

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__					: Any								=      logging.get_logger(__name__)

UpperCamelCase__					: Optional[Any]								=      {
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class  __snake_case					(      snake_case__						):
  __lowerCAmelCase       :		str  	=					'rembert'

  def __init__(   self  ,						_A=250300  ,						_A=1152  ,						_A=32  ,						_A=18  ,						_A=256  ,						_A=1664  ,						_A=4608  ,						_A="gelu"  ,						_A=0.0  ,						_A=0.0  ,						_A=0.1  ,						_A=512  ,						_A=2  ,						_A=0.0_2  ,						_A=1E-12  ,						_A=True  ,						_A=0  ,						_A=312  ,						_A=313  ,						**_A  ,						):
      super().__init__(pad_token_id=_A  ,						bos_token_id=_A  ,						eos_token_id=_A  ,						**_A)

      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  input_embedding_size
      SCREAMING_SNAKE_CASE_													=  output_embedding_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  hidden_dropout_prob
      SCREAMING_SNAKE_CASE_													=  attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_													=  classifier_dropout_prob
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  type_vocab_size
      SCREAMING_SNAKE_CASE_													=  layer_norm_eps
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  False






class  __snake_case					(      snake_case__						):

  @property
  def   lowerCAmelCase__       (   self):
      if self.task == "multiple-choice":
          SCREAMING_SNAKE_CASE_													=  {0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
          SCREAMING_SNAKE_CASE_													=  {0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('token_type_ids', dynamic_axis),
          ])


  @property
  def   lowerCAmelCase__       (   self):
      return 1E-4





# 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]}))


