





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						int     		=				(DPMSolverSinglestepScheduler,)
   _UpperCamelCase :						List[str]     		=				(("""num_inference_steps""", 25),)





   def 	_snake_case ( self					,			**snake_case      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  {
          "num_train_timesteps": 1_000,
          "beta_start": 0.0_001,
          "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",
          "lambda_min_clipped": -float("inf"      ),
          "variance_type": None,
      }

      config.update(**snake_case      )
      return config





   def 	_snake_case ( self					,			snake_case=0					,			**snake_case      )				->  List[str]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  dict(self.forward_default_kwargs      )
      a__	:						Union[str, Any]      		=  kwargs.pop("num_inference_steps"					,			snake_case      )
      a__	:						Any      		=  self.dummy_sample
      a__	:						List[str]      		=  0.1 * sample
      a__	:						Optional[Any]      		=  [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
         a__	:						List[Any]      		=  self.get_scheduler_config(**snake_case      )
         a__	:						Tuple      		=  scheduler_class(**snake_case      )
         scheduler.set_timesteps(snake_case      )
         # copy over dummy past residuals
         a__	:						List[str]      		=  dummy_past_residuals[: scheduler.config.solver_order]

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

         a__		,     a__	:						Tuple      		=  sample, sample
         for t in range(snake_case					,			time_step + scheduler.config.solver_order + 1      ):
            a__	:						str      		=  scheduler.step(snake_case					,			snake_case					,			snake_case					,			**snake_case      ).prev_sample
            a__	:						List[str]      		=  new_scheduler.step(snake_case					,			snake_case					,			snake_case					,			**snake_case      ).prev_sample

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





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      pass





   def 	_snake_case ( self					,			snake_case=0					,			**snake_case      )				->  Optional[int]:
      """simple docstring"""





      a__	:						Dict      		=  dict(self.forward_default_kwargs      )
      a__	:						Tuple      		=  kwargs.pop("num_inference_steps"					,			snake_case      )
      a__	:						Any      		=  self.dummy_sample
      a__	:						Optional[int]      		=  0.1 * sample
      a__	:						Union[str, Any]      		=  [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
         a__	:						Union[str, Any]      		=  self.get_scheduler_config()
         a__	:						str      		=  scheduler_class(**snake_case      )
         scheduler.set_timesteps(snake_case      )

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

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(snake_case      )
            a__	:						List[Any]      		=  scheduler_class.from_pretrained(snake_case      )
            # copy over dummy past residuals
            new_scheduler.set_timesteps(snake_case      )

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

         a__	:						Union[str, Any]      		=  scheduler.step(snake_case					,			snake_case					,			snake_case					,			**snake_case      ).prev_sample
         a__	:						Tuple      		=  new_scheduler.step(snake_case					,			snake_case					,			snake_case					,			**snake_case      ).prev_sample

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





   def 	_snake_case ( self					,			snake_case=None					,			**snake_case      )				->  str:
      """simple docstring"""





      if scheduler is None:
         a__	:						int      		=  self.scheduler_classes[0]
         a__	:						Any      		=  self.get_scheduler_config(**snake_case      )
         a__	:						Union[str, Any]      		=  scheduler_class(**snake_case      )

      a__	:						int      		=  self.scheduler_classes[0]
      a__	:						Tuple      		=  self.get_scheduler_config(**snake_case      )
      a__	:						Any      		=  scheduler_class(**snake_case      )

      a__	:						Any      		=  10
      a__	:						Optional[int]      		=  self.dummy_model()
      a__	:						List[Any]      		=  self.dummy_sample_deter
      scheduler.set_timesteps(snake_case      )

      for i, t in enumerate(scheduler.timesteps      ):
         a__	:						Optional[int]      		=  model(snake_case					,			snake_case      )
         a__	:						str      		=  scheduler.step(snake_case					,			snake_case					,			snake_case      ).prev_sample

      return sample





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Optional[int]      		=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
      a__	:						List[Any]      		=  50
      a__	:						int      		=  self.dummy_model()
      a__	:						str      		=  self.dummy_sample_deter
      scheduler.set_timesteps(snake_case      )

      # make sure that the first t is uneven
      for i, t in enumerate(scheduler.timesteps[3:]      ):
         a__	:						List[Any]      		=  model(snake_case					,			snake_case      )
         a__	:						Tuple      		=  scheduler.step(snake_case					,			snake_case					,			snake_case      ).prev_sample

      a__	:						str      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.2_574      ) < 1E-3





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      for timesteps in [25, 50, 100, 999, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Tuple      		=  DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
      a__	:						Any      		=  self.full_loop(scheduler=snake_case      )
      a__	:						Dict      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.2_791      ) < 1E-3

      a__	:						Union[str, Any]      		=  DEISMultistepScheduler.from_config(scheduler.config      )
      a__	:						Any      		=  DPMSolverMultistepScheduler.from_config(scheduler.config      )
      a__	:						Any      		=  UniPCMultistepScheduler.from_config(scheduler.config      )
      a__	:						Dict      		=  DPMSolverSinglestepScheduler.from_config(scheduler.config      )

      a__	:						Union[str, Any]      		=  self.full_loop(scheduler=snake_case      )
      a__	:						Tuple      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.2_791      ) < 1E-3





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      self.check_over_configs(thresholding=snake_case      )
      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=snake_case					,			prediction_type=snake_case					,			sample_max_value=snake_case					,			algorithm_type="dpmsolver++"					,			solver_order=snake_case					,			solver_type=snake_case					,			)





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      for algorithm_type in ["dpmsolver", "dpmsolver++"]:
         for solver_type in ["midpoint", "heun"]:
            for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=snake_case					,			solver_type=snake_case					,			prediction_type=snake_case					,			algorithm_type=snake_case					,			)
                  a__	:						List[str]      		=  self.full_loop(
                      solver_order=snake_case					,			solver_type=snake_case					,			prediction_type=snake_case					,			algorithm_type=snake_case					,			)
                  assert not torch.isnan(snake_case      ).any(), "Samples have nan numbers"





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      self.check_over_configs(lower_order_final=snake_case      )
      self.check_over_configs(lower_order_final=snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      self.check_over_configs(lambda_min_clipped=-float("inf"      )      )
      self.check_over_configs(lambda_min_clipped=-5.1      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      self.check_over_configs(variance_type=snake_case      )
      self.check_over_configs(variance_type="learned_range"      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
         self.check_over_forward(num_inference_steps=snake_case					,			time_step=0      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						int      		=  self.full_loop()
      a__	:						List[Any]      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.2_791      ) < 1E-3





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						List[Any]      		=  self.full_loop(use_karras_sigmas=snake_case      )
      a__	:						str      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.2_248      ) < 1E-3





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.full_loop(prediction_type="v_prediction"      )
      a__	:						Tuple      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.1_453      ) < 1E-3





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						int      		=  self.full_loop(prediction_type="v_prediction"					,			use_karras_sigmas=snake_case      )
      a__	:						List[str]      		=  torch.mean(torch.abs(snake_case      )      )

      assert abs(result_mean.item() - 0.0_649      ) < 1E-3





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						str      		=  self.scheduler_classes[0]
      a__	:						int      		=  self.get_scheduler_config(thresholding=snake_case					,			dynamic_thresholding_ratio=0      )
      a__	:						List[Any]      		=  scheduler_class(**snake_case      )

      a__	:						str      		=  10
      a__	:						List[str]      		=  self.dummy_model()
      a__	:						List[str]      		=  self.dummy_sample_deter.half()
      scheduler.set_timesteps(snake_case      )

      for i, t in enumerate(scheduler.timesteps      ):
         a__	:						Union[str, Any]      		=  model(snake_case					,			snake_case      )
         a__	:						str      		=  scheduler.step(snake_case					,			snake_case					,			snake_case      ).prev_sample

      assert sample.dtype == torch.floataa








from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """VanForImageClassification""",
      """VanModel""",
      """VanPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_van import (
        VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
        VanForImageClassification,
        VanModel,
        VanPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			List[str]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

