


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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



  def        lowerCAmelCase__	(  self							:							Tuple     ,					**_lowerCAmelCase							:							Optional[int]	) ->			int:




     """simple docstring"""
     snake_case_            =       {
         "num_train_timesteps": 1_0_0_0,
         "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(**_lowerCAmelCase	)
     return config



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Optional[Any]=0     ,					**_lowerCAmelCase							:							Optional[int]	) ->			Any:




     """simple docstring"""
     snake_case_            =       dict(self.forward_default_kwargs	)
     snake_case_            =       kwargs.pop("num_inference_steps"     ,					_lowerCAmelCase	)
     snake_case_            =       self.dummy_sample
     snake_case_            =       0.1 * sample
     snake_case_            =       [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

        snake_case_     ,     snake_case_            =       sample, sample
        for t in range(_lowerCAmelCase     ,					time_step + scheduler.config.solver_order + 1	):
           snake_case_            =       scheduler.step(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	).prev_sample
           snake_case_            =       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							:							Union[str, Any]	) ->			List[str]:




     """simple docstring"""
     pass



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							int=0     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       dict(self.forward_default_kwargs	)
     snake_case_            =       kwargs.pop("num_inference_steps"     ,					_lowerCAmelCase	)
     snake_case_            =       self.dummy_sample
     snake_case_            =       0.1 * sample
     snake_case_            =       [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
        snake_case_            =       self.get_scheduler_config()
        snake_case_            =       scheduler_class(**_lowerCAmelCase	)
        scheduler.set_timesteps(_lowerCAmelCase	)

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

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_lowerCAmelCase	)
           snake_case_            =       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)
           snake_case_            =       dummy_past_residuals[: new_scheduler.config.solver_order]

        snake_case_            =       scheduler.step(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	).prev_sample
        snake_case_            =       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     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							Any	) ->			List[str]:




     """simple docstring"""
     if scheduler is None:
        snake_case_            =       self.scheduler_classes[0]
        snake_case_            =       self.get_scheduler_config(**_lowerCAmelCase	)
        snake_case_            =       scheduler_class(**_lowerCAmelCase	)

     snake_case_            =       self.scheduler_classes[0]
     snake_case_            =       self.get_scheduler_config(**_lowerCAmelCase	)
     snake_case_            =       scheduler_class(**_lowerCAmelCase	)

     snake_case_            =       1_0
     snake_case_            =       self.dummy_model()
     snake_case_            =       self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase	)

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

     return sample



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




     """simple docstring"""
     snake_case_            =       DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
     snake_case_            =       5_0
     snake_case_            =       self.dummy_model()
     snake_case_            =       self.dummy_sample_deter
     scheduler.set_timesteps(_lowerCAmelCase	)

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

     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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



  def        lowerCAmelCase__	(  self							:							str	) ->			str:




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			List[str]:




     """simple docstring"""
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     snake_case_            =       DPMSolverSinglestepScheduler(**self.get_scheduler_config()	)
     snake_case_            =       self.full_loop(scheduler=_lowerCAmelCase	)
     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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

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

     snake_case_            =       self.full_loop(scheduler=_lowerCAmelCase	)
     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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



  def        lowerCAmelCase__	(  self							:							Any	) ->			Any:




     """simple docstring"""
     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[str]:




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



  def        lowerCAmelCase__	(  self							:							Dict	) ->			List[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=_lowerCAmelCase     ,					solver_type=_lowerCAmelCase     ,					prediction_type=_lowerCAmelCase     ,					algorithm_type=_lowerCAmelCase     ,					)
                 snake_case_            =       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							:							str	) ->			int:




     """simple docstring"""
     self.check_over_configs(lower_order_final=_lowerCAmelCase	)
     self.check_over_configs(lower_order_final=_lowerCAmelCase	)



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




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



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     self.check_over_configs(variance_type=_lowerCAmelCase	)
     self.check_over_configs(variance_type="learned_range"	)



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




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							str	) ->			str:




     """simple docstring"""
     snake_case_            =       self.full_loop()
     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.full_loop(use_karras_sigmas=_lowerCAmelCase	)
     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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



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




     """simple docstring"""
     snake_case_            =       self.full_loop(prediction_type="v_prediction"	)
     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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



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




     """simple docstring"""
     snake_case_            =       self.full_loop(prediction_type="v_prediction"     ,					use_karras_sigmas=_lowerCAmelCase	)
     snake_case_            =       torch.mean(torch.abs(_lowerCAmelCase	)	)

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



  def        lowerCAmelCase__	(  self							:							Any	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.scheduler_classes[0]
     snake_case_            =       self.get_scheduler_config(thresholding=_lowerCAmelCase     ,					dynamic_thresholding_ratio=0	)
     snake_case_            =       scheduler_class(**_lowerCAmelCase	)

     snake_case_            =       1_0
     snake_case_            =       self.dummy_model()
     snake_case_            =       self.dummy_sample_deter.half()
     scheduler.set_timesteps(_lowerCAmelCase	)

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

     assert sample.dtype == torch.floataa








def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->int:
   '''simple docstring'''






   return int((input_a, input_a).count(0				) == 0				)



def       _lowerCAmelCase    (				)->None:
   '''simple docstring'''






   assert and_gate(0		,    0				) == 0
   assert and_gate(0		,    1				) == 0
   assert and_gate(1		,    0				) == 0
   assert and_gate(1		,    1				) == 1


if __name__ == "__main__":
   test_and_gate()
   print(and_gate(1, 0))
   print(and_gate(0, 0))
   print(and_gate(0, 1))
   print(and_gate(1, 1))




