


"""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   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[str, Any]             =    (DPMSolverSDEScheduler,)
    a_    :				Dict             =    10
    def      _UpperCamelCase       ( self							:					str							,					**SCREAMING_SNAKE_CASE__							:					Optional[int]				):
          lowerCamelCase__           =		{
              'num_train_timesteps': 11_00,
              'beta_start': 0.00_01,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'noise_sampler_seed': 0,
          }

          config.update(**SCREAMING_SNAKE_CASE__				)
          return config
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          for timesteps in [10, 50, 1_00, 10_00]:
                self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					Any				):
          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,					[0.00_02, 0.0_02, 0.02]				):
                self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__							,					beta_end=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					str				):
          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					int				):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config()
          lowerCamelCase__           =		scheduler_class(**SCREAMING_SNAKE_CASE__				)

          scheduler.set_timesteps(self.num_inference_steps				)

          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCamelCase__           =		sample.to(SCREAMING_SNAKE_CASE__				)

          for i, t in enumerate(scheduler.timesteps				):
                lowerCamelCase__           =		scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		output.prev_sample

          lowerCamelCase__           =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75				) < 1e-2
                assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77				) < 1e-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06				) < 1e-2
                assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52				) < 1e-3
          else:
                assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62				) < 1e-2
                assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26				) < 1e-3
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config(prediction_type='v_prediction'				)
          lowerCamelCase__           =		scheduler_class(**SCREAMING_SNAKE_CASE__				)

          scheduler.set_timesteps(self.num_inference_steps				)

          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter * scheduler.init_noise_sigma
          lowerCamelCase__           =		sample.to(SCREAMING_SNAKE_CASE__				)

          for i, t in enumerate(scheduler.timesteps				):
                lowerCamelCase__           =		scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		output.prev_sample

          lowerCamelCase__           =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53				) < 1e-2
                assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84				) < 1e-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03				) < 1e-2
                assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97				) < 1e-3
          else:
                assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25				) < 1e-2
                assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21				) < 1e-3
    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config()
          lowerCamelCase__           =		scheduler_class(**SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__				) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                lowerCamelCase__           =		scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		output.prev_sample

          lowerCamelCase__           =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38				) < 1e-2
                assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35				) < 1e-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12				) < 1e-2
                assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71				) < 1e-3
          else:
                assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62				) < 1e-2
                assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26				) < 1e-3


    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.scheduler_classes[0]
          lowerCamelCase__           =		self.get_scheduler_config()
          lowerCamelCase__           =		scheduler_class(**SCREAMING_SNAKE_CASE__							,					use_karras_sigmas=SCREAMING_SNAKE_CASE__				)

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

          lowerCamelCase__           =		self.dummy_model()
          lowerCamelCase__           =		self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__				) * scheduler.init_noise_sigma
          lowerCamelCase__           =		sample.to(SCREAMING_SNAKE_CASE__				)

          for t in scheduler.timesteps:
                lowerCamelCase__           =		scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		model(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		scheduler.step(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		output.prev_sample

          lowerCamelCase__           =		torch.sum(torch.abs(SCREAMING_SNAKE_CASE__				)				)
          lowerCamelCase__           =		torch.mean(torch.abs(SCREAMING_SNAKE_CASE__				)				)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88				) < 1e-2
                assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11				) < 1e-2
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25				) < 1e-2
                assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11				) < 1e-2
          else:
                assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72				) < 1e-2
                assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11				) < 1e-2







"""simple docstring"""


def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     str		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[]
      queue.append(_a							)
      lowerCamelCase__           =		True

      while queue:
            lowerCamelCase__           =		queue.pop(0							)
            for ind in range(len(graph[u]							)							):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a							)
                        lowerCamelCase__           =		True
                        lowerCamelCase__           =		u

      return visited[t]



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[-1] * (len(_a							))
      lowerCamelCase__           =		0
      while bfs(_a		,   _a		,   _a		,   _a							):
            lowerCamelCase__           =		float('Inf'							)
            lowerCamelCase__           =		sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase__           =		min(_a		,   graph[parent[s]][s]							)
                  lowerCamelCase__           =		parent[s]

            max_flow += path_flow
            lowerCamelCase__           =		sink

            while v != source:
                  lowerCamelCase__           =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase__           =		parent[v]
      return max_flow


_snake_case          =							[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

_snake_case      ,					_snake_case          =							0, 5






print(ford_fulkerson(graph, source, sink))



