



"""simple docstring"""





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  (DPMSolverSinglestepScheduler,)
       UpperCamelCase		=  (('''num_inference_steps''', 25),)



       def   lowercase__       (							self    :						int	,       **_UpperCAmelCase    :						Tuple					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{
                 "num_train_timesteps": 1000,
                 "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(**_UpperCAmelCase					)
             return config



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						Tuple=0	,       **_UpperCAmelCase    :						List[str]					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							dict(self.forward_default_kwargs					)
             UpperCAmelCase_    				=							kwargs.pop("num_inference_steps"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							self.dummy_sample
             UpperCAmelCase_    				=							0.1 * sample
             UpperCAmelCase_    				=							[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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



       def   lowercase__       (							self    :						Any					)				->		Any:


             '''simple docstring'''




             pass



       def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						List[Any]=0	,       **_UpperCAmelCase    :						Any					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							dict(self.forward_default_kwargs					)
             UpperCAmelCase_    				=							kwargs.pop("num_inference_steps"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							self.dummy_sample
             UpperCAmelCase_    				=							0.1 * sample
             UpperCAmelCase_    				=							[residual + 0.2, residual + 0.15, residual + 0.10]

             for scheduler_class in self.scheduler_classes:
                   UpperCAmelCase_    				=							self.get_scheduler_config()
                   UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)
                   scheduler.set_timesteps(_UpperCAmelCase					)

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

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

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

                   UpperCAmelCase_    				=							scheduler.step(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       **_UpperCAmelCase					).prev_sample
                   UpperCAmelCase_    				=							new_scheduler.step(_UpperCAmelCase	,       _UpperCAmelCase	,       _UpperCAmelCase	,       **_UpperCAmelCase					).prev_sample

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



       def   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						Tuple=None	,       **_UpperCAmelCase    :						int					)				->		Dict:


             '''simple docstring'''




             if scheduler is None:
                   UpperCAmelCase_    				=							self.scheduler_classes[0]
                   UpperCAmelCase_    				=							self.get_scheduler_config(**_UpperCAmelCase					)
                   UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config(**_UpperCAmelCase					)
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							10
             UpperCAmelCase_    				=							self.dummy_model()
             UpperCAmelCase_    				=							self.dummy_sample_deter
             scheduler.set_timesteps(_UpperCAmelCase					)

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

             return sample



       def   lowercase__       (							self    :						Any					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
             UpperCAmelCase_    				=							50
             UpperCAmelCase_    				=							self.dummy_model()
             UpperCAmelCase_    				=							self.dummy_sample_deter
             scheduler.set_timesteps(_UpperCAmelCase					)

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

             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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



       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             for timesteps in [25, 50, 100, 999, 1000]:
                   self.check_over_configs(num_train_timesteps=_UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[int]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()					)
             UpperCAmelCase_    				=							self.full_loop(scheduler=_UpperCAmelCase					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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

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

             UpperCAmelCase_    				=							self.full_loop(scheduler=_UpperCAmelCase					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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



       def   lowercase__       (							self    :						Tuple					)				->		List[Any]:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Tuple					)				->		str:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Union[str, Any]					)				->		Any:


             '''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=_UpperCAmelCase	,       solver_type=_UpperCAmelCase	,       prediction_type=_UpperCAmelCase	,       algorithm_type=_UpperCAmelCase	,       )
                                     UpperCAmelCase_    				=							self.full_loop(
                                         solver_order=_UpperCAmelCase	,       solver_type=_UpperCAmelCase	,       prediction_type=_UpperCAmelCase	,       algorithm_type=_UpperCAmelCase	,       )
                                     assert not torch.isnan(_UpperCAmelCase					).any(), "Samples have nan numbers"



       def   lowercase__       (							self    :						List[Any]					)				->		Tuple:


             '''simple docstring'''




             self.check_over_configs(lower_order_final=_UpperCAmelCase					)
             self.check_over_configs(lower_order_final=_UpperCAmelCase					)



       def   lowercase__       (							self    :						Tuple					)				->		str:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						str					)				->		Dict:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Dict					)				->		Union[str, Any]:


             '''simple docstring'''




             for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                   self.check_over_forward(num_inference_steps=_UpperCAmelCase	,       time_step=0					)



       def   lowercase__       (							self    :						Optional[int]					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.full_loop()
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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



       def   lowercase__       (							self    :						List[str]					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.full_loop(use_karras_sigmas=_UpperCAmelCase					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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



       def   lowercase__       (							self    :						str					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.full_loop(prediction_type="v_prediction"					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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



       def   lowercase__       (							self    :						Optional[Any]					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.full_loop(prediction_type="v_prediction"	,       use_karras_sigmas=_UpperCAmelCase					)
             UpperCAmelCase_    				=							torch.mean(torch.abs(_UpperCAmelCase					)					)

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







       def   lowercase__       (							self    :						Optional[Any]					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.scheduler_classes[0]
             UpperCAmelCase_    				=							self.get_scheduler_config(thresholding=_UpperCAmelCase	,       dynamic_thresholding_ratio=0					)
             UpperCAmelCase_    				=							scheduler_class(**_UpperCAmelCase					)

             UpperCAmelCase_    				=							10
             UpperCAmelCase_    				=							self.dummy_model()
             UpperCAmelCase_    				=							self.dummy_sample_deter.half()
             scheduler.set_timesteps(_UpperCAmelCase					)

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

             assert sample.dtype == torch.floataa









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return []
      UpperCAmelCase_				,					UpperCAmelCase_    				=							min(lowerCAmelCase__   ), max(lowerCAmelCase__   )
      UpperCAmelCase_    				=							int(max_value - min_value   ) + 1
      UpperCAmelCase_    				=							[[] for _ in range(lowerCAmelCase__   )]

      for i in my_list:
            buckets[int(i - min_value   )].append(lowerCAmelCase__   )

      return [v for bucket in buckets for v in sorted(lowerCAmelCase__   )]


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

  testmod()
  assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




