





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					(UniPCMultistepScheduler,)
  a_              =					(("""num_inference_steps""", 25),)






  def 							lowercase						(							self					:						List[str]   ,    **lowerCAmelCase_					:						List[Any]	)				->		str:
     __lowerCAmelCase				      =      {
         'num_train_timesteps': 1_0_0_0,
         'beta_start': 0.00_01,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'solver_order': 2,
         'solver_type': 'bh2',
     }

     config.update(**lowercase__	)
     return config






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Union[str, Any]=0   ,    **lowerCAmelCase_					:						Any	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      dict(self.forward_default_kwargs	)
     __lowerCAmelCase				      =      kwargs.pop('num_inference_steps'   ,    lowercase__	)
     __lowerCAmelCase				      =      self.dummy_sample
     __lowerCAmelCase				      =      0.1 * sample
     __lowerCAmelCase				      =      [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase				      =      self.get_scheduler_config(**lowercase__	)
        __lowerCAmelCase				      =      scheduler_class(**lowercase__	)
        scheduler.set_timesteps(lowercase__	)
        # copy over dummy past residuals
        __lowerCAmelCase				      =      dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowercase__	)
           __lowerCAmelCase				      =      scheduler_class.from_pretrained(lowercase__	)
           new_scheduler.set_timesteps(lowercase__	)
           # copy over dummy past residuals
           __lowerCAmelCase				      =      dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCAmelCase      ,      __lowerCAmelCase				      =      sample, sample
        for t in range(lowercase__   ,    time_step + scheduler.config.solver_order + 1	):
           __lowerCAmelCase				      =      scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__   ,    **lowercase__	).prev_sample
           __lowerCAmelCase				      =      new_scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__   ,    **lowercase__	).prev_sample

           assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						int=0   ,    **lowerCAmelCase_					:						int	)				->		Optional[Any]:
     __lowerCAmelCase				      =      dict(self.forward_default_kwargs	)
     __lowerCAmelCase				      =      kwargs.pop('num_inference_steps'   ,    lowercase__	)
     __lowerCAmelCase				      =      self.dummy_sample
     __lowerCAmelCase				      =      0.1 * sample
     __lowerCAmelCase				      =      [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase				      =      self.get_scheduler_config()
        __lowerCAmelCase				      =      scheduler_class(**lowercase__	)
        scheduler.set_timesteps(lowercase__	)

        # copy over dummy past residuals (must be after setting timesteps)
        __lowerCAmelCase				      =      dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowercase__	)
           __lowerCAmelCase				      =      scheduler_class.from_pretrained(lowercase__	)
           # copy over dummy past residuals
           new_scheduler.set_timesteps(lowercase__	)

           # copy over dummy past residual (must be after setting timesteps)
           __lowerCAmelCase				      =      dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCAmelCase				      =      scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__   ,    **lowercase__	).prev_sample
        __lowerCAmelCase				      =      new_scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__   ,    **lowercase__	).prev_sample

        assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Union[str, Any]=None   ,    **lowerCAmelCase_					:						Dict	)				->		str:
     if scheduler is None:
        __lowerCAmelCase				      =      self.scheduler_classes[0]
        __lowerCAmelCase				      =      self.get_scheduler_config(**lowercase__	)
        __lowerCAmelCase				      =      scheduler_class(**lowercase__	)

     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config(**lowercase__	)
     __lowerCAmelCase				      =      scheduler_class(**lowercase__	)

     __lowerCAmelCase				      =      1_0
     __lowerCAmelCase				      =      self.dummy_model()
     __lowerCAmelCase				      =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase__	)

     for i, t in enumerate(scheduler.timesteps	):
        __lowerCAmelCase				      =      model(lowercase__   ,    lowercase__	)
        __lowerCAmelCase				      =      scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__	).prev_sample

     return sample






  def 							lowercase						(							self					:						List[str]	)				->		Optional[Any]:
     __lowerCAmelCase				      =      dict(self.forward_default_kwargs	)

     __lowerCAmelCase				      =      kwargs.pop('num_inference_steps'   ,    lowercase__	)

     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase				      =      self.get_scheduler_config()
        __lowerCAmelCase				      =      scheduler_class(**lowercase__	)

        __lowerCAmelCase				      =      self.dummy_sample
        __lowerCAmelCase				      =      0.1 * sample

        if num_inference_steps is not None and hasattr(lowercase__   ,    'set_timesteps'	):
           scheduler.set_timesteps(lowercase__	)
        elif num_inference_steps is not None and not hasattr(lowercase__   ,    'set_timesteps'	):
           __lowerCAmelCase				      =      num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        __lowerCAmelCase				      =      [residual + 0.2, residual + 0.15, residual + 0.10]
        __lowerCAmelCase				      =      dummy_past_residuals[: scheduler.config.solver_order]

        __lowerCAmelCase				      =      scheduler.timesteps[5]
        __lowerCAmelCase				      =      scheduler.timesteps[6]

        __lowerCAmelCase				      =      scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__   ,    **lowercase__	).prev_sample
        __lowerCAmelCase				      =      scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__   ,    **lowercase__	).prev_sample

        self.assertEqual(output_a.shape   ,    sample.shape	)
        self.assertEqual(output_a.shape   ,    output_a.shape	)






  def 							lowercase						(							self					:						str	)				->		Dict:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     __lowerCAmelCase				      =      UniPCMultistepScheduler(**self.get_scheduler_config()	)
     __lowerCAmelCase				      =      self.full_loop(scheduler=lowercase__	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowercase__	)	)

     assert abs(result_mean.item() - 0.24_64	) < 1e-3

     __lowerCAmelCase				      =      DPMSolverSinglestepScheduler.from_config(scheduler.config	)
     __lowerCAmelCase				      =      DEISMultistepScheduler.from_config(scheduler.config	)
     __lowerCAmelCase				      =      DPMSolverMultistepScheduler.from_config(scheduler.config	)
     __lowerCAmelCase				      =      UniPCMultistepScheduler.from_config(scheduler.config	)

     __lowerCAmelCase				      =      self.full_loop(scheduler=lowercase__	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowercase__	)	)

     assert abs(result_mean.item() - 0.24_64	) < 1e-3






  def 							lowercase						(							self					:						Optional[Any]	)				->		Optional[Any]:
     for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowercase__	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Dict:
     self.check_over_configs(thresholding=lowercase__	)
     for order in [1, 2, 3]:
        for solver_type in ["bh1", "bh2"]:
           for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=lowercase__   ,    prediction_type=lowercase__   ,    sample_max_value=lowercase__   ,    solver_order=lowercase__   ,    solver_type=lowercase__   ,    )






  def 							lowercase						(							self					:						int	)				->		Any:
     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=lowercase__	)






  def 							lowercase						(							self					:						Tuple	)				->		List[str]:
     for solver_type in ["bh1", "bh2"]:
        for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  solver_order=lowercase__   ,    solver_type=lowercase__   ,    prediction_type=lowercase__   ,    )
              __lowerCAmelCase				      =      self.full_loop(
                  solver_order=lowercase__   ,    solver_type=lowercase__   ,    prediction_type=lowercase__   ,    )
              assert not torch.isnan(lowercase__	).any(), "Samples have nan numbers"






  def 							lowercase						(							self					:						Optional[Any]	)				->		Union[str, Any]:
     self.check_over_configs(lower_order_final=lowercase__	)
     self.check_over_configs(lower_order_final=lowercase__	)






  def 							lowercase						(							self					:						Tuple	)				->		int:
     for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
        self.check_over_forward(num_inference_steps=lowercase__   ,    time_step=0	)






  def 							lowercase						(							self					:						Any	)				->		List[str]:
     __lowerCAmelCase				      =      self.full_loop()
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowercase__	)	)

     assert abs(result_mean.item() - 0.24_64	) < 1e-3






  def 							lowercase						(							self					:						Optional[int]	)				->		Dict:
     __lowerCAmelCase				      =      self.full_loop(prediction_type='v_prediction'	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowercase__	)	)

     assert abs(result_mean.item() - 0.10_14	) < 1e-3






  def 							lowercase						(							self					:						Dict	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config(thresholding=lowercase__   ,    dynamic_thresholding_ratio=0	)
     __lowerCAmelCase				      =      scheduler_class(**lowercase__	)

     __lowerCAmelCase				      =      1_0
     __lowerCAmelCase				      =      self.dummy_model()
     __lowerCAmelCase				      =      self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowercase__	)

     for i, t in enumerate(scheduler.timesteps	):
        __lowerCAmelCase				      =      model(lowercase__   ,    lowercase__	)
        __lowerCAmelCase				      =      scheduler.step(lowercase__   ,    lowercase__   ,    lowercase__	).prev_sample

     assert sample.dtype == torch.floataa






  def 							lowercase						(							self					:						List[Any]   ,    **lowerCAmelCase_					:						List[str]	)				->		str:
     for scheduler_class in self.scheduler_classes:
        __lowerCAmelCase				      =      self.get_scheduler_config(**lowercase__	)
        __lowerCAmelCase				      =      scheduler_class(**lowercase__	)

        scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
        assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps


from typing import TYPE_CHECKING

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


_lowercase:	Any       			=		{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

				_lowercase:	Tuple       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






