
								snake_case_   : List[str]						=			self.dummy_sample
								snake_case_   : Union[str, Any]						=			0.1 * sample
								snake_case_   : Any						=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
														snake_case_   : str						=			self.get_scheduler_config()
														snake_case_   : Optional[Any]						=			scheduler_class(**_lowercase   )
														scheduler.set_timesteps(_lowercase   )

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(_lowercase   )
																				snake_case_   : Union[str, 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)
																				snake_case_   : Optional[Any]						=			dummy_past_residuals[:]

														snake_case_   : List[str]						=			scheduler.step_prk(_lowercase							,    _lowercase							,    _lowercase							,    **_lowercase   ).prev_sample
														snake_case_   : Tuple						=			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"

														snake_case_   : Optional[int]						=			scheduler.step_plms(_lowercase							,    _lowercase							,    _lowercase							,    **_lowercase   ).prev_sample
														snake_case_   : Optional[int]						=			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    UpperCAmelCase__							(					self							,    **_lowercase   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			self.scheduler_classes[0]
								snake_case_   : int						=			self.get_scheduler_config(**_lowercase   )
								snake_case_   : Optional[Any]						=			scheduler_class(**_lowercase   )

								snake_case_   : Tuple						=			1_0
								snake_case_   : Optional[int]						=			self.dummy_model()
								snake_case_   : Optional[Any]						=			self.dummy_sample_deter
								scheduler.set_timesteps(_lowercase   )

								for i, t in enumerate(scheduler.prk_timesteps   ):
														snake_case_   : str						=			model(_lowercase							,    _lowercase   )
														snake_case_   : Optional[Any]						=			scheduler.step_prk(_lowercase							,    _lowercase							,    _lowercase   ).prev_sample

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

								return sample





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







								'''simple docstring'''

								snake_case_   : Any						=			dict(self.forward_default_kwargs   )

								snake_case_   : Tuple						=			kwargs.pop("""num_inference_steps"""							,    _lowercase   )

								for scheduler_class in self.scheduler_classes:
														snake_case_   : Union[str, Any]						=			self.get_scheduler_config()
														snake_case_   : int						=			scheduler_class(**_lowercase   )

														snake_case_   : Dict						=			self.dummy_sample
														snake_case_   : Any						=			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"""   ):
																				snake_case_   : List[Any]						=			num_inference_steps

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

														snake_case_   : Dict						=			scheduler.step_prk(_lowercase							,    0							,    _lowercase							,    **_lowercase   ).prev_sample
														snake_case_   : List[str]						=			scheduler.step_prk(_lowercase							,    1							,    _lowercase							,    **_lowercase   ).prev_sample

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

														snake_case_   : str						=			scheduler.step_plms(_lowercase							,    0							,    _lowercase							,    **_lowercase   ).prev_sample
														snake_case_   : str						=			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    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								for timesteps in [1_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

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

								snake_case_   : Optional[int]						=			self.scheduler_classes[0]
								snake_case_   : Tuple						=			self.get_scheduler_config(steps_offset=1   )
								snake_case_   : Any						=			scheduler_class(**_lowercase   )
								scheduler.set_timesteps(1_0   )
								assert torch.equal(
								    scheduler.timesteps							,    torch.LongTensor(
								        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]   )							,    )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								for t in [1, 5, 1_0]:
														self.check_over_forward(time_step=_lowercase   )





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







								'''simple docstring'''

								for t, num_inference_steps in zip([1, 5, 1_0]							,    [1_0, 5_0, 1_0_0]   ):
														self.check_over_forward(num_inference_steps=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			2_7

								for scheduler_class in self.scheduler_classes:
														snake_case_   : Dict						=			self.dummy_sample
														snake_case_   : Optional[Any]						=			0.1 * sample

														snake_case_   : Tuple						=			self.get_scheduler_config()
														snake_case_   : Optional[Any]						=			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]   ):
																				snake_case_   : Union[str, Any]						=			scheduler.step_prk(_lowercase							,    _lowercase							,    _lowercase   ).prev_sample





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								with self.assertRaises(_lowercase   ):
														snake_case_   : str						=			self.scheduler_classes[0]
														snake_case_   : List[Any]						=			self.get_scheduler_config()
														snake_case_   : Dict						=			scheduler_class(**_lowercase   )

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





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			self.full_loop()
								snake_case_   : Optional[int]						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : str						=			torch.mean(torch.abs(_lowercase   )   )

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





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Any						=			self.full_loop(prediction_type="""v_prediction"""   )
								snake_case_   : Tuple						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : Any						=			torch.mean(torch.abs(_lowercase   )   )

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





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.full_loop(set_alpha_to_one=_lowercase							,    beta_start=0.01   )
								snake_case_   : Any						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : Optional[Any]						=			torch.mean(torch.abs(_lowercase   )   )

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





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			self.full_loop(set_alpha_to_one=_lowercase							,    beta_start=0.01   )
								snake_case_   : Dict						=			torch.sum(torch.abs(_lowercase   )   )
								snake_case_   : int						=			torch.mean(torch.abs(_lowercase   )   )

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






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						return " ".join(input_str.split()[::-1]    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

