






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        __snake_case(					__UpperCamelCase      ):
 lowercase :       Union[str, Any]		      =		(EulerAncestralDiscreteScheduler,)
 lowercase :       Optional[Any]		      =		10



 def 						UpperCAmelCase    (	self   :   int ,		**UpperCAmelCase__   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	Tuple         = {
        """num_train_timesteps""": 1_100,
        """beta_start""": 0.0_001,
        """beta_end""": 0.02,
        """beta_schedule""": """linear""",
    }

    config.update(**UpperCAmelCase__	)
    return config



 def 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






    for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """simple docstring"""






    for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,		[0.0_002, 0.002, 0.02]	):
       self.check_over_configs(beta_start=UpperCAmelCase__ ,		beta_end=UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    lowercase__						:	Any         = self.scheduler_classes[0]
    lowercase__						:	Tuple         = self.get_scheduler_config()
    lowercase__						:	int         = scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps	)

    lowercase__						:	Tuple         = torch.manual_seed(0	)

    lowercase__						:	str         = self.dummy_model()
    lowercase__						:	Tuple         = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    lowercase__						:	Dict         = sample.to(UpperCAmelCase__	)

    for i, t in enumerate(scheduler.timesteps	):
       lowercase__						:	Optional[Any]         = scheduler.scale_model_input(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	List[Any]         = model(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	List[Any]         = scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	List[Any]         = output.prev_sample

    lowercase__						:	int         = torch.sum(torch.abs(UpperCAmelCase__	)	)
    lowercase__						:	Tuple         = torch.mean(torch.abs(UpperCAmelCase__	)	)

    assert abs(result_sum.item() - 152.3_192	) < 1e-2
    assert abs(result_mean.item() - 0.1_983	) < 1e-3



 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Tuple         = self.scheduler_classes[0]
    lowercase__						:	Optional[Any]         = self.get_scheduler_config(prediction_type="""v_prediction"""	)
    lowercase__						:	Optional[int]         = scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps	)

    lowercase__						:	Tuple         = torch.manual_seed(0	)

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

    for i, t in enumerate(scheduler.timesteps	):
       lowercase__						:	Dict         = scheduler.scale_model_input(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	List[str]         = model(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	Union[str, Any]         = scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	Tuple         = output.prev_sample

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

    assert abs(result_sum.item() - 108.4_439	) < 1e-2
    assert abs(result_mean.item() - 0.1_412	) < 1e-3







 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = self.scheduler_classes[0]
    lowercase__						:	str         = self.get_scheduler_config()
    lowercase__						:	Any         = scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps ,		device=UpperCAmelCase__	)
    lowercase__						:	Optional[int]         = torch.manual_seed(0	)

    lowercase__						:	List[str]         = self.dummy_model()
    lowercase__						:	Tuple         = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    lowercase__						:	Optional[int]         = sample.to(UpperCAmelCase__	)

    for t in scheduler.timesteps:
       lowercase__						:	str         = scheduler.scale_model_input(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	str         = model(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	Tuple         = scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	Any         = output.prev_sample

    lowercase__						:	Optional[Any]         = torch.sum(torch.abs(UpperCAmelCase__	)	)
    lowercase__						:	List[str]         = torch.mean(torch.abs(UpperCAmelCase__	)	)

    assert abs(result_sum.item() - 152.3_192	) < 1e-2
    assert abs(result_mean.item() - 0.1_983	) < 1e-3










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a    =       {'configuration_nat': ['NAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NatConfig']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a    =       [
     'NAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'NatForImageClassification',
     'NatModel',
     'NatPreTrainedModel',
     'NatBackbone',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

 a    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


