






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				_lowercase				:   List[Any]      =  (DEISMultistepScheduler,)
				_lowercase				:   str      =  (('''num_inference_steps''', 25),)



				def 					__magic_name__							(      self    :    Any			,		**UpperCamelCase__    :    str):






											'''simple docstring'''







											snake_case__	  =       {
											    """num_train_timesteps""": 1_0_0_0,
											    """beta_start""": 0.00_01,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											    """solver_order""": 2,
											}

											config.update(**UpperCamelCase__)
											return config



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    Tuple=0			,		**UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       dict(self.forward_default_kwargs)
											snake_case__	  =       kwargs.pop("""num_inference_steps"""			,		UpperCamelCase__)
											snake_case__	  =       self.dummy_sample
											snake_case__	  =       0.1 * sample
											snake_case__	  =       [residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		snake_case__	  =       self.get_scheduler_config(**UpperCamelCase__)
																		snake_case__	  =       scheduler_class(**UpperCamelCase__)
																		scheduler.set_timesteps(UpperCamelCase__)
																		# copy over dummy past residuals
																		snake_case__	  =       dummy_past_residuals[: scheduler.config.solver_order]

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

																		snake_case__  , snake_case__	  =       sample, sample
																		for t in range(UpperCamelCase__			,		time_step + scheduler.config.solver_order + 1):
																									snake_case__	  =       scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample
																									snake_case__	  =       new_scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample

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



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    int=0			,		**UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       dict(self.forward_default_kwargs)
											snake_case__	  =       kwargs.pop("""num_inference_steps"""			,		UpperCamelCase__)
											snake_case__	  =       self.dummy_sample
											snake_case__	  =       0.1 * sample
											snake_case__	  =       [residual + 0.2, residual + 0.15, residual + 0.10]

											for scheduler_class in self.scheduler_classes:
																		snake_case__	  =       self.get_scheduler_config()
																		snake_case__	  =       scheduler_class(**UpperCamelCase__)
																		scheduler.set_timesteps(UpperCamelCase__)

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

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

																									# copy over dummy past residual (must be after setting timesteps)
																									snake_case__	  =       dummy_past_residuals[: new_scheduler.config.solver_order]

																		snake_case__	  =       scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample
																		snake_case__	  =       new_scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample

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



				def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    List[str]=None			,		**UpperCamelCase__    :    List[Any]):






											'''simple docstring'''







											if scheduler is None:
																		snake_case__	  =       self.scheduler_classes[0]
																		snake_case__	  =       self.get_scheduler_config(**UpperCamelCase__)
																		snake_case__	  =       scheduler_class(**UpperCamelCase__)

											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config(**UpperCamelCase__)
											snake_case__	  =       scheduler_class(**UpperCamelCase__)

											snake_case__	  =       1_0
											snake_case__	  =       self.dummy_model()
											snake_case__	  =       self.dummy_sample_deter
											scheduler.set_timesteps(UpperCamelCase__)

											for i, t in enumerate(scheduler.timesteps):
																		snake_case__	  =       model(UpperCamelCase__			,		UpperCamelCase__)
																		snake_case__	  =       scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__).prev_sample

											return sample



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       dict(self.forward_default_kwargs)

											snake_case__	  =       kwargs.pop("""num_inference_steps"""			,		UpperCamelCase__)

											for scheduler_class in self.scheduler_classes:
																		snake_case__	  =       self.get_scheduler_config()
																		snake_case__	  =       scheduler_class(**UpperCamelCase__)

																		snake_case__	  =       self.dummy_sample
																		snake_case__	  =       0.1 * sample

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

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

																		snake_case__	  =       scheduler.timesteps[5]
																		snake_case__	  =       scheduler.timesteps[6]

																		snake_case__	  =       scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample
																		snake_case__	  =       scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample

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



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											snake_case__	  =       DEISMultistepScheduler(**self.get_scheduler_config())
											snake_case__	  =       self.full_loop(scheduler=UpperCamelCase__)
											snake_case__	  =       torch.mean(torch.abs(UpperCamelCase__))

											assert abs(result_mean.item() - 0.2_39_16) < 1E-3

											snake_case__	  =       DPMSolverSinglestepScheduler.from_config(scheduler.config)
											snake_case__	  =       DPMSolverMultistepScheduler.from_config(scheduler.config)
											snake_case__	  =       UniPCMultistepScheduler.from_config(scheduler.config)
											snake_case__	  =       DEISMultistepScheduler.from_config(scheduler.config)

											snake_case__	  =       self.full_loop(scheduler=UpperCamelCase__)
											snake_case__	  =       torch.mean(torch.abs(UpperCamelCase__))

											assert abs(result_mean.item() - 0.2_39_16) < 1E-3



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																		self.check_over_configs(num_train_timesteps=UpperCamelCase__)



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											self.check_over_configs(thresholding=UpperCamelCase__)
											for order in [1, 2, 3]:
																		for solver_type in ["logrho"]:
																									for threshold in [0.5, 1.0, 2.0]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    thresholding=UpperCamelCase__			,		prediction_type=UpperCamelCase__			,		sample_max_value=UpperCamelCase__			,		algorithm_type="""deis"""			,		solver_order=UpperCamelCase__			,		solver_type=UpperCamelCase__			,		)



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											for algorithm_type in ["deis"]:
																		for solver_type in ["logrho"]:
																									for order in [1, 2, 3]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    solver_order=UpperCamelCase__			,		solver_type=UpperCamelCase__			,		prediction_type=UpperCamelCase__			,		algorithm_type=UpperCamelCase__			,		)
																																							snake_case__	  =       self.full_loop(
																																							    solver_order=UpperCamelCase__			,		solver_type=UpperCamelCase__			,		prediction_type=UpperCamelCase__			,		algorithm_type=UpperCamelCase__			,		)
																																							assert not torch.isnan(UpperCamelCase__).any(), "Samples have nan numbers"



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											self.check_over_configs(lower_order_final=UpperCamelCase__)
											self.check_over_configs(lower_order_final=UpperCamelCase__)



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																		self.check_over_forward(num_inference_steps=UpperCamelCase__			,		time_step=0)



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       self.full_loop()
											snake_case__	  =       torch.mean(torch.abs(UpperCamelCase__))

											assert abs(result_mean.item() - 0.2_39_16) < 1E-3



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       self.full_loop(prediction_type="""v_prediction""")
											snake_case__	  =       torch.mean(torch.abs(UpperCamelCase__))

											assert abs(result_mean.item() - 0.0_91) < 1E-3



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       self.scheduler_classes[0]
											snake_case__	  =       self.get_scheduler_config(thresholding=UpperCamelCase__			,		dynamic_thresholding_ratio=0)
											snake_case__	  =       scheduler_class(**UpperCamelCase__)

											snake_case__	  =       1_0
											snake_case__	  =       self.dummy_model()
											snake_case__	  =       self.dummy_sample_deter.half()
											scheduler.set_timesteps(UpperCamelCase__)

											for i, t in enumerate(scheduler.timesteps):
																		snake_case__	  =       model(UpperCamelCase__			,		UpperCamelCase__)
																		snake_case__	  =       scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__).prev_sample

											assert sample.dtype == torch.floataa








from collections.abc import Callable

def _UpperCAmelCase   (	a						:	Callable[[float], float]  ,					a						:	float  ,					a						:	float ):
							snake_case__	  =       a
							snake_case__	  =       b
							if function(a ) == 0:  # one of the a or b is a root for the function
														return a
							elif function(a ) == 0:
														return b
							elif (
							    function(a ) * function(a ) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError("""could not find root in given interval.""" )
							else:
														snake_case__	  =       start + (end - start) / 2.0
														while abs(start - mid ) > 10**-7:  # until precisely equals to 10^-7
																					if function(a ) == 0:
																												return mid
																					elif function(a ) * function(a ) < 0:
																												snake_case__	  =       mid
																					else:
																												snake_case__	  =       mid
																					snake_case__	  =       start + (end - start) / 2.0
														return mid

def _UpperCAmelCase   (	a						:	float ):
							return x**3 - 2 * x - 5


if __name__ == "__main__":
	print(bisection(f, 1, 1_0_0_0))

	import doctest

	doctest.testmod()
