

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 							A_ (		lowerCAmelCase_					):
     _lowercase			:     Optional[int]								=	(DPMSolverSinglestepScheduler,)
     _lowercase			:     Optional[Any]								=	(('num_inference_steps', 2_5),)


     def   UpperCAmelCase  (							self			:		Any			,  **UpperCAmelCase			:		Any					)							->	List[Any]:
      __lowerCAmelCase:   Optional[int]   	=      {
          'num_train_timesteps': 1_0_0_0,
          'beta_start': 0.0001,
          '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(**__lowerCAmelCase					)
      return config


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

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

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

       __lowerCAmelCase	,							__lowerCAmelCase:   int   	=      sample, sample
       for t in range(__lowerCAmelCase			,  time_step + scheduler.config.solver_order + 1					):
        __lowerCAmelCase:   int   	=      scheduler.step(__lowerCAmelCase			,  __lowerCAmelCase			,  __lowerCAmelCase			,  **__lowerCAmelCase					).prev_sample
        __lowerCAmelCase:   str   	=      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   UpperCAmelCase  (							self			:		Any					)							->	str:
      pass


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

      for scheduler_class in self.scheduler_classes:
       __lowerCAmelCase:   str   	=      self.get_scheduler_config()
       __lowerCAmelCase:   Optional[int]   	=      scheduler_class(**__lowerCAmelCase					)
       scheduler.set_timesteps(__lowerCAmelCase					)

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

       with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(__lowerCAmelCase					)
        __lowerCAmelCase:   Optional[int]   	=      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)
        __lowerCAmelCase:   Optional[int]   	=      dummy_past_residuals[: new_scheduler.config.solver_order]

       __lowerCAmelCase:   str   	=      scheduler.step(__lowerCAmelCase			,  __lowerCAmelCase			,  __lowerCAmelCase			,  **__lowerCAmelCase					).prev_sample
       __lowerCAmelCase:   Union[str, Any]   	=      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   UpperCAmelCase  (							self			:		Dict			,  UpperCAmelCase			:		List[str]=None			,  **UpperCAmelCase			:		Optional[Any]					)							->	Any:
      if scheduler is None:
       __lowerCAmelCase:   Any   	=      self.scheduler_classes[0]
       __lowerCAmelCase:   List[Any]   	=      self.get_scheduler_config(**__lowerCAmelCase					)
       __lowerCAmelCase:   Optional[Any]   	=      scheduler_class(**__lowerCAmelCase					)

      __lowerCAmelCase:   Union[str, Any]   	=      self.scheduler_classes[0]
      __lowerCAmelCase:   int   	=      self.get_scheduler_config(**__lowerCAmelCase					)
      __lowerCAmelCase:   List[Any]   	=      scheduler_class(**__lowerCAmelCase					)

      __lowerCAmelCase:   List[str]   	=      1_0
      __lowerCAmelCase:   Tuple   	=      self.dummy_model()
      __lowerCAmelCase:   List[str]   	=      self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase					)

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

      return sample


     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	List[str]:
      __lowerCAmelCase:   Optional[Any]   	=      DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
      __lowerCAmelCase:   Union[str, Any]   	=      5_0
      __lowerCAmelCase:   Optional[Any]   	=      self.dummy_model()
      __lowerCAmelCase:   Tuple   	=      self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase					)

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

      __lowerCAmelCase:   List[Any]   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.2574					) < 1E-3


     def   UpperCAmelCase  (							self			:		Dict					)							->	Optional[int]:
      for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		List[Any]					)							->	Optional[Any]:
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      __lowerCAmelCase:   Optional[Any]   	=      DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
      __lowerCAmelCase:   List[Any]   	=      self.full_loop(scheduler=__lowerCAmelCase					)
      __lowerCAmelCase:   Any   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.2791					) < 1E-3

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

      __lowerCAmelCase:   List[Any]   	=      self.full_loop(scheduler=__lowerCAmelCase					)
      __lowerCAmelCase:   Tuple   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.2791					) < 1E-3


     def   UpperCAmelCase  (							self			:		Tuple					)							->	Optional[Any]:
      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   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Any:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		Any					)							->	Any:
      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=__lowerCAmelCase			,  solver_type=__lowerCAmelCase			,  prediction_type=__lowerCAmelCase			,  algorithm_type=__lowerCAmelCase			,  )
          __lowerCAmelCase:   List[Any]   	=      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   UpperCAmelCase  (							self			:		Tuple					)							->	int:
      self.check_over_configs(lower_order_final=__lowerCAmelCase					)
      self.check_over_configs(lower_order_final=__lowerCAmelCase					)


     def   UpperCAmelCase  (							self			:		str					)							->	Any:
      self.check_over_configs(lambda_min_clipped=-float('inf'					)					)
      self.check_over_configs(lambda_min_clipped=-5.1					)


     def   UpperCAmelCase  (							self			:		int					)							->	Optional[int]:
      self.check_over_configs(variance_type=__lowerCAmelCase					)
      self.check_over_configs(variance_type='learned_range'					)


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Optional[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=__lowerCAmelCase			,  time_step=0					)


     def   UpperCAmelCase  (							self			:		List[str]					)							->	Any:
      __lowerCAmelCase:   Optional[int]   	=      self.full_loop()
      __lowerCAmelCase:   List[Any]   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.2791					) < 1E-3


     def   UpperCAmelCase  (							self			:		Tuple					)							->	int:
      __lowerCAmelCase:   List[str]   	=      self.full_loop(use_karras_sigmas=__lowerCAmelCase					)
      __lowerCAmelCase:   List[Any]   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.2248					) < 1E-3


     def   UpperCAmelCase  (							self			:		int					)							->	Optional[int]:
      __lowerCAmelCase:   List[Any]   	=      self.full_loop(prediction_type='v_prediction'					)
      __lowerCAmelCase:   str   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.1453					) < 1E-3


     def   UpperCAmelCase  (							self			:		Dict					)							->	Any:
      __lowerCAmelCase:   str   	=      self.full_loop(prediction_type='v_prediction'			,  use_karras_sigmas=__lowerCAmelCase					)
      __lowerCAmelCase:   Optional[int]   	=      torch.mean(torch.abs(__lowerCAmelCase					)					)

      assert abs(result_mean.item() - 0.0649					) < 1E-3


     def   UpperCAmelCase  (							self			:		Dict					)							->	Optional[Any]:
      __lowerCAmelCase:   Tuple   	=      self.scheduler_classes[0]
      __lowerCAmelCase:   str   	=      self.get_scheduler_config(thresholding=__lowerCAmelCase			,  dynamic_thresholding_ratio=0					)
      __lowerCAmelCase:   Any   	=      scheduler_class(**__lowerCAmelCase					)

      __lowerCAmelCase:   Union[str, Any]   	=      1_0
      __lowerCAmelCase:   List[Any]   	=      self.dummy_model()
      __lowerCAmelCase:   List[str]   	=      self.dummy_sample_deter.half()
      scheduler.set_timesteps(__lowerCAmelCase					)

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

      assert sample.dtype == torch.floataa






"""simple docstring"""


UpperCAmelCase__     						=  {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
UpperCAmelCase__     						=  {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 1_2,
    """Pm""": 1_5,
    """Em""": 1_8,
    """Zm""": 2_1,
    """Ym""": 2_4,
}






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					from_type.lower().strip("""s"""						)
  _UpperCAmelCase         =					to_type.lower().strip("""s"""						)

  _UpperCAmelCase         =					UNIT_SYMBOL.get(lowercase      ,lowercase						)
  _UpperCAmelCase         =					UNIT_SYMBOL.get(lowercase      ,lowercase						)

  if from_sanitized not in METRIC_CONVERSION:
    _UpperCAmelCase         =					(
        f'''Invalid \'from_type\' value: {from_type!r}.\n'''
        f'''Conversion abbreviations are: {", ".join(lowercase						)}'''
    )
    raise ValueError(lowercase						)
  if to_sanitized not in METRIC_CONVERSION:
    _UpperCAmelCase         =					(
        f'''Invalid \'to_type\' value: {to_type!r}.\n'''
        f'''Conversion abbreviations are: {", ".join(lowercase						)}'''
    )
    raise ValueError(lowercase						)
  _UpperCAmelCase         =					METRIC_CONVERSION[from_sanitized]
  _UpperCAmelCase         =					METRIC_CONVERSION[to_sanitized]
  _UpperCAmelCase         =					1

  if from_exponent > to_exponent:
    _UpperCAmelCase         =					from_exponent - to_exponent
  else:
    _UpperCAmelCase         =					-(to_exponent - from_exponent)

  return value * pow(10      ,lowercase						)


if __name__ == "__main__":
    from doctest import testmod

    testmod()
