import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 						a__							(       __snake_case		):
			A__    :       List[Any]        =      (DEISMultistepScheduler,)
			A__    :       List[str]        =      (('num_inference_steps', 25),)



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   List[str]:
								__a		 =       {
								    'num_train_timesteps': 1_0_0_0,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'solver_order': 2,
								}

								config.update(**UpperCAmelCase   )
								return config



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase=0				,						**UpperCAmelCase   )			->   Tuple:
								__a		 =       dict(self.forward_default_kwargs   )
								__a		 =       kwargs.pop('num_inference_steps'				,						UpperCAmelCase   )
								__a		 =       self.dummy_sample
								__a		 =       0.1 * sample
								__a		 =       [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase=0				,						**UpperCAmelCase   )			->   List[Any]:
								__a		 =       dict(self.forward_default_kwargs   )
								__a		 =       kwargs.pop('num_inference_steps'				,						UpperCAmelCase   )
								__a		 =       self.dummy_sample
								__a		 =       0.1 * sample
								__a		 =       [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
													__a		 =       self.get_scheduler_config()
													__a		 =       scheduler_class(**UpperCAmelCase   )
													scheduler.set_timesteps(UpperCAmelCase   )

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(UpperCAmelCase   )
																		__a		 =       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)
																		__a		 =       dummy_past_residuals[: new_scheduler.config.solver_order]

													__a		 =       scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   ).prev_sample
													__a		 =       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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Optional[Any]:
								if scheduler is None:
													__a		 =       self.scheduler_classes[0]
													__a		 =       self.get_scheduler_config(**UpperCAmelCase   )
													__a		 =       scheduler_class(**UpperCAmelCase   )

								__a		 =       self.scheduler_classes[0]
								__a		 =       self.get_scheduler_config(**UpperCAmelCase   )
								__a		 =       scheduler_class(**UpperCAmelCase   )

								__a		 =       1_0
								__a		 =       self.dummy_model()
								__a		 =       self.dummy_sample_deter
								scheduler.set_timesteps(UpperCAmelCase   )

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

								return sample



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       dict(self.forward_default_kwargs   )

								__a		 =       kwargs.pop('num_inference_steps'				,						UpperCAmelCase   )

								for scheduler_class in self.scheduler_classes:
													__a		 =       self.get_scheduler_config()
													__a		 =       scheduler_class(**UpperCAmelCase   )

													__a		 =       self.dummy_sample
													__a		 =       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'   ):
																		__a		 =       num_inference_steps

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

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

													__a		 =       scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   ).prev_sample
													__a		 =       scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   ).prev_sample

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								__a		 =       DEISMultistepScheduler(**self.get_scheduler_config()   )
								__a		 =       self.full_loop(scheduler=UpperCAmelCase   )
								__a		 =       torch.mean(torch.abs(UpperCAmelCase   )   )

								assert abs(result_mean.item() - 0.23_916   ) < 1e-3

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

								__a		 =       self.full_loop(scheduler=UpperCAmelCase   )
								__a		 =       torch.mean(torch.abs(UpperCAmelCase   )   )

								assert abs(result_mean.item() - 0.23_916   ) < 1e-3



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								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 __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								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 __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								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				,						)
																												__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								self.check_over_configs(lower_order_final=UpperCAmelCase   )
								self.check_over_configs(lower_order_final=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								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 __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       self.full_loop()
								__a		 =       torch.mean(torch.abs(UpperCAmelCase   )   )

								assert abs(result_mean.item() - 0.23_916   ) < 1e-3



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.full_loop(prediction_type='v_prediction'   )
								__a		 =       torch.mean(torch.abs(UpperCAmelCase   )   )

								assert abs(result_mean.item() - 0.091   ) < 1e-3







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

								__a		 =       1_0
								__a		 =       self.dummy_model()
								__a		 =       self.dummy_sample_deter.half()
								scheduler.set_timesteps(UpperCAmelCase   )

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

								assert sample.dtype == torch.floataa





import warnings

from ..trainer import Trainer
from ..utils import logging


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Any:
								warnings.warn(
								    '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
								    'instead.'				,						UpperCAmelCase				,						)
								super().__init__(args=UpperCAmelCase				,						**UpperCAmelCase   )




