


'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class        __magic_name__  (					lowercase_   ):

     """simple docstring"""






     lowerCamelCase__						=							(DPMSolverMultistepScheduler,)
     lowerCamelCase__						=							(("""num_inference_steps""", 25),)


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       {
              "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",
              "lower_order_final": False,
              "lambda_min_clipped": -float("inf"		),
              "variance_type": None,
          }

          config.update(**lowerCamelCase		)
          return config


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       dict(self.forward_default_kwargs		)
          __A  :					Tuple						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)
          __A  :					List[str]						     =       self.dummy_sample
          __A  :					int						     =       0.1 * sample
          __A  :					List[str]						     =       [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               __A  :					List[str]						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Union[str, Any]						     =       scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)
               # copy over dummy past residuals
               __A  :					Union[str, Any]						     =       dummy_past_residuals[: scheduler.config.solver_order]

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

               __A     ,__A  :					str						     =       sample, sample
               for t in range(lowerCamelCase ,    time_step + scheduler.config.solver_order + 1		):
                    __A  :					List[str]						     =       scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    **lowerCamelCase		).prev_sample
                    __A  :					Dict						     =       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		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0 ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       dict(self.forward_default_kwargs		)
          __A  :					Optional[Any]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)
          __A  :					Optional[Any]						     =       self.dummy_sample
          __A  :					List[Any]						     =       0.1 * sample
          __A  :					List[Any]						     =       [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
               __A  :					Any						     =       self.get_scheduler_config()
               __A  :					int						     =       scheduler_class(**lowerCamelCase		)
               scheduler.set_timesteps(lowerCamelCase		)

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCamelCase		)
                    __A  :					Dict						     =       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)
                    __A  :					int						     =       dummy_past_residuals[: new_scheduler.config.solver_order]

               __A  :					List[str]						     =       scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    **lowerCamelCase		).prev_sample
               __A  :					str						     =       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 ,    lowerCamelCase=None ,    **lowerCamelCase		):


          '''simple docstring'''





          if scheduler is None:
               __A  :					Optional[Any]						     =       self.scheduler_classes[0]
               __A  :					str						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Union[str, Any]						     =       scheduler_class(**lowerCamelCase		)

          __A  :					str						     =       10
          __A  :					int						     =       self.dummy_model()
          __A  :					Any						     =       self.dummy_sample_deter
          scheduler.set_timesteps(lowerCamelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               __A  :					Optional[int]						     =       model(lowerCamelCase ,    lowerCamelCase		)
               __A  :					Optional[int]						     =       scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return sample


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       dict(self.forward_default_kwargs		)

          __A  :					List[str]						     =       kwargs.pop("num_inference_steps" ,    lowerCamelCase		)

          for scheduler_class in self.scheduler_classes:
               __A  :					Optional[Any]						     =       self.get_scheduler_config()
               __A  :					Union[str, Any]						     =       scheduler_class(**lowerCamelCase		)

               __A  :					int						     =       self.dummy_sample
               __A  :					str						     =       0.1 * sample

               if num_inference_steps is not None and hasattr(lowerCamelCase ,    "set_timesteps"		):
                    scheduler.set_timesteps(lowerCamelCase		)
               elif num_inference_steps is not None and not hasattr(lowerCamelCase ,    "set_timesteps"		):
                    __A  :					Optional[int]						     =       num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               __A  :					Tuple						     =       [residual + 0.2, residual + 0.15, residual + 0.10]
               __A  :					Any						     =       dummy_past_residuals[: scheduler.config.solver_order]

               __A  :					Optional[Any]						     =       scheduler.timesteps[5]
               __A  :					Tuple						     =       scheduler.timesteps[6]

               __A  :					Optional[int]						     =       scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    **lowerCamelCase		).prev_sample
               __A  :					List[str]						     =       scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    **lowerCamelCase		).prev_sample

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''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		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                    for order in [1, 2, 3]:
                         for prediction_type in ["epsilon", "sample"]:
                              if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                   if order == 3:
                                        continue
                              else:
                                   self.check_over_configs(
                                       solver_order=lowerCamelCase ,    solver_type=lowerCamelCase ,    prediction_type=lowerCamelCase ,    algorithm_type=lowerCamelCase ,    )
                              __A  :					Dict						     =       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		):


          '''simple docstring'''





          self.check_over_configs(lower_order_final=lowerCamelCase		)
          self.check_over_configs(lower_order_final=lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.full_loop()
          __A  :					List[str]						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.3301		) < 1E-3


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Any						     =       self.full_loop(thresholding=lowerCamelCase ,    dynamic_thresholding_ratio=0.87 ,    sample_max_value=0.5		)
          __A  :					int						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 1.1364		) < 1E-3


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       self.full_loop(prediction_type="v_prediction"		)
          __A  :					int						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2251		) < 1E-3


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.full_loop(prediction_type="v_prediction" ,    use_karras_sigmas=lowerCamelCase		)
          __A  :					Tuple						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.2096		) < 1E-3


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
          __A  :					int						     =       self.full_loop(scheduler=lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.3301		) < 1E-3

          __A  :					List[Any]						     =       DPMSolverSinglestepScheduler.from_config(scheduler.config		)
          __A  :					Dict						     =       UniPCMultistepScheduler.from_config(scheduler.config		)
          __A  :					Any						     =       DEISMultistepScheduler.from_config(scheduler.config		)
          __A  :					Optional[Any]						     =       DPMSolverMultistepScheduler.from_config(scheduler.config		)

          __A  :					int						     =       self.full_loop(scheduler=lowerCamelCase		)
          __A  :					List[str]						     =       torch.mean(torch.abs(lowerCamelCase		)		)

          assert abs(result_mean.item() - 0.3301		) < 1E-3


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Dict						     =       self.scheduler_classes[0]
          __A  :					str						     =       self.get_scheduler_config(thresholding=lowerCamelCase ,    dynamic_thresholding_ratio=0		)
          __A  :					int						     =       scheduler_class(**lowerCamelCase		)

          __A  :					Union[str, Any]						     =       10
          __A  :					Tuple						     =       self.dummy_model()
          __A  :					Tuple						     =       self.dummy_sample_deter.half()
          scheduler.set_timesteps(lowerCamelCase		)

          for i, t in enumerate(scheduler.timesteps		):
               __A  :					Any						     =       model(lowerCamelCase ,    lowerCamelCase		)
               __A  :					str						     =       scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          assert sample.dtype == torch.floataa


     def lowerCAmelCase__     (      self ,    **lowerCamelCase		):


          '''simple docstring'''





          for scheduler_class in self.scheduler_classes:
               __A  :					Any						     =       self.get_scheduler_config(**lowerCamelCase		)
               __A  :					Dict						     =       scheduler_class(**lowerCamelCase		)

               scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
               assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps






'''simple docstring'''


from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase          =     {
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       {node: {node} for node in graph}

     __A  :					List[str]						     =       {node: graph[node][:] for node in graph}

     while len(SCREAMING_SNAKE_CASE       ) > 2:
          # Choose a random edge.
          __A  :					Tuple						     =       random.choice(list(graph_copy.keys()       )       )
          __A  :					Any						     =       random.choice(graph_copy[u]       )

          # Contract edge (u, v) to new node uv
          __A  :					str						     =       u + v
          __A  :					Any						     =       list(set(graph_copy[u] + graph_copy[v]       )       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          __A  :					Any						     =       uv_neighbors
          for neighbor in uv_neighbors:
               graph_copy[neighbor].append(SCREAMING_SNAKE_CASE       )

          __A  :					str						     =       set(contracted_nodes[u].union(contracted_nodes[v]       )       )

          # Remove nodes u and v.
          del graph_copy[u]
          del graph_copy[v]
          for neighbor in uv_neighbors:
               if u in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )
               if v in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )

    # Find cutset.
     __A  :					int						     =       [contracted_nodes[node] for node in graph_copy]
     return {
         (node, neighbor)
         for node in groups[0]
         for neighbor in graph[node]
         if neighbor in groups[1]
     }


if __name__ == "__main__":
      print(partition_graph(TEST_GRAPH))

