


'''simple docstring'''
import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=(DPMSolverSDEScheduler,)
       __UpperCAmelCase					=10






       def   UpperCamelCase						( self			,     **_UpperCamelCase   )->      List[Any]:
            _A				=							{
                'num_train_timesteps': 1100,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'noise_sampler_seed': 0,
            }

            config.update(**_UpperCamelCase   )
            return config






       def   UpperCamelCase						( self   )->      Optional[int]:
            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[Any]:
            for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,     [0.0002, 0.002, 0.02]   ):
                 self.check_over_configs(beta_start=_UpperCamelCase			,     beta_end=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							self.scheduler_classes[0]
            _A				=							self.get_scheduler_config()
            _A				=							scheduler_class(**_UpperCamelCase   )

            scheduler.set_timesteps(self.num_inference_steps   )

            _A				=							self.dummy_model()
            _A				=							self.dummy_sample_deter * scheduler.init_noise_sigma
            _A				=							sample.to(_UpperCamelCase   )

            for i, t in enumerate(scheduler.timesteps   ):
                 _A				=							scheduler.scale_model_input(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							model(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							scheduler.step(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							output.prev_sample

            _A				=							torch.sum(torch.abs(_UpperCamelCase   )   )
            _A				=							torch.mean(torch.abs(_UpperCamelCase   )   )

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.47_8210_4492_1875   ) < 1e-2
                 assert abs(result_mean.item() - 0.2178_7059_6456_5277   ) < 1e-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59_3521_1181_6406   ) < 1e-2
                 assert abs(result_mean.item() - 0.2_2342_9068_9229_9652   ) < 1e-3
            else:
                 assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1e-2
                 assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1e-3






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							self.scheduler_classes[0]
            _A				=							self.get_scheduler_config(prediction_type='v_prediction'   )
            _A				=							scheduler_class(**_UpperCamelCase   )

            scheduler.set_timesteps(self.num_inference_steps   )

            _A				=							self.dummy_model()
            _A				=							self.dummy_sample_deter * scheduler.init_noise_sigma
            _A				=							sample.to(_UpperCamelCase   )

            for i, t in enumerate(scheduler.timesteps   ):
                 _A				=							scheduler.scale_model_input(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							model(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							scheduler.step(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							output.prev_sample

            _A				=							torch.sum(torch.abs(_UpperCamelCase   )   )
            _A				=							torch.mean(torch.abs(_UpperCamelCase   )   )

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 124.77_1492_0043_9453   ) < 1e-2
                 assert abs(result_mean.item() - 0.1_6226_2890_1481_6284   ) < 1e-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 128.1_6633_6059_5703   ) < 1e-2
                 assert abs(result_mean.item() - 0.1_6688_3260_0116_7297   ) < 1e-3
            else:
                 assert abs(result_sum.item() - 119.8_4875_4882_8125   ) < 1e-2
                 assert abs(result_mean.item() - 0.1560_5306_6253_6621   ) < 1e-3






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							self.scheduler_classes[0]
            _A				=							self.get_scheduler_config()
            _A				=							scheduler_class(**_UpperCamelCase   )

            scheduler.set_timesteps(self.num_inference_steps			,     device=_UpperCamelCase   )

            _A				=							self.dummy_model()
            _A				=							self.dummy_sample_deter.to(_UpperCamelCase   ) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                 _A				=							scheduler.scale_model_input(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							model(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							scheduler.step(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							output.prev_sample

            _A				=							torch.sum(torch.abs(_UpperCamelCase   )   )
            _A				=							torch.mean(torch.abs(_UpperCamelCase   )   )

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 167.46_9573_9746_0938   ) < 1e-2
                 assert abs(result_mean.item() - 0.2_1805_9346_0798_2635   ) < 1e-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 171.59_3536_3769_5312   ) < 1e-2
                 assert abs(result_mean.item() - 0.2_2342_9083_8241_5771   ) < 1e-3
            else:
                 assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1e-2
                 assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1e-3






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							self.scheduler_classes[0]
            _A				=							self.get_scheduler_config()
            _A				=							scheduler_class(**_UpperCamelCase			,     use_karras_sigmas=_UpperCamelCase   )

            scheduler.set_timesteps(self.num_inference_steps			,     device=_UpperCamelCase   )

            _A				=							self.dummy_model()
            _A				=							self.dummy_sample_deter.to(_UpperCamelCase   ) * scheduler.init_noise_sigma
            _A				=							sample.to(_UpperCamelCase   )

            for t in scheduler.timesteps:
                 _A				=							scheduler.scale_model_input(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							model(_UpperCamelCase			,     _UpperCamelCase   )

                 _A				=							scheduler.step(_UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )
                 _A				=							output.prev_sample

            _A				=							torch.sum(torch.abs(_UpperCamelCase   )   )
            _A				=							torch.mean(torch.abs(_UpperCamelCase   )   )

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 176.66_9741_3574_2188   ) < 1e-2
                 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1e-2
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 177.63_6535_6445_3125   ) < 1e-2
                 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1e-2
            else:
                 assert abs(result_sum.item() - 170.3_1352_2338_8672   ) < 1e-2
                 assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1e-2









'''simple docstring'''
from __future__ import annotations

def 					lowerCamelCase_ ( __UpperCamelCase					:       dict							,    __UpperCamelCase					:       str		)					->	set[str]:







     """simple docstring"""
     _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


lowerCAmelCase						=					{
    """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"""))





