


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 			_lowercase (     A__			):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__							:     Optional[int]         =						(DDIMParallelScheduler,)
				SCREAMING_SNAKE_CASE__							:     Tuple         =						(('''eta''', 0.0), ('''num_inference_steps''', 50))


				def 						__magic_name__(			self     :Tuple     ,     **lowerCAmelCase__     :Tuple      )						->     Optional[Any]:
										__SCREAMING_SNAKE_CASE		:	Any						     =						{
										    '''num_train_timesteps''': 1_000,
										    '''beta_start''': 0.0001,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''clip_sample''': True,
										}

										config.update(**snake_case_      )
										return config


				def 						__magic_name__(			self     :Union[str, Any]     ,     **lowerCAmelCase__     :Optional[int]      )						->     Tuple:
										__SCREAMING_SNAKE_CASE		:	Any						     =						self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE		:	int						     =						self.get_scheduler_config(**snake_case_      )
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						scheduler_class(**snake_case_      )

										__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE		:	List[str]						     =						10, 0.0

										__SCREAMING_SNAKE_CASE		:	Dict						     =						self.dummy_model()
										__SCREAMING_SNAKE_CASE		:	Dict						     =						self.dummy_sample_deter

										scheduler.set_timesteps(snake_case_      )

										for t in scheduler.timesteps:
																__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						model(snake_case_     ,     snake_case_      )
																__SCREAMING_SNAKE_CASE		:	List[str]						     =						scheduler.step(snake_case_     ,     snake_case_     ,     snake_case_     ,     snake_case_      ).prev_sample

										return sample


				def 						__magic_name__(			self     :str      )						->     List[str]:
										for timesteps in [100, 500, 1_000]:
																self.check_over_configs(num_train_timesteps=snake_case_      )


				def 						__magic_name__(			self     :List[Any]      )						->     Tuple:
										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=snake_case_      )

										__SCREAMING_SNAKE_CASE		:	int						     =						self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						self.get_scheduler_config(steps_offset=1      )
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						scheduler_class(**snake_case_      )
										scheduler.set_timesteps(5      )
										assert torch.equal(scheduler.timesteps     ,     torch.LongTensor([801, 601, 401, 201, 1]      )      )


				def 						__magic_name__(			self     :List[str]      )						->     int:
										for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,     [0.002, 0.02, 0.2, 2]      ):
																self.check_over_configs(beta_start=snake_case_     ,     beta_end=snake_case_      )


				def 						__magic_name__(			self     :int      )						->     str:
										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=snake_case_      )


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


				def 						__magic_name__(			self     :List[str]      )						->     Optional[int]:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=snake_case_      )


				def 						__magic_name__(			self     :Dict      )						->     Dict:
										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=snake_case_      )


				def 						__magic_name__(			self     :Dict      )						->     str:
										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=snake_case_      )


				def 						__magic_name__(			self     :List[str]      )						->     Optional[int]:
										self.check_over_configs(thresholding=snake_case_      )
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=snake_case_     ,     prediction_type=snake_case_     ,     sample_max_value=snake_case_     ,     )


				def 						__magic_name__(			self     :Union[str, Any]      )						->     Union[str, Any]:
										for t in [1, 10, 49]:
																self.check_over_forward(time_step=snake_case_      )


				def 						__magic_name__(			self     :Union[str, Any]      )						->     Optional[int]:
										for t, num_inference_steps in zip([1, 10, 50]     ,     [10, 50, 500]      ):
																self.check_over_forward(time_step=snake_case_     ,     num_inference_steps=snake_case_      )


				def 						__magic_name__(			self     :List[Any]      )						->     Optional[int]:
										for t, eta in zip([1, 10, 49]     ,     [0.0, 0.5, 1.0]      ):
																self.check_over_forward(time_step=snake_case_     ,     eta=snake_case_      )


				def 						__magic_name__(			self     :Optional[int]      )						->     Optional[int]:
										__SCREAMING_SNAKE_CASE		:	Dict						     =						self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						self.get_scheduler_config()
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						scheduler_class(**snake_case_      )

										assert torch.sum(torch.abs(scheduler._get_variance(0     ,     0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420     ,     400      ) - 0.1_4771      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980     ,     960      ) - 0.3_2460      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0     ,     0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487     ,     486      ) - 0.0_0979      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999     ,     998      ) - 0.02      )      ) < 1E-5


				def 						__magic_name__(			self     :Optional[Any]      )						->     Optional[int]:
										__SCREAMING_SNAKE_CASE		:	str						     =						self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						self.get_scheduler_config()
										__SCREAMING_SNAKE_CASE		:	str						     =						scheduler_class(**snake_case_      )

										__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE		:	List[str]						     =						10, 0.0
										scheduler.set_timesteps(snake_case_      )

										__SCREAMING_SNAKE_CASE		:	Dict						     =						self.dummy_model()
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						self.dummy_sample_deter
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						self.dummy_sample_deter + 0.1
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						self.dummy_sample_deter - 0.1

										__SCREAMING_SNAKE_CASE		:	List[str]						     =						samplea.shape[0]
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.stack([samplea, samplea, samplea]     ,     dim=0      )
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						torch.arange(snake_case_      )[0:3, None].repeat(1     ,     snake_case_      )

										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						model(samples.flatten(0     ,     1      )     ,     timesteps.flatten(0     ,     1      )      )
										__SCREAMING_SNAKE_CASE		:	str						     =						scheduler.batch_step_no_noise(snake_case_     ,     timesteps.flatten(0     ,     1      )     ,     samples.flatten(0     ,     1      )     ,     snake_case_      )

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.sum(torch.abs(snake_case_      )      )
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						torch.mean(torch.abs(snake_case_      )      )

										assert abs(result_sum.item() - 1_147.7_904      ) < 1E-2
										assert abs(result_mean.item() - 0.4982      ) < 1E-3


				def 						__magic_name__(			self     :Tuple      )						->     Dict:
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						self.full_loop()

										__SCREAMING_SNAKE_CASE		:	str						     =						torch.sum(torch.abs(snake_case_      )      )
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.mean(torch.abs(snake_case_      )      )

										assert abs(result_sum.item() - 172.0067      ) < 1E-2
										assert abs(result_mean.item() - 0.22_3967      ) < 1E-3


				def 						__magic_name__(			self     :Any      )						->     str:
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						self.full_loop(prediction_type='''v_prediction'''      )

										__SCREAMING_SNAKE_CASE		:	Tuple						     =						torch.sum(torch.abs(snake_case_      )      )
										__SCREAMING_SNAKE_CASE		:	Dict						     =						torch.mean(torch.abs(snake_case_      )      )

										assert abs(result_sum.item() - 52.5302      ) < 1E-2
										assert abs(result_mean.item() - 0.0684      ) < 1E-3


				def 						__magic_name__(			self     :Union[str, Any]      )						->     Tuple:
										# We specify different beta, so that the first alpha is 0.99
										__SCREAMING_SNAKE_CASE		:	Dict						     =						self.full_loop(set_alpha_to_one=snake_case_     ,     beta_start=0.01      )
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.sum(torch.abs(snake_case_      )      )
										__SCREAMING_SNAKE_CASE		:	str						     =						torch.mean(torch.abs(snake_case_      )      )

										assert abs(result_sum.item() - 149.8295      ) < 1E-2
										assert abs(result_mean.item() - 0.1951      ) < 1E-3



				def 						__magic_name__(			self     :Optional[int]      )						->     Union[str, Any]:
										# We specify different beta, so that the first alpha is 0.99
										__SCREAMING_SNAKE_CASE		:	List[str]						     =						self.full_loop(set_alpha_to_one=snake_case_     ,     beta_start=0.01      )
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.sum(torch.abs(snake_case_      )      )
										__SCREAMING_SNAKE_CASE		:	int						     =						torch.mean(torch.abs(snake_case_      )      )

										assert abs(result_sum.item() - 149.0784      ) < 1E-2
										assert abs(result_mean.item() - 0.1941      ) < 1E-3


"""simple docstring"""
import warnings

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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





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