





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__a							(		lowercase_					):
       __snake_case  :      Optional[Any]					=						(HeunDiscreteScheduler,)
       __snake_case  :      Dict					=						10



       def        A							(   self		:      Any    ,	**UpperCAmelCase		:      Dict	):
           lowerCAmelCase_    :		Tuple 						=      {
               """num_train_timesteps""": 11_00,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**_lowercase	)
           return config



       def        A							(   self		:      Any	):





           for timesteps in [10, 50, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=_lowercase	)



       def        A							(   self		:      List[Any]	):





           for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]    ,	[0.0002, 0.002, 0.02]	):
               self.check_over_configs(beta_start=_lowercase    ,	beta_end=_lowercase	)



       def        A							(   self		:      Any	):





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



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





           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=_lowercase    ,	clip_sample=_lowercase	)



       def        A							(   self		:      Tuple	):





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



       def        A							(   self		:      int	):
           lowerCAmelCase_    :		Tuple 						=      self.scheduler_classes[0]
           lowerCAmelCase_    :		int 						=      self.get_scheduler_config()
           lowerCAmelCase_    :		int 						=      scheduler_class(**_lowercase	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCAmelCase_    :		Any 						=      self.dummy_model()
           lowerCAmelCase_    :		Any 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCAmelCase_    :		Tuple 						=      sample.to(_lowercase	)

           for i, t in enumerate(scheduler.timesteps	):
               lowerCAmelCase_    :		Dict 						=      scheduler.scale_model_input(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		List[str] 						=      model(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		Optional[int] 						=      scheduler.step(_lowercase    ,	_lowercase    ,	_lowercase	)
               lowerCAmelCase_    :		int 						=      output.prev_sample

           lowerCAmelCase_    :		List[str] 						=      torch.sum(torch.abs(_lowercase	)	)
           lowerCAmelCase_    :		List[Any] 						=      torch.mean(torch.abs(_lowercase	)	)

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233	) < 1e-2
               assert abs(result_mean.item() - 0.0002	) < 1e-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.1233	) < 1e-2
               assert abs(result_mean.item() - 0.0002	) < 1e-3



       def        A							(   self		:      int	):
           lowerCAmelCase_    :		Tuple 						=      self.scheduler_classes[0]
           lowerCAmelCase_    :		Any 						=      self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCAmelCase_    :		Optional[int] 						=      scheduler_class(**_lowercase	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCAmelCase_    :		str 						=      self.dummy_model()
           lowerCAmelCase_    :		Dict 						=      self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCAmelCase_    :		int 						=      sample.to(_lowercase	)

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

               lowerCAmelCase_    :		str 						=      model(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		Optional[Any] 						=      scheduler.step(_lowercase    ,	_lowercase    ,	_lowercase	)
               lowerCAmelCase_    :		List[Any] 						=      output.prev_sample

           lowerCAmelCase_    :		List[str] 						=      torch.sum(torch.abs(_lowercase	)	)
           lowerCAmelCase_    :		List[str] 						=      torch.mean(torch.abs(_lowercase	)	)

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
               assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
               assert abs(result_mean.item() - 0.0002	) < 1e-3



       def        A							(   self		:      Optional[int]	):
           lowerCAmelCase_    :		Tuple 						=      self.scheduler_classes[0]
           lowerCAmelCase_    :		List[str] 						=      self.get_scheduler_config()
           lowerCAmelCase_    :		List[Any] 						=      scheduler_class(**_lowercase	)

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

           lowerCAmelCase_    :		Dict 						=      self.dummy_model()
           lowerCAmelCase_    :		List[str] 						=      self.dummy_sample_deter.to(_lowercase	) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               lowerCAmelCase_    :		List[Any] 						=      scheduler.scale_model_input(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		Any 						=      model(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		List[Any] 						=      scheduler.step(_lowercase    ,	_lowercase    ,	_lowercase	)
               lowerCAmelCase_    :		List[str] 						=      output.prev_sample

           lowerCAmelCase_    :		Union[str, Any] 						=      torch.sum(torch.abs(_lowercase	)	)
           lowerCAmelCase_    :		Any 						=      torch.mean(torch.abs(_lowercase	)	)

           if str(_lowercase	).startswith("""cpu"""	):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233	) < 1e-2
               assert abs(result_mean.item() - 0.0002	) < 1e-3
           elif str(_lowercase	).startswith("""mps"""	):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002	) < 1e-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.1233	) < 1e-2
               assert abs(result_mean.item() - 0.0002	) < 1e-3



       def        A							(   self		:      List[Any]	):
           lowerCAmelCase_    :		List[Any] 						=      self.scheduler_classes[0]
           lowerCAmelCase_    :		Optional[int] 						=      self.get_scheduler_config()
           lowerCAmelCase_    :		Dict 						=      scheduler_class(**_lowercase    ,	use_karras_sigmas=_lowercase	)

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

           lowerCAmelCase_    :		List[Any] 						=      self.dummy_model()
           lowerCAmelCase_    :		List[Any] 						=      self.dummy_sample_deter.to(_lowercase	) * scheduler.init_noise_sigma
           lowerCAmelCase_    :		List[Any] 						=      sample.to(_lowercase	)

           for t in scheduler.timesteps:
               lowerCAmelCase_    :		Optional[int] 						=      scheduler.scale_model_input(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		List[Any] 						=      model(_lowercase    ,	_lowercase	)

               lowerCAmelCase_    :		str 						=      scheduler.step(_lowercase    ,	_lowercase    ,	_lowercase	)
               lowerCAmelCase_    :		str 						=      output.prev_sample

           lowerCAmelCase_    :		Optional[Any] 						=      torch.sum(torch.abs(_lowercase	)	)
           lowerCAmelCase_    :		Union[str, Any] 						=      torch.mean(torch.abs(_lowercase	)	)

           assert abs(result_sum.item() - 0.0_0015	) < 1e-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5e-0_7	) < 1e-2









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case				= {"""configuration_bit""": ["""BIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BitConfig""", """BitOnnxConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """BIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BitForImageClassification""",
      """BitModel""",
      """BitPreTrainedModel""",
      """BitBackbone""",
  ]


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""BitImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bit import (
        BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BitBackbone,
        BitForImageClassification,
        BitModel,
        BitPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bit import BitImageProcessor

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






