





"""simple docstring"""




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =(UniPCMultistepScheduler,)
							lowerCamelCase  :   Any    =(("""num_inference_steps""", 25),)






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Union[str, Any]:
								a :						Dict      =							{
								    "num_train_timesteps": 1000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**lowerCAmelCase__ )
								return config






							def        __a  (  self ,   lowerCAmelCase__=0 ,   **lowerCAmelCase__ )      ->					Dict:
								a :						Optional[int]      =							dict(self.forward_default_kwargs )
								a :						int      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )
								a :						List[str]      =							self.dummy_sample
								a :						int      =							0.1 * sample
								a :						Optional[Any]      =							[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a :						Any      =							self.get_scheduler_config(**lowerCAmelCase__ )
									a :						Tuple      =							scheduler_class(**lowerCAmelCase__ )
									scheduler.set_timesteps(lowerCAmelCase__ )
									# copy over dummy past residuals
									a :						Optional[int]      =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__ )
										a :						str      =							scheduler_class.from_pretrained(lowerCAmelCase__ )
										new_scheduler.set_timesteps(lowerCAmelCase__ )
										# copy over dummy past residuals
										a :						List[Any]      =							dummy_past_residuals[: new_scheduler.config.solver_order]

									a,			a :						List[str]      =							sample, sample
									for t in range(lowerCAmelCase__ ,   time_step + scheduler.config.solver_order + 1 ):
										a :						Optional[int]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
										a :						Tuple      =							new_scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self ,   lowerCAmelCase__=0 ,   **lowerCAmelCase__ )      ->					List[Any]:
								a :						List[Any]      =							dict(self.forward_default_kwargs )
								a :						Tuple      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )
								a :						Optional[int]      =							self.dummy_sample
								a :						List[Any]      =							0.1 * sample
								a :						List[Any]      =							[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a :						str      =							self.get_scheduler_config()
									a :						List[Any]      =							scheduler_class(**lowerCAmelCase__ )
									scheduler.set_timesteps(lowerCAmelCase__ )

									# copy over dummy past residuals (must be after setting timesteps)
									a :						Tuple      =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__ )
										a :						Tuple      =							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)
										a :						int      =							dummy_past_residuals[: new_scheduler.config.solver_order]

									a :						Dict      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						Optional[Any]      =							new_scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self ,   lowerCAmelCase__=None ,   **lowerCAmelCase__ )      ->					int:
								if scheduler is None:
									a :						List[str]      =							self.scheduler_classes[0]
									a :						Tuple      =							self.get_scheduler_config(**lowerCAmelCase__ )
									a :						List[Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						Optional[int]      =							self.scheduler_classes[0]
								a :						Union[str, Any]      =							self.get_scheduler_config(**lowerCAmelCase__ )
								a :						Optional[Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						Dict      =							10
								a :						Tuple      =							self.dummy_model()
								a :						str      =							self.dummy_sample_deter
								scheduler.set_timesteps(lowerCAmelCase__ )

								for i, t in enumerate(scheduler.timesteps ):
									a :						str      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Any      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

								return sample






							def        __a  (  self )      ->					Tuple:
								a :						Dict      =							dict(self.forward_default_kwargs )

								a :						int      =							kwargs.pop("num_inference_steps" ,   lowerCAmelCase__ )

								for scheduler_class in self.scheduler_classes:
									a :						Tuple      =							self.get_scheduler_config()
									a :						Optional[int]      =							scheduler_class(**lowerCAmelCase__ )

									a :						Union[str, Any]      =							self.dummy_sample
									a :						List[Any]      =							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" ):
										a :						Optional[int]      =							num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									a :						List[Any]      =							[residual + 0.2, residual + 0.15, residual + 0.10]
									a :						Optional[Any]      =							dummy_past_residuals[: scheduler.config.solver_order]

									a :						Union[str, Any]      =							scheduler.timesteps[5]
									a :						Dict      =							scheduler.timesteps[6]

									a :						Union[str, Any]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample
									a :						List[str]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self )      ->					List[Any]:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a :						Tuple      =							UniPCMultistepScheduler(**self.get_scheduler_config() )
								a :						Optional[Any]      =							self.full_loop(scheduler=lowerCAmelCase__ )
								a :						Tuple      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.2_464 ) < 1E-3

								a :						Optional[int]      =							DPMSolverSinglestepScheduler.from_config(scheduler.config )
								a :						Any      =							DEISMultistepScheduler.from_config(scheduler.config )
								a :						Any      =							DPMSolverMultistepScheduler.from_config(scheduler.config )
								a :						List[Any]      =							UniPCMultistepScheduler.from_config(scheduler.config )

								a :						Optional[int]      =							self.full_loop(scheduler=lowerCAmelCase__ )
								a :						Optional[Any]      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.2_464 ) < 1E-3






							def        __a  (  self )      ->					Any:
								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								self.check_over_configs(thresholding=lowerCAmelCase__ )
								for order in [1, 2, 3]:
									for solver_type in ["bh1", "bh2"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=lowerCAmelCase__ ,   prediction_type=lowerCAmelCase__ ,   sample_max_value=lowerCAmelCase__ ,   solver_order=lowerCAmelCase__ ,   solver_type=lowerCAmelCase__ ,   )






							def        __a  (  self )      ->					Tuple:
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								for solver_type in ["bh1", "bh2"]:
									for order in [1, 2, 3]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=lowerCAmelCase__ ,   solver_type=lowerCAmelCase__ ,   prediction_type=lowerCAmelCase__ ,   )
											a :						List[str]      =							self.full_loop(
											    solver_order=lowerCAmelCase__ ,   solver_type=lowerCAmelCase__ ,   prediction_type=lowerCAmelCase__ ,   )
											assert not torch.isnan(lowerCAmelCase__ ).any(), "Samples have nan numbers"






							def        __a  (  self )      ->					Optional[Any]:
								self.check_over_configs(lower_order_final=lowerCAmelCase__ )
								self.check_over_configs(lower_order_final=lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[Any]:
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=lowerCAmelCase__ ,   time_step=0 )






							def        __a  (  self )      ->					Any:
								a :						Any      =							self.full_loop()
								a :						Union[str, Any]      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.2_464 ) < 1E-3






							def        __a  (  self )      ->					List[str]:
								a :						Tuple      =							self.full_loop(prediction_type="v_prediction" )
								a :						Tuple      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

								assert abs(result_mean.item() - 0.1_014 ) < 1E-3






							def        __a  (  self )      ->					Optional[Any]:
								a :						str      =							self.scheduler_classes[0]
								a :						int      =							self.get_scheduler_config(thresholding=lowerCAmelCase__ ,   dynamic_thresholding_ratio=0 )
								a :						Union[str, Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						List[Any]      =							10
								a :						int      =							self.dummy_model()
								a :						List[Any]      =							self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCAmelCase__ )

								for i, t in enumerate(scheduler.timesteps ):
									a :						Optional[int]      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Any      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

								assert sample.dtype == torch.floataa






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Tuple:
								for scheduler_class in self.scheduler_classes:
									a :						Optional[int]      =							self.get_scheduler_config(**lowerCAmelCase__ )
									a :						Union[str, Any]      =							scheduler_class(**lowerCAmelCase__ )

									scheduler.set_timesteps(scheduler.config.num_train_timesteps )
									assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Optional[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	a :						List[str]      =							set({"(", "[", "{"}   )
	a :						int      =							set({")", "]", "}"}   )
	a :						int      =							{"{": "}", "[": "]", "(": ")"}

	for i in range(len(_lowercase   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(_lowercase   ) == 0 or (len(_lowercase   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(_lowercase   ) == 0






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						Any      =							input("Enter sequence of brackets: "   )
	if is_balanced(_lowercase   ):
		print(_lowercase       ,			"is balanced"   )
	else:
		print(_lowercase       ,			"is not balanced"   )


if __name__ == "__main__":
		main()


