
"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			List[Any]	=       (HeunDiscreteScheduler,)
    __lowercase				:			Optional[Any]	=       1_0
    def        lowerCamelCase__						(     self    ,     **snake_case_  ):
      _snake_case  :    Union[str, Any]            = {
          "num_train_timesteps": 11_00,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**snake_case_  )
      return config
    def        lowerCamelCase__						(     self  ):
      for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]    ,     [0.0002, 0.002, 0.02]  ):
        self.check_over_configs(beta_start=snake_case_    ,     beta_end=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for schedule in ["linear", "scaled_linear", "exp"]:
        self.check_over_configs(beta_schedule=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for clip_sample_range in [1.0, 2.0, 3.0]:
        self.check_over_configs(clip_sample_range=snake_case_    ,     clip_sample=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
        self.check_over_configs(prediction_type=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.scheduler_classes[0]
      _snake_case  :    str            = self.get_scheduler_config()
      _snake_case  :    Dict            = scheduler_class(**snake_case_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _snake_case  :    List[str]            = self.dummy_model()
      _snake_case  :    str            = self.dummy_sample_deter * scheduler.init_noise_sigma
      _snake_case  :    Dict            = sample.to(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    List[str]            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Optional[Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Dict            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    str            = output.prev_sample

      _snake_case  :    Optional[Any]            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Tuple            = torch.mean(torch.abs(snake_case_  )  )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.scheduler_classes[0]
      _snake_case  :    Dict            = self.get_scheduler_config(prediction_type="v_prediction"  )
      _snake_case  :    int            = scheduler_class(**snake_case_  )

      scheduler.set_timesteps(self.num_inference_steps  )

      _snake_case  :    List[str]            = self.dummy_model()
      _snake_case  :    Tuple            = self.dummy_sample_deter * scheduler.init_noise_sigma
      _snake_case  :    Tuple            = sample.to(snake_case_  )

      for i, t in enumerate(scheduler.timesteps  ):
        _snake_case  :    Tuple            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Union[str, Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Optional[Any]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    Any            = output.prev_sample

      _snake_case  :    int            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    Dict            = torch.mean(torch.abs(snake_case_  )  )

      if torch_device in ["cpu", "mps"]:
        assert abs(result_sum.item() - 4.69_34E-07  ) < 1E-2
        assert abs(result_mean.item() - 6.11_12E-10  ) < 1E-3
      else:
        # CUDA
        assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.scheduler_classes[0]
      _snake_case  :    int            = self.get_scheduler_config()
      _snake_case  :    Tuple            = scheduler_class(**snake_case_  )

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

      _snake_case  :    Dict            = self.dummy_model()
      _snake_case  :    Dict            = self.dummy_sample_deter.to(snake_case_  ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
        _snake_case  :    Optional[Any]            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Tuple            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    Dict            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    Union[str, Any]            = output.prev_sample

      _snake_case  :    Tuple            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    str            = torch.mean(torch.abs(snake_case_  )  )

      if str(snake_case_  ).startswith("cpu"  ):
        # The following sum varies between 148 and 156 on mps. Why?
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3
      elif str(snake_case_  ).startswith("mps"  ):
        # Larger tolerance on mps
        assert abs(result_mean.item() - 0.0002  ) < 1E-2
      else:
        # CUDA
        assert abs(result_sum.item() - 0.1233  ) < 1E-2
        assert abs(result_mean.item() - 0.0002  ) < 1E-3


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

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

      _snake_case  :    int            = self.dummy_model()
      _snake_case  :    int            = self.dummy_sample_deter.to(snake_case_  ) * scheduler.init_noise_sigma
      _snake_case  :    Tuple            = sample.to(snake_case_  )

      for t in scheduler.timesteps:
        _snake_case  :    Any            = scheduler.scale_model_input(snake_case_    ,     snake_case_  )

        _snake_case  :    Union[str, Any]            = model(snake_case_    ,     snake_case_  )

        _snake_case  :    List[str]            = scheduler.step(snake_case_    ,     snake_case_    ,     snake_case_  )
        _snake_case  :    List[str]            = output.prev_sample

      _snake_case  :    Dict            = torch.sum(torch.abs(snake_case_  )  )
      _snake_case  :    int            = torch.mean(torch.abs(snake_case_  )  )

      assert abs(result_sum.item() - 0.00015  ) < 1E-2
      assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07  ) < 1E-2



"""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
_a   :      List[str]       =				{
    """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 					a__ ( a :					dict[str, list[str]]		):

  """simple docstring"""







  _snake_case  :    List[Any]            = {node: {node} for node in graph}

  _snake_case  :    str            = {node: graph[node][:] for node in graph}

  while len(a		) > 2:
    # Choose a random edge.
    _snake_case  :    str            = random.choice(list(graph_copy.keys()		)		)
    _snake_case  :    Union[str, Any]            = random.choice(graph_copy[u]		)

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

    _snake_case  :    Dict            = 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(a		)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)

    # Find cutset.
  _snake_case  :    List[Any]            = [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))

