import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     (EulerDiscreteScheduler,)
							_UpperCAmelCase						    =     1_0







							def 				snake_case  (				self     :    Dict	,					**__snake_case     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     {
										    '''num_train_timesteps''': 1100,
										    '''beta_start''': 0.0_0_0_1,
										    '''beta_end''': 0.0_2,
										    '''beta_schedule''': '''linear''',
										}

										config.update(**__snake_case		)
										return config







							def 				snake_case  (				self     :    Any		):
										for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=__snake_case		)







							def 				snake_case  (				self     :    List[str]		):
										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=__snake_case	,					beta_end=__snake_case		)







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







							def 				snake_case  (				self     :    int		):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__snake_case		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:int						  =     self.scheduler_classes[0]
										lowerCamelCase						:List[Any]						  =     self.get_scheduler_config()
										lowerCamelCase						:List[str]						  =     scheduler_class(**__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps		)

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

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

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

													lowerCamelCase						:Dict						  =     model(__snake_case	,					__snake_case		)

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

										lowerCamelCase						:Optional[int]						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:Tuple						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 1_0.0_8_0_7		) < 1e-2
										assert abs(result_mean.item() - 0.0_1_3_1		) < 1e-3







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[Any]						  =     self.scheduler_classes[0]
										lowerCamelCase						:List[Any]						  =     self.get_scheduler_config(prediction_type='''v_prediction'''		)
										lowerCamelCase						:Dict						  =     scheduler_class(**__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps		)

										lowerCamelCase						:Optional[int]						  =     torch.manual_seed(0		)

										lowerCamelCase						:Union[str, Any]						  =     self.dummy_model()
										lowerCamelCase						:str						  =     self.dummy_sample_deter * scheduler.init_noise_sigma
										lowerCamelCase						:int						  =     sample.to(__snake_case		)

										for i, t in enumerate(scheduler.timesteps		):
													lowerCamelCase						:int						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:List[str]						  =     model(__snake_case	,					__snake_case		)

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

										lowerCamelCase						:List[Any]						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:str						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 0.0_0_0_2		) < 1e-2
										assert abs(result_mean.item() - 2.2_6_7_6e-0_6		) < 1e-3







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Optional[int]						  =     self.scheduler_classes[0]
										lowerCamelCase						:Any						  =     self.get_scheduler_config()
										lowerCamelCase						:List[str]						  =     scheduler_class(**__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps	,					device=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     torch.manual_seed(0		)

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

										for t in scheduler.timesteps:
													lowerCamelCase						:Union[str, Any]						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:int						  =     model(__snake_case	,					__snake_case		)

													lowerCamelCase						:Any						  =     scheduler.step(__snake_case	,					__snake_case	,					__snake_case	,					generator=__snake_case		)
													lowerCamelCase						:Tuple						  =     output.prev_sample

										lowerCamelCase						:List[str]						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:Tuple						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 1_0.0_8_0_7		) < 1e-2
										assert abs(result_mean.item() - 0.0_1_3_1		) < 1e-3







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:Union[str, Any]						  =     self.scheduler_classes[0]
										lowerCamelCase						:Tuple						  =     self.get_scheduler_config()
										lowerCamelCase						:List[str]						  =     scheduler_class(**__snake_case	,					use_karras_sigmas=__snake_case		)

										scheduler.set_timesteps(self.num_inference_steps	,					device=__snake_case		)

										lowerCamelCase						:int						  =     torch.manual_seed(0		)

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

										for t in scheduler.timesteps:
													lowerCamelCase						:str						  =     scheduler.scale_model_input(__snake_case	,					__snake_case		)

													lowerCamelCase						:Optional[int]						  =     model(__snake_case	,					__snake_case		)

													lowerCamelCase						:int						  =     scheduler.step(__snake_case	,					__snake_case	,					__snake_case	,					generator=__snake_case		)
													lowerCamelCase						:Optional[Any]						  =     output.prev_sample

										lowerCamelCase						:str						  =     torch.sum(torch.abs(__snake_case		)		)
										lowerCamelCase						:List[str]						  =     torch.mean(torch.abs(__snake_case		)		)

										assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9		) < 1e-2
										assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3		) < 1e-3




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__           =							{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """NllbMoeForConditionalGeneration""",
			    """NllbMoeModel""",
			    """NllbMoePreTrainedModel""",
			    """NllbMoeTop2Router""",
			    """NllbMoeSparseMLP""",
			]


if TYPE_CHECKING:
			from .configuration_nllb_moe import (
			    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    NllbMoeConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nllb_moe import (
						    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NllbMoeForConditionalGeneration,
						    NllbMoeModel,
						    NllbMoePreTrainedModel,
						    NllbMoeSparseMLP,
						    NllbMoeTopaRouter,
						)


else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



