






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        __snake_case(					a__      ):
 lowercase :       Tuple		      =		(LMSDiscreteScheduler,)
 lowercase :       Any		      =		10



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

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = {
        "num_train_timesteps": 1_100,
        "beta_start": 0.0_001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
    }

    config.update(**__A	)
    return config



 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






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



 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=__A ,		beta_end=__A	)



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    for t in [0, 500, 800]:
       self.check_over_forward(time_step=__A	)



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	Dict         = self.scheduler_classes[0]
    lowercase__						:	List[Any]         = self.get_scheduler_config()
    lowercase__						:	Any         = scheduler_class(**__A	)

    scheduler.set_timesteps(self.num_inference_steps	)

    lowercase__						:	Union[str, Any]         = self.dummy_model()
    lowercase__						:	Dict         = self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps	):
       lowercase__						:	Tuple         = scheduler.scale_model_input(__A ,		__A	)

       lowercase__						:	Optional[int]         = model(__A ,		__A	)

       lowercase__						:	Dict         = scheduler.step(__A ,		__A ,		__A	)
       lowercase__						:	List[Any]         = output.prev_sample

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

    assert abs(result_sum.item() - 1_006.388	) < 1e-2
    assert abs(result_mean.item() - 1.31	) < 1e-3



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

    """simple docstring"""






    lowercase__						:	str         = self.scheduler_classes[0]
    lowercase__						:	Optional[int]         = self.get_scheduler_config(prediction_type="""v_prediction"""	)
    lowercase__						:	Union[str, Any]         = scheduler_class(**__A	)

    scheduler.set_timesteps(self.num_inference_steps	)

    lowercase__						:	int         = self.dummy_model()
    lowercase__						:	str         = self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps	):
       lowercase__						:	Any         = scheduler.scale_model_input(__A ,		__A	)

       lowercase__						:	Optional[int]         = model(__A ,		__A	)

       lowercase__						:	List[Any]         = scheduler.step(__A ,		__A ,		__A	)
       lowercase__						:	int         = output.prev_sample

    lowercase__						:	List[str]         = torch.sum(torch.abs(__A	)	)
    lowercase__						:	List[str]         = torch.mean(torch.abs(__A	)	)

    assert abs(result_sum.item() - 0.0_017	) < 1e-2
    assert abs(result_mean.item() - 2.2_676e-06	) < 1e-3



 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Dict         = self.scheduler_classes[0]
    lowercase__						:	Tuple         = self.get_scheduler_config()
    lowercase__						:	Optional[Any]         = scheduler_class(**__A	)

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

    lowercase__						:	List[str]         = self.dummy_model()
    lowercase__						:	Optional[Any]         = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    lowercase__						:	Union[str, Any]         = sample.to(__A	)

    for i, t in enumerate(scheduler.timesteps	):
       lowercase__						:	str         = scheduler.scale_model_input(__A ,		__A	)

       lowercase__						:	Any         = model(__A ,		__A	)

       lowercase__						:	int         = scheduler.step(__A ,		__A ,		__A	)
       lowercase__						:	List[str]         = output.prev_sample

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

    assert abs(result_sum.item() - 1_006.388	) < 1e-2
    assert abs(result_mean.item() - 1.31	) < 1e-3







 def 						UpperCAmelCase    (	self   :   str	):

    """simple docstring"""






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

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

    lowercase__						:	str         = self.dummy_model()
    lowercase__						:	Any         = self.dummy_sample_deter.to(__A	) * scheduler.init_noise_sigma
    lowercase__						:	Union[str, Any]         = sample.to(__A	)

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

       lowercase__						:	Optional[int]         = model(__A ,		__A	)

       lowercase__						:	List[str]         = scheduler.step(__A ,		__A ,		__A	)
       lowercase__						:	Dict         = output.prev_sample

    lowercase__						:	List[str]         = torch.sum(torch.abs(__A	)	)
    lowercase__						:	str         = torch.mean(torch.abs(__A	)	)

    assert abs(result_sum.item() - 3_812.9_927	) < 2e-2
    assert abs(result_mean.item() - 4.9_648	) < 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__)


