



'''simple docstring'''






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	(KDPMaAncestralDiscreteScheduler,)
	lowerCamelCase__      			=	10







	def 					A     (						self      :					Dict		,      **__snake_case      :					Union[str, Any]		)      ->		Optional[Any]:
								UpperCAmelCase			:	str										=		{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**__snake_case		)
								return config







	def 					A     (						self      :					List[Any]		)      ->		str:
								for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case		)







	def 					A     (						self      :					Tuple		)      ->		Union[str, Any]:
								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]		,      [0.00_02, 0.0_02, 0.02]		):
															self.check_over_configs(beta_start=__snake_case		,      beta_end=__snake_case		)







	def 					A     (						self      :					int		)      ->		Dict:
								for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=__snake_case		)







	def 					A     (						self      :					List[Any]		)      ->		Any:
								if torch_device == "mps":
															return
								UpperCAmelCase			:	Any										=		self.scheduler_classes[0]
								UpperCAmelCase			:	List[str]										=		self.get_scheduler_config()
								UpperCAmelCase			:	Dict										=		scheduler_class(**__snake_case		)

								scheduler.set_timesteps(self.num_inference_steps		)

								UpperCAmelCase			:	Optional[Any]										=		torch.manual_seed(0		)

								UpperCAmelCase			:	List[Any]										=		self.dummy_model()
								UpperCAmelCase			:	List[str]										=		self.dummy_sample_deter * scheduler.init_noise_sigma
								UpperCAmelCase			:	int										=		sample.to(__snake_case		)

								for i, t in enumerate(scheduler.timesteps		):
															UpperCAmelCase			:	Optional[int]										=		scheduler.scale_model_input(__snake_case		,      __snake_case		)

															UpperCAmelCase			:	List[Any]										=		model(__snake_case		,      __snake_case		)

															UpperCAmelCase			:	List[Any]										=		scheduler.step(__snake_case		,      __snake_case		,      __snake_case		,      generator=__snake_case		)
															UpperCAmelCase			:	Dict										=		output.prev_sample

								UpperCAmelCase			:	Optional[int]										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	List[Any]										=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 13849.3877		) < 1E-2
								assert abs(result_mean.item() - 18.03_31		) < 5E-3







	def 					A     (						self      :					str		)      ->		Optional[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case		)







	def 					A     (						self      :					List[Any]		)      ->		List[Any]:
								if torch_device == "mps":
															return
								UpperCAmelCase			:	int										=		self.scheduler_classes[0]
								UpperCAmelCase			:	Any										=		self.get_scheduler_config(prediction_type='''v_prediction'''		)
								UpperCAmelCase			:	Optional[int]										=		scheduler_class(**__snake_case		)

								scheduler.set_timesteps(self.num_inference_steps		)

								UpperCAmelCase			:	List[str]										=		self.dummy_model()
								UpperCAmelCase			:	List[Any]										=		self.dummy_sample_deter * scheduler.init_noise_sigma
								UpperCAmelCase			:	Optional[Any]										=		sample.to(__snake_case		)

								UpperCAmelCase			:	int										=		torch.manual_seed(0		)

								for i, t in enumerate(scheduler.timesteps		):
															UpperCAmelCase			:	Tuple										=		scheduler.scale_model_input(__snake_case		,      __snake_case		)

															UpperCAmelCase			:	str										=		model(__snake_case		,      __snake_case		)

															UpperCAmelCase			:	int										=		scheduler.step(__snake_case		,      __snake_case		,      __snake_case		,      generator=__snake_case		)
															UpperCAmelCase			:	str										=		output.prev_sample

								UpperCAmelCase			:	str										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	Optional[int]										=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 328.9970		) < 1E-2
								assert abs(result_mean.item() - 0.42_84		) < 1E-3







	def 					A     (						self      :					List[Any]		)      ->		int:
								if torch_device == "mps":
															return
								UpperCAmelCase			:	Optional[Any]										=		self.scheduler_classes[0]
								UpperCAmelCase			:	List[str]										=		self.get_scheduler_config()
								UpperCAmelCase			:	Optional[Any]										=		scheduler_class(**__snake_case		)

								scheduler.set_timesteps(self.num_inference_steps		,      device=__snake_case		)
								UpperCAmelCase			:	Tuple										=		torch.manual_seed(0		)

								UpperCAmelCase			:	Optional[Any]										=		self.dummy_model()
								UpperCAmelCase			:	Union[str, Any]										=		self.dummy_sample_deter.to(__snake_case		) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
															UpperCAmelCase			:	List[Any]										=		scheduler.scale_model_input(__snake_case		,      __snake_case		)

															UpperCAmelCase			:	str										=		model(__snake_case		,      __snake_case		)

															UpperCAmelCase			:	Dict										=		scheduler.step(__snake_case		,      __snake_case		,      __snake_case		,      generator=__snake_case		)
															UpperCAmelCase			:	List[str]										=		output.prev_sample

								UpperCAmelCase			:	str										=		torch.sum(torch.abs(__snake_case		)		)
								UpperCAmelCase			:	Union[str, Any]										=		torch.mean(torch.abs(__snake_case		)		)

								assert abs(result_sum.item() - 13849.3818		) < 1E-1
								assert abs(result_mean.item() - 18.03_31		) < 1E-3










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__:       Tuple							     =      {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:       Optional[Any]							     =      ["SplinterTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:       Any							     =      [
						    "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SplinterForQuestionAnswering",
						    "SplinterForPreTraining",
						    "SplinterLayer",
						    "SplinterModel",
						    "SplinterPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
						from .tokenization_splinter import SplinterTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_splinter_fast import SplinterTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_splinter import (
												    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SplinterForPreTraining,
												    SplinterForQuestionAnswering,
												    SplinterLayer,
												    SplinterModel,
												    SplinterPreTrainedModel,
												)


else:
						import sys

						UpperCamelCase__:       Dict							     =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





