import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 			__snake_case  (      _lowerCamelCase     ):
					__lowerCamelCase	 =       (KDPMaAncestralDiscreteScheduler,)
					__lowerCamelCase	 =       10




					def       __a      (       self   ,	**__UpperCamelCase		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				{
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_0_0_1,
									    'beta_end': 0.0_2,
									    'beta_schedule': 'linear',
									}

									config.update(**__UpperCamelCase		)
									return config




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=__UpperCamelCase		)




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]   ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]		):
													self.check_over_configs(beta_start=__UpperCamelCase   ,	beta_end=__UpperCamelCase		)




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__UpperCamelCase		)




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									if torch_device == "mps":
													return
									snake_case__ :     Tuple 							=				self.scheduler_classes[0]
									snake_case__ :     Dict 							=				self.get_scheduler_config()
									snake_case__ :     Optional[int] 							=				scheduler_class(**__UpperCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps		)

									snake_case__ :     List[str] 							=				torch.manual_seed(0		)

									snake_case__ :     Optional[int] 							=				self.dummy_model()
									snake_case__ :     Any 							=				self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__ :     Any 							=				sample.to(__UpperCamelCase		)

									for i, t in enumerate(scheduler.timesteps		):
													snake_case__ :     int 							=				scheduler.scale_model_input(__UpperCamelCase   ,	__UpperCamelCase		)

													snake_case__ :     Union[str, Any] 							=				model(__UpperCamelCase   ,	__UpperCamelCase		)

													snake_case__ :     Optional[Any] 							=				scheduler.step(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	generator=__UpperCamelCase		)
													snake_case__ :     str 							=				output.prev_sample

									snake_case__ :     List[str] 							=				torch.sum(torch.abs(__UpperCamelCase		)		)
									snake_case__ :     Union[str, Any] 							=				torch.mean(torch.abs(__UpperCamelCase		)		)

									assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7		) < 1E-2
									assert abs(result_mean.item() - 1_8.0_3_3_1		) < 5E-3




					def       __a      (       self		)					->  str:



									'''simple docstring'''







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




					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									if torch_device == "mps":
													return
									snake_case__ :     Optional[Any] 							=				self.scheduler_classes[0]
									snake_case__ :     str 							=				self.get_scheduler_config(prediction_type='v_prediction'		)
									snake_case__ :     int 							=				scheduler_class(**__UpperCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps		)

									snake_case__ :     List[str] 							=				self.dummy_model()
									snake_case__ :     Any 							=				self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case__ :     Dict 							=				sample.to(__UpperCamelCase		)

									snake_case__ :     int 							=				torch.manual_seed(0		)

									for i, t in enumerate(scheduler.timesteps		):
													snake_case__ :     Dict 							=				scheduler.scale_model_input(__UpperCamelCase   ,	__UpperCamelCase		)

													snake_case__ :     List[str] 							=				model(__UpperCamelCase   ,	__UpperCamelCase		)

													snake_case__ :     Union[str, Any] 							=				scheduler.step(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	generator=__UpperCamelCase		)
													snake_case__ :     Optional[Any] 							=				output.prev_sample

									snake_case__ :     Union[str, Any] 							=				torch.sum(torch.abs(__UpperCamelCase		)		)
									snake_case__ :     str 							=				torch.mean(torch.abs(__UpperCamelCase		)		)

									assert abs(result_sum.item() - 3_2_8.9_9_7_0		) < 1E-2
									assert abs(result_mean.item() - 0.4_2_8_4		) < 1E-3




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									if torch_device == "mps":
													return
									snake_case__ :     Tuple 							=				self.scheduler_classes[0]
									snake_case__ :     str 							=				self.get_scheduler_config()
									snake_case__ :     Tuple 							=				scheduler_class(**__UpperCamelCase		)

									scheduler.set_timesteps(self.num_inference_steps   ,	device=__UpperCamelCase		)
									snake_case__ :     int 							=				torch.manual_seed(0		)

									snake_case__ :     Any 							=				self.dummy_model()
									snake_case__ :     Any 							=				self.dummy_sample_deter.to(__UpperCamelCase		) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
													snake_case__ :     Dict 							=				scheduler.scale_model_input(__UpperCamelCase   ,	__UpperCamelCase		)

													snake_case__ :     Any 							=				model(__UpperCamelCase   ,	__UpperCamelCase		)

													snake_case__ :     Optional[Any] 							=				scheduler.step(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	generator=__UpperCamelCase		)
													snake_case__ :     Optional[Any] 							=				output.prev_sample

									snake_case__ :     Tuple 							=				torch.sum(torch.abs(__UpperCamelCase		)		)
									snake_case__ :     Optional[Any] 							=				torch.mean(torch.abs(__UpperCamelCase		)		)

									assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8		) < 1E-1
									assert abs(result_mean.item() - 1_8.0_3_3_1		) < 1E-3



from typing import TYPE_CHECKING

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


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


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

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

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


