


'''simple docstring'''






import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =(DPMSolverSDEScheduler,)
       UpperCAmelCase		 =1_0






       def      lowerCAmelCase						(     self    ,				**snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       ={
              'num_train_timesteps': 1_1_0_0,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'noise_sampler_seed': 0,
          }

          config.update(**snake_case)
          return config






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=snake_case)






       def      lowerCAmelCase						(     self)		->    Optional[int]:




          '''simple docstring'''
          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      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=snake_case)






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=snake_case)






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




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =self.scheduler_classes[0]
          _UpperCAmelCase  :  Any				       =self.get_scheduler_config()
          _UpperCAmelCase  :  Optional[int]				       =scheduler_class(**snake_case)

          scheduler.set_timesteps(self.num_inference_steps)

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

          for i, t in enumerate(scheduler.timesteps):
             _UpperCAmelCase  :  List[Any]				       =scheduler.scale_model_input(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Tuple				       =model(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Dict				       =scheduler.step(snake_case    ,				snake_case    ,				snake_case)
             _UpperCAmelCase  :  Union[str, Any]				       =output.prev_sample

          _UpperCAmelCase  :  Tuple				       =torch.sum(torch.abs(snake_case))
          _UpperCAmelCase  :  Any				       =torch.mean(torch.abs(snake_case))

          if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75) < 1E-2
             assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77) < 1E-3
          elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06) < 1E-2
             assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52) < 1E-3
          else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62) < 1E-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26) < 1E-3






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




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.scheduler_classes[0]
          _UpperCAmelCase  :  List[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[Any]				       =self.dummy_model()
          _UpperCAmelCase  :  Optional[int]				       =self.dummy_sample_deter * scheduler.init_noise_sigma
          _UpperCAmelCase  :  Optional[int]				       =sample.to(snake_case)

          for i, t in enumerate(scheduler.timesteps):
             _UpperCAmelCase  :  int				       =scheduler.scale_model_input(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Tuple				       =model(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Optional[Any]				       =scheduler.step(snake_case    ,				snake_case    ,				snake_case)
             _UpperCAmelCase  :  List[str]				       =output.prev_sample

          _UpperCAmelCase  :  int				       =torch.sum(torch.abs(snake_case))
          _UpperCAmelCase  :  List[str]				       =torch.mean(torch.abs(snake_case))

          if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53) < 1E-2
             assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84) < 1E-3
          elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03) < 1E-2
             assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97) < 1E-3
          else:
             assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25) < 1E-2
             assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21) < 1E-3






       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =self.scheduler_classes[0]
          _UpperCAmelCase  :  Any				       =self.get_scheduler_config()
          _UpperCAmelCase  :  Tuple				       =scheduler_class(**snake_case)

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

          _UpperCAmelCase  :  List[str]				       =self.dummy_model()
          _UpperCAmelCase  :  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  :  int				       =model(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Dict				       =scheduler.step(snake_case    ,				snake_case    ,				snake_case)
             _UpperCAmelCase  :  int				       =output.prev_sample

          _UpperCAmelCase  :  List[str]				       =torch.sum(torch.abs(snake_case))
          _UpperCAmelCase  :  int				       =torch.mean(torch.abs(snake_case))

          if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38) < 1E-2
             assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35) < 1E-3
          elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12) < 1E-2
             assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71) < 1E-3
          else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62) < 1E-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26) < 1E-3






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =self.scheduler_classes[0]
          _UpperCAmelCase  :  Optional[Any]				       =self.get_scheduler_config()
          _UpperCAmelCase  :  int				       =scheduler_class(**snake_case    ,				use_karras_sigmas=snake_case)

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

          _UpperCAmelCase  :  Any				       =self.dummy_model()
          _UpperCAmelCase  :  int				       =self.dummy_sample_deter.to(snake_case) * scheduler.init_noise_sigma
          _UpperCAmelCase  :  Tuple				       =sample.to(snake_case)

          for t in scheduler.timesteps:
             _UpperCAmelCase  :  List[str]				       =scheduler.scale_model_input(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Any				       =model(snake_case    ,				snake_case)

             _UpperCAmelCase  :  Optional[int]				       =scheduler.step(snake_case    ,				snake_case    ,				snake_case)
             _UpperCAmelCase  :  Dict				       =output.prev_sample

          _UpperCAmelCase  :  Optional[int]				       =torch.sum(torch.abs(snake_case))
          _UpperCAmelCase  :  int				       =torch.mean(torch.abs(snake_case))

          if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88) < 1E-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11) < 1E-2
          elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25) < 1E-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11) < 1E-2
          else:
             assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72) < 1E-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11) < 1E-2





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

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

