
'''simple docstring'''






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		(PNDMScheduler,)
    lowerCAmelCase_					     =		(('num_inference_steps', 50),)



    def 	lowerCamelCase_	(						self   :							Tuple,**__A   :							Any  ):
          _lowerCamelCase					: Optional[Any]		    = {
              "num_train_timesteps": 1_0_0_0,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**__A  )
          return config



    def 	lowerCamelCase_	(						self   :							Dict,__A   :							Tuple=0,**__A   :							int  ):
          _lowerCamelCase					: Union[str, Any]		    = dict(self.forward_default_kwargs  )
          _lowerCamelCase					: Tuple		    = kwargs.pop("num_inference_steps",__A  )
          _lowerCamelCase					: Tuple		    = self.dummy_sample
          _lowerCamelCase					: str		    = 0.1 * sample
          _lowerCamelCase					: str		    = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
                _lowerCamelCase					: List[str]		    = self.get_scheduler_config(**__A  )
                _lowerCamelCase					: int		    = scheduler_class(**__A  )
                scheduler.set_timesteps(__A  )
                # copy over dummy past residuals
                _lowerCamelCase					: Optional[int]		    = dummy_past_residuals[:]

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__A  )
                      _lowerCamelCase					: Optional[Any]		    = scheduler_class.from_pretrained(__A  )
                      new_scheduler.set_timesteps(__A  )
                      # copy over dummy past residuals
                      _lowerCamelCase					: Union[str, Any]		    = dummy_past_residuals[:]

                _lowerCamelCase					: Union[str, Any]		    = scheduler.step_prk(__A,__A,__A,**__A  ).prev_sample
                _lowerCamelCase					: int		    = new_scheduler.step_prk(__A,__A,__A,**__A  ).prev_sample

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

                _lowerCamelCase					: Tuple		    = scheduler.step_plms(__A,__A,__A,**__A  ).prev_sample
                _lowerCamelCase					: int		    = new_scheduler.step_plms(__A,__A,__A,**__A  ).prev_sample

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



    def 	lowerCamelCase_	(						self   :							str  ):
          pass



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							List[Any]=0,**__A   :							Union[str, Any]  ):
          _lowerCamelCase					: Any		    = dict(self.forward_default_kwargs  )
          _lowerCamelCase					: Optional[int]		    = kwargs.pop("num_inference_steps",__A  )
          _lowerCamelCase					: Tuple		    = self.dummy_sample
          _lowerCamelCase					: Union[str, Any]		    = 0.1 * sample
          _lowerCamelCase					: List[str]		    = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
                _lowerCamelCase					: str		    = self.get_scheduler_config()
                _lowerCamelCase					: Dict		    = scheduler_class(**__A  )
                scheduler.set_timesteps(__A  )

                # copy over dummy past residuals (must be after setting timesteps)
                _lowerCamelCase					: str		    = dummy_past_residuals[:]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      _lowerCamelCase					: Any		    = dummy_past_residuals[:]

                _lowerCamelCase					: List[Any]		    = scheduler.step_prk(__A,__A,__A,**__A  ).prev_sample
                _lowerCamelCase					: Union[str, Any]		    = new_scheduler.step_prk(__A,__A,__A,**__A  ).prev_sample

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

                _lowerCamelCase					: Any		    = scheduler.step_plms(__A,__A,__A,**__A  ).prev_sample
                _lowerCamelCase					: Optional[Any]		    = new_scheduler.step_plms(__A,__A,__A,**__A  ).prev_sample

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



    def 	lowerCamelCase_	(						self   :							int,**__A   :							Optional[Any]  ):
          _lowerCamelCase					: str		    = self.scheduler_classes[0]
          _lowerCamelCase					: str		    = self.get_scheduler_config(**__A  )
          _lowerCamelCase					: str		    = scheduler_class(**__A  )

          _lowerCamelCase					: Any		    = 1_0
          _lowerCamelCase					: Optional[Any]		    = self.dummy_model()
          _lowerCamelCase					: Optional[int]		    = self.dummy_sample_deter
          scheduler.set_timesteps(__A  )

          for i, t in enumerate(scheduler.prk_timesteps  ):
                _lowerCamelCase					: Dict		    = model(__A,__A  )
                _lowerCamelCase					: Optional[int]		    = scheduler.step_prk(__A,__A,__A  ).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps  ):
                _lowerCamelCase					: List[Any]		    = model(__A,__A  )
                _lowerCamelCase					: Dict		    = scheduler.step_plms(__A,__A,__A  ).prev_sample

          return sample



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: int		    = dict(self.forward_default_kwargs  )

          _lowerCamelCase					: Optional[Any]		    = kwargs.pop("num_inference_steps",__A  )

          for scheduler_class in self.scheduler_classes:
                _lowerCamelCase					: Optional[Any]		    = self.get_scheduler_config()
                _lowerCamelCase					: List[Any]		    = scheduler_class(**__A  )

                _lowerCamelCase					: Any		    = self.dummy_sample
                _lowerCamelCase					: Union[str, Any]		    = 0.1 * sample

                if num_inference_steps is not None and hasattr(__A,"set_timesteps"  ):
                      scheduler.set_timesteps(__A  )
                elif num_inference_steps is not None and not hasattr(__A,"set_timesteps"  ):
                      _lowerCamelCase					: Tuple		    = num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                _lowerCamelCase					: str		    = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                _lowerCamelCase					: List[Any]		    = dummy_past_residuals[:]

                _lowerCamelCase					: List[Any]		    = scheduler.step_prk(__A,0,__A,**__A  ).prev_sample
                _lowerCamelCase					: Dict		    = scheduler.step_prk(__A,1,__A,**__A  ).prev_sample

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

                _lowerCamelCase					: Dict		    = scheduler.step_plms(__A,0,__A,**__A  ).prev_sample
                _lowerCamelCase					: List[Any]		    = scheduler.step_plms(__A,1,__A,**__A  ).prev_sample

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



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          for timesteps in [1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__A  )



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=__A  )

          _lowerCamelCase					: Tuple		    = self.scheduler_classes[0]
          _lowerCamelCase					: List[str]		    = self.get_scheduler_config(steps_offset=1  )
          _lowerCamelCase					: Optional[int]		    = scheduler_class(**__A  )
          scheduler.set_timesteps(1_0  )
          assert torch.equal(
              scheduler.timesteps,torch.LongTensor(
                  [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]  ),)



    def 	lowerCamelCase_	(						self   :							str  ):
          for beta_start, beta_end in zip([0.0001, 0.001],[0.002, 0.02]  ):
                self.check_over_configs(beta_start=__A,beta_end=__A  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                self.check_over_configs(beta_schedule=__A  )



    def 	lowerCamelCase_	(						self   :							str  ):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=__A  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          for t in [1, 5, 1_0]:
                self.check_over_forward(time_step=__A  )



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          for t, num_inference_steps in zip([1, 5, 1_0],[1_0, 5_0, 1_0_0]  ):
                self.check_over_forward(num_inference_steps=__A  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
          _lowerCamelCase					: List[str]		    = 2_7

          for scheduler_class in self.scheduler_classes:
                _lowerCamelCase					: List[Any]		    = self.dummy_sample
                _lowerCamelCase					: str		    = 0.1 * sample

                _lowerCamelCase					: Dict		    = self.get_scheduler_config()
                _lowerCamelCase					: Any		    = scheduler_class(**__A  )

                scheduler.set_timesteps(__A  )

                # before power of 3 fix, would error on first step, so we only need to do two
                for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
                      _lowerCamelCase					: List[str]		    = scheduler.step_prk(__A,__A,__A  ).prev_sample



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          with self.assertRaises(__A  ):
                _lowerCamelCase					: Optional[int]		    = self.scheduler_classes[0]
                _lowerCamelCase					: Union[str, Any]		    = self.get_scheduler_config()
                _lowerCamelCase					: str		    = scheduler_class(**__A  )

                scheduler.step_plms(self.dummy_sample,1,self.dummy_sample  ).prev_sample



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: str		    = self.full_loop()
          _lowerCamelCase					: str		    = torch.sum(torch.abs(__A  )  )
          _lowerCamelCase					: Any		    = torch.mean(torch.abs(__A  )  )

          assert abs(result_sum.item() - 198.1318  ) < 1e-2
          assert abs(result_mean.item() - 0.2580  ) < 1e-3



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Dict		    = self.full_loop(prediction_type="v_prediction"  )
          _lowerCamelCase					: int		    = torch.sum(torch.abs(__A  )  )
          _lowerCamelCase					: int		    = torch.mean(torch.abs(__A  )  )

          assert abs(result_sum.item() - 67.3986  ) < 1e-2
          assert abs(result_mean.item() - 0.0878  ) < 1e-3



    def 	lowerCamelCase_	(						self   :							Any  ):
          # We specify different beta, so that the first alpha is 0.99
          _lowerCamelCase					: Optional[Any]		    = self.full_loop(set_alpha_to_one=__A,beta_start=0.01  )
          _lowerCamelCase					: str		    = torch.sum(torch.abs(__A  )  )
          _lowerCamelCase					: Dict		    = torch.mean(torch.abs(__A  )  )

          assert abs(result_sum.item() - 230.0399  ) < 1e-2
          assert abs(result_mean.item() - 0.2995  ) < 1e-3



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          # We specify different beta, so that the first alpha is 0.99
          _lowerCamelCase					: Union[str, Any]		    = self.full_loop(set_alpha_to_one=__A,beta_start=0.01  )
          _lowerCamelCase					: Optional[Any]		    = torch.sum(torch.abs(__A  )  )
          _lowerCamelCase					: List[Any]		    = torch.mean(torch.abs(__A  )  )

          assert abs(result_sum.item() - 186.9482  ) < 1e-2
          assert abs(result_mean.item() - 0.2434  ) < 1e-3




from __future__ import annotations



def 		lowerCAmelCase__     (       UpperCamelCase_					:							dict			,							UpperCamelCase_					:							str						)->	set[str]:
    A__			,						A__					=       set(UpperCamelCase_						), [start]

    while stack:
        A__					=       stack.pop()
        explored.add(UpperCamelCase_						)
        # Differences from BFS:
        # 1) pop last element instead of first one
        # 2) add adjacent elements to stack without exploring them
        for adj in reversed(graph[v]						):
            if adj not in explored:
                stack.append(UpperCamelCase_						)
    return explored


_lowercase    	=		{
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(depth_first_search(G, "A"))
