import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class        lowercase_   (					_lowerCAmelCase		):
							_lowerCamelCase						=		(PNDMScheduler,)
							_lowerCamelCase						=		(('num_inference_steps', 50),)



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													_snake_case						:					Optional[int]    =   {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													}

													config.update(**SCREAMING_SNAKE_CASE_    )
													return config



							def 	UpperCamelCase	(						self				,    lowercase_=0				,    **lowercase_    ):
													_snake_case						:					int    =   dict(self.forward_default_kwargs    )
													_snake_case						:					Union[str, Any]    =   kwargs.pop("num_inference_steps"				,    SCREAMING_SNAKE_CASE_    )
													_snake_case						:					Optional[Any]    =   self.dummy_sample
													_snake_case						:					Any    =   0.1 * sample
													_snake_case						:					List[Any]    =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(SCREAMING_SNAKE_CASE_    )
																									_snake_case						:					Union[str, Any]    =   scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_    )
																									new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )
																									# copy over dummy past residuals
																									_snake_case						:					List[str]    =   dummy_past_residuals[:]

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

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

																			_snake_case						:					int    =   scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample
																			_snake_case						:					Union[str, Any]    =   new_scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample

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



							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self				,    lowercase_=0				,    **lowercase_    ):
													_snake_case						:					List[Any]    =   dict(self.forward_default_kwargs    )
													_snake_case						:					Dict    =   kwargs.pop("num_inference_steps"				,    SCREAMING_SNAKE_CASE_    )
													_snake_case						:					Tuple    =   self.dummy_sample
													_snake_case						:					List[str]    =   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						:					Tuple    =   self.get_scheduler_config()
																			_snake_case						:					str    =   scheduler_class(**SCREAMING_SNAKE_CASE_    )
																			scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

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

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

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

																			_snake_case						:					Any    =   scheduler.step_prk(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample
																			_snake_case						:					List[str]    =   new_scheduler.step_prk(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample

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

																			_snake_case						:					Tuple    =   scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample
																			_snake_case						:					int    =   new_scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample

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



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													_snake_case						:					Union[str, Any]    =   self.scheduler_classes[0]
													_snake_case						:					int    =   self.get_scheduler_config(**SCREAMING_SNAKE_CASE_    )
													_snake_case						:					Optional[int]    =   scheduler_class(**SCREAMING_SNAKE_CASE_    )

													_snake_case						:					int    =   10
													_snake_case						:					Union[str, Any]    =   self.dummy_model()
													_snake_case						:					Optional[Any]    =   self.dummy_sample_deter
													scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

													for i, t in enumerate(scheduler.prk_timesteps    ):
																			_snake_case						:					Optional[Any]    =   model(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_    )
																			_snake_case						:					Tuple    =   scheduler.step_prk(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_    ).prev_sample

													for i, t in enumerate(scheduler.plms_timesteps    ):
																			_snake_case						:					Optional[int]    =   model(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_    )
																			_snake_case						:					List[Any]    =   scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_    ).prev_sample

													return sample



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   dict(self.forward_default_kwargs    )

													_snake_case						:					Tuple    =   kwargs.pop("num_inference_steps"				,    SCREAMING_SNAKE_CASE_    )

													for scheduler_class in self.scheduler_classes:
																			_snake_case						:					int    =   self.get_scheduler_config()
																			_snake_case						:					List[Any]    =   scheduler_class(**SCREAMING_SNAKE_CASE_    )

																			_snake_case						:					Dict    =   self.dummy_sample
																			_snake_case						:					List[str]    =   0.1 * sample

																			if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_				,    "set_timesteps"    ):
																									scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )
																			elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_				,    "set_timesteps"    ):
																									_snake_case						:					Optional[int]    =   num_inference_steps

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

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

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

																			_snake_case						:					Optional[int]    =   scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    0				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample
																			_snake_case						:					str    =   scheduler.step_plms(SCREAMING_SNAKE_CASE_				,    1				,    SCREAMING_SNAKE_CASE_				,    **SCREAMING_SNAKE_CASE_    ).prev_sample

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



							def 	UpperCamelCase	(						self    ):
													for timesteps in [100, 1_000]:
																			self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_    )



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

													_snake_case						:					Union[str, Any]    =   self.scheduler_classes[0]
													_snake_case						:					Optional[int]    =   self.get_scheduler_config(steps_offset=1    )
													_snake_case						:					Tuple    =   scheduler_class(**SCREAMING_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 	UpperCamelCase	(						self    ):
													for beta_start, beta_end in zip([0.0_001, 0.001]				,    [0.002, 0.02]    ):
																			self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_				,    beta_end=SCREAMING_SNAKE_CASE_    )



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



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



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



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



							def 	UpperCamelCase	(						self    ):
													# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
													_snake_case						:					Optional[Any]    =   27

													for scheduler_class in self.scheduler_classes:
																			_snake_case						:					Dict    =   self.dummy_sample
																			_snake_case						:					Union[str, Any]    =   0.1 * sample

																			_snake_case						:					Optional[int]    =   self.get_scheduler_config()
																			_snake_case						:					Dict    =   scheduler_class(**SCREAMING_SNAKE_CASE_    )

																			scheduler.set_timesteps(SCREAMING_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]    ):
																									_snake_case						:					Optional[int]    =   scheduler.step_prk(SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_    ).prev_sample



							def 	UpperCamelCase	(						self    ):
													with self.assertRaises(SCREAMING_SNAKE_CASE_    ):
																			_snake_case						:					Optional[Any]    =   self.scheduler_classes[0]
																			_snake_case						:					int    =   self.get_scheduler_config()
																			_snake_case						:					Dict    =   scheduler_class(**SCREAMING_SNAKE_CASE_    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.full_loop()
													_snake_case						:					Union[str, Any]    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
													_snake_case						:					Tuple    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

													assert abs(result_sum.item() - 198.1_318    ) < 1e-2
													assert abs(result_mean.item() - 0.2_580    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.full_loop(prediction_type="v_prediction"    )
													_snake_case						:					List[str]    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
													_snake_case						:					List[Any]    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

													assert abs(result_sum.item() - 67.3_986    ) < 1e-2
													assert abs(result_mean.item() - 0.0_878    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													# We specify different beta, so that the first alpha is 0.99
													_snake_case						:					Tuple    =   self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_				,    beta_start=0.01    )
													_snake_case						:					Tuple    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
													_snake_case						:					List[str]    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

													assert abs(result_sum.item() - 230.0_399    ) < 1e-2
													assert abs(result_mean.item() - 0.2_995    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													# We specify different beta, so that the first alpha is 0.99
													_snake_case						:					Optional[Any]    =   self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_				,    beta_start=0.01    )
													_snake_case						:					Any    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_    )    )
													_snake_case						:					Optional[Any]    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_    )    )

													assert abs(result_sum.item() - 186.9_482    ) < 1e-2
													assert abs(result_mean.item() - 0.2_434    ) < 1e-3




def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						_snake_case						:					Dict    =   0
						for ch in input_str:
												_snake_case						:					int    =   ord(__lowercase						)
												_snake_case						:					List[Any]    =   pow(2 ,      __lowercase						)

												# If we already turned on bit for current character's unicode
												if bitmap >> ch_unicode & 1 == 1:
																		return False
												bitmap |= ch_bit_index_on
						return True


if __name__ == "__main__":
				import doctest

				doctest.testmod()