


"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Dict					      =    (DPMSolverSinglestepScheduler,)
	snake_case__       :						Tuple					      =    (("num_inference_steps", 25),)




	def 						UpperCAmelCase_   (				self    :       str    ,    **UpperCAmelCase__    :       List[str]     ) ->			Union[str, Any]:
						__SCREAMING_SNAKE_CASE		     =       {
						    "num_train_timesteps": 1_0_0_0,
						    "beta_start": 0.0_001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						    "sample_max_value": 1.0,
						    "algorithm_type": "dpmsolver++",
						    "solver_type": "midpoint",
						    "lambda_min_clipped": -float("inf"     ),
						    "variance_type": None,
						}

						config.update(**UpperCAmelCase__     )
						return config




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

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

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

											__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       sample, sample
											for t in range(UpperCAmelCase__    ,    time_step + scheduler.config.solver_order + 1     ):
																__SCREAMING_SNAKE_CASE		     =       scheduler.step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    **UpperCAmelCase__     ).prev_sample
																__SCREAMING_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 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			str:
						pass




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       str=0    ,    **UpperCAmelCase__    :       Union[str, Any]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       dict(self.forward_default_kwargs     )
						__SCREAMING_SNAKE_CASE		     =       kwargs.pop("num_inference_steps"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample
						__SCREAMING_SNAKE_CASE		     =       0.1 * sample
						__SCREAMING_SNAKE_CASE		     =       [residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
											__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
											__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )
											scheduler.set_timesteps(UpperCAmelCase__     )

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

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(UpperCAmelCase__     )
																__SCREAMING_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)
																__SCREAMING_SNAKE_CASE		     =       dummy_past_residuals[: new_scheduler.config.solver_order]

											__SCREAMING_SNAKE_CASE		     =       scheduler.step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    **UpperCAmelCase__     ).prev_sample
											__SCREAMING_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 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Any=None    ,    **UpperCAmelCase__    :       Dict     ) ->			Union[str, Any]:
						if scheduler is None:
											__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
											__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config(**UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       1_0
						__SCREAMING_SNAKE_CASE		     =       self.dummy_model()
						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample_deter
						scheduler.set_timesteps(UpperCAmelCase__     )

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

						return sample




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
						__SCREAMING_SNAKE_CASE		     =       5_0
						__SCREAMING_SNAKE_CASE		     =       self.dummy_model()
						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample_deter
						scheduler.set_timesteps(UpperCAmelCase__     )

						# make sure that the first t is uneven
						for i, t in enumerate(scheduler.timesteps[3:]     ):
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       scheduler.step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ).prev_sample

						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.2_574     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Any:
						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 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Union[str, Any]:
						# make sure that iterating over schedulers with same config names gives same results
						# for defaults
						__SCREAMING_SNAKE_CASE		     =       DPMSolverSinglestepScheduler(**self.get_scheduler_config()     )
						__SCREAMING_SNAKE_CASE		     =       self.full_loop(scheduler=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.2_791     ) < 1E-3

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

						__SCREAMING_SNAKE_CASE		     =       self.full_loop(scheduler=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.2_791     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			Optional[int]:
						self.check_over_configs(thresholding=UpperCAmelCase__     )
						for order in [1, 2, 3]:
											for solver_type in ["midpoint", "heun"]:
																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="dpmsolver++"    ,    solver_order=UpperCAmelCase__    ,    solver_type=UpperCAmelCase__    ,    )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			List[Any]:
						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Tuple:
						for algorithm_type in ["dpmsolver", "dpmsolver++"]:
											for solver_type in ["midpoint", "heun"]:
																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__    ,    )
																										__SCREAMING_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 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[Any]:
						self.check_over_configs(lower_order_final=UpperCAmelCase__     )
						self.check_over_configs(lower_order_final=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Optional[int]:
						self.check_over_configs(lambda_min_clipped=-float("inf"     )     )
						self.check_over_configs(lambda_min_clipped=-5.1     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:
						self.check_over_configs(variance_type=UpperCAmelCase__     )
						self.check_over_configs(variance_type="learned_range"     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			List[Any]:
						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 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.full_loop()
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.2_791     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       self.full_loop(use_karras_sigmas=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.2_248     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.full_loop(prediction_type="v_prediction"     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.1_453     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       self.full_loop(prediction_type="v_prediction"    ,    use_karras_sigmas=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       torch.mean(torch.abs(UpperCAmelCase__     )     )

						assert abs(result_mean.item() - 0.0_649     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config(thresholding=UpperCAmelCase__    ,    dynamic_thresholding_ratio=0     )
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       1_0
						__SCREAMING_SNAKE_CASE		     =       self.dummy_model()
						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample_deter.half()
						scheduler.set_timesteps(UpperCAmelCase__     )

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

						assert sample.dtype == torch.floataa




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


a__       :							List[str]        =  {}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  ['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		a__       :							Optional[Any]        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
