"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 					a					(				A_							):
  A_ :				Tuple 			=							(DPMSolverMultistepScheduler,)
  A_ :				List[str] 			=							(('''num_inference_steps''', 25),)



  def        lowerCAmelCase_				(		self   :    List[Any]						,  **lowerCamelCase_   :    int     )      -> int:
      __a			      =						{
          """num_train_timesteps""": 10_00,
          """beta_start""": 0.00_01,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
          """solver_order""": 2,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
          """sample_max_value""": 1.0,
          """algorithm_type""": """dpmsolver++""",
          """solver_type""": """midpoint""",
          """lower_order_final""": False,
          """lambda_min_clipped""": -float("""inf"""     ),
          """variance_type""": None,
      }

      config.update(**lowerCamelCase_     )
      return config



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    List[str]=0						,  **lowerCamelCase_   :    List[Any]     )      -> str:
      __a			      =						dict(self.forward_default_kwargs     )
      __a			      =						kwargs.pop("""num_inference_steps"""						,  lowerCamelCase_     )
      __a			      =						self.dummy_sample
      __a			      =						0.1 * sample
      __a			      =						[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Any:
      pass



  def        lowerCAmelCase_				(		self   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]=0						,  **lowerCamelCase_   :    Any     )      -> Optional[int]:
      __a			      =						dict(self.forward_default_kwargs     )
      __a			      =						kwargs.pop("""num_inference_steps"""						,  lowerCamelCase_     )
      __a			      =						self.dummy_sample
      __a			      =						0.1 * sample
      __a			      =						[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
          __a			      =						self.get_scheduler_config()
          __a			      =						scheduler_class(**lowerCamelCase_     )
          scheduler.set_timesteps(lowerCamelCase_     )

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

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

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

          __a			      =						scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  **lowerCamelCase_     ).prev_sample
          __a			      =						new_scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  **lowerCamelCase_     ).prev_sample

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



  def        lowerCAmelCase_				(		self   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]=None						,  **lowerCamelCase_   :    str     )      -> Dict:
      if scheduler is None:
          __a			      =						self.scheduler_classes[0]
          __a			      =						self.get_scheduler_config(**lowerCamelCase_     )
          __a			      =						scheduler_class(**lowerCamelCase_     )

      __a			      =						10
      __a			      =						self.dummy_model()
      __a			      =						self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase_     )

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

      return sample



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> List[Any]:
      __a			      =						dict(self.forward_default_kwargs     )

      __a			      =						kwargs.pop("""num_inference_steps"""						,  lowerCamelCase_     )

      for scheduler_class in self.scheduler_classes:
          __a			      =						self.get_scheduler_config()
          __a			      =						scheduler_class(**lowerCamelCase_     )

          __a			      =						self.dummy_sample
          __a			      =						0.1 * sample

          if num_inference_steps is not None and hasattr(lowerCamelCase_						,  """set_timesteps"""     ):
              scheduler.set_timesteps(lowerCamelCase_     )
          elif num_inference_steps is not None and not hasattr(lowerCamelCase_						,  """set_timesteps"""     ):
              __a			      =						num_inference_steps

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

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

          __a			      =						scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  **lowerCamelCase_     ).prev_sample
          __a			      =						scheduler.step(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  **lowerCamelCase_     ).prev_sample

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



  def        lowerCAmelCase_				(		self   :    int     )      -> str:
      for timesteps in [25, 50, 1_00, 9_99, 10_00]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Tuple:
      self.check_over_configs(thresholding=lowerCamelCase_     )
      for order in [1, 2, 3]:
          for solver_type in ["midpoint", "heun"]:
              for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=lowerCamelCase_						,  prediction_type=lowerCamelCase_						,  sample_max_value=lowerCamelCase_						,  algorithm_type="""dpmsolver++"""						,  solver_order=lowerCamelCase_						,  solver_type=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    int     )      -> Any:
      for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
          for solver_type in ["midpoint", "heun"]:
              for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                      if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                          if order == 3:
                              continue
                      else:
                          self.check_over_configs(
                              solver_order=lowerCamelCase_						,  solver_type=lowerCamelCase_						,  prediction_type=lowerCamelCase_						,  algorithm_type=lowerCamelCase_						,  )
                      __a			      =						self.full_loop(
                          solver_order=lowerCamelCase_						,  solver_type=lowerCamelCase_						,  prediction_type=lowerCamelCase_						,  algorithm_type=lowerCamelCase_						,  )
                      assert not torch.isnan(lowerCamelCase_     ).any(), "Samples have nan numbers"



  def        lowerCAmelCase_				(		self   :    Any     )      -> Optional[int]:
      self.check_over_configs(lower_order_final=lowerCamelCase_     )
      self.check_over_configs(lower_order_final=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Any:
      self.check_over_configs(lambda_min_clipped=-float("""inf"""     )     )
      self.check_over_configs(lambda_min_clipped=-5.1     )



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Union[str, Any]:
      self.check_over_configs(variance_type=lowerCamelCase_     )
      self.check_over_configs(variance_type="""learned_range"""     )



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Optional[Any]:
      for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
          self.check_over_forward(num_inference_steps=lowerCamelCase_						,  time_step=0     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> str:
      __a			      =						self.full_loop()
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_mean.item() - 0.33_01     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> List[Any]:
      __a			      =						self.full_loop(thresholding=lowerCamelCase_						,  dynamic_thresholding_ratio=0.87						,  sample_max_value=0.5     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_mean.item() - 1.13_64     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Dict     )      -> List[Any]:
      __a			      =						self.full_loop(prediction_type="""v_prediction"""     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_mean.item() - 0.22_51     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Any     )      -> Optional[int]:
      __a			      =						self.full_loop(prediction_type="""v_prediction"""						,  use_karras_sigmas=lowerCamelCase_     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_mean.item() - 0.20_96     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Any     )      -> Any:
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      __a			      =						DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
      __a			      =						self.full_loop(scheduler=lowerCamelCase_     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_mean.item() - 0.33_01     ) < 1E-3

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

      __a			      =						self.full_loop(scheduler=lowerCamelCase_     )
      __a			      =						torch.mean(torch.abs(lowerCamelCase_     )     )

      assert abs(result_mean.item() - 0.33_01     ) < 1E-3



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> int:
      __a			      =						self.scheduler_classes[0]
      __a			      =						self.get_scheduler_config(thresholding=lowerCamelCase_						,  dynamic_thresholding_ratio=0     )
      __a			      =						scheduler_class(**lowerCamelCase_     )

      __a			      =						10
      __a			      =						self.dummy_model()
      __a			      =						self.dummy_sample_deter.half()
      scheduler.set_timesteps(lowerCamelCase_     )

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

      assert sample.dtype == torch.floataa



  def        lowerCAmelCase_				(		self   :    Optional[int]						,  **lowerCamelCase_   :    Tuple     )      -> Optional[int]:
      for scheduler_class in self.scheduler_classes:
          __a			      =						self.get_scheduler_config(**lowerCamelCase_     )
          __a			      =						scheduler_class(**lowerCamelCase_     )

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

"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
