

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__						):
  __lowerCAmelCase       :		Union[str, Any]  	=					(HeunDiscreteScheduler,)
  __lowerCAmelCase       :		Optional[int]  	=					10

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A  ,						beta_end=_A)

  def   lowerCAmelCase__       (   self):
      for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_A)

  def   lowerCAmelCase__       (   self):
      for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_A  ,						clip_sample=_A)

  def   lowerCAmelCase__       (   self):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
          self.check_over_configs(prediction_type=_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

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

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

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

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

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

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

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

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

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

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

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

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

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

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

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

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

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

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

      if str(_A).startswith('cpu'):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      elif str(_A).startswith('mps'):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-2
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A  ,						use_karras_sigmas=_A)

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

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

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

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

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

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

      assert abs(result_sum.item() - 0.0_0_0_1_5) < 1E-2
      assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07) < 1E-2





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  Queue()
    SCREAMING_SNAKE_CASE_													=  [False] * len(_SCREAMING_SNAKE_CASE    )
    SCREAMING_SNAKE_CASE_													=  [-1] * len(_SCREAMING_SNAKE_CASE    )

    def bfs():
        while not queue.empty():
            SCREAMING_SNAKE_CASE_													=  queue.get()
            SCREAMING_SNAKE_CASE_													=  True

            for neighbour in graph[u]:
                if neighbour == u:
                    return False

                if color[neighbour] == -1:
                    SCREAMING_SNAKE_CASE_													=  1 - color[u]
                    queue.put(_SCREAMING_SNAKE_CASE    )

                elif color[neighbour] == color[u]:
                    return False

        return True

    for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
        if not visited[i]:
            queue.put(_SCREAMING_SNAKE_CASE    )
            SCREAMING_SNAKE_CASE_													=  0
            if bfs() is False:
                return False

    return True


if __name__ == "__main__":
       # Adjacency List of graph
       print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))


