






"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				List[str]         =				{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"xmod"
      def __init__(		self  :						str		,			lowercase_  :						Any=30522		,			lowercase_  :						Optional[int]=768		,			lowercase_  :						List[str]=12		,			lowercase_  :						Dict=12		,			lowercase_  :						int=3072		,			lowercase_  :						Any="gelu"		,			lowercase_  :						Tuple=0.1		,			lowercase_  :						List[Any]=0.1		,			lowercase_  :						Optional[Any]=512		,			lowercase_  :						str=2		,			lowercase_  :						List[str]=0.02		,			lowercase_  :						Any=1e-12		,			lowercase_  :						Optional[Any]=1		,			lowercase_  :						List[str]=0		,			lowercase_  :						List[str]=2		,			lowercase_  :						Any="absolute"		,			lowercase_  :						List[Any]=True		,			lowercase_  :						List[str]=None		,			lowercase_  :						Tuple=False		,			lowercase_  :						List[Any]=2		,			lowercase_  :						Optional[Any]=False		,			lowercase_  :						int=True		,			lowercase_  :						Tuple=True		,			lowercase_  :						Union[str, Any]=("en_XX",)		,			lowercase_  :						Optional[Any]=None		,			**lowercase_  :						List[str]		,			):




          '''simple docstring'''


          super().__init__(pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						str							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[str]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						str							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Tuple							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Tuple							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						List[str]							=	layer_norm_eps
          SCREAMING_SNAKE_CASE_   :						int							=	position_embedding_type
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	use_cache
          SCREAMING_SNAKE_CASE_   :						str							=	classifier_dropout
          SCREAMING_SNAKE_CASE_   :						List[str]							=	pre_norm
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	adapter_reduction_factor
          SCREAMING_SNAKE_CASE_   :						List[str]							=	adapter_layer_norm
          SCREAMING_SNAKE_CASE_   :						Any							=	adapter_reuse_layer_norm
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	ln_before_adapter
          SCREAMING_SNAKE_CASE_   :						Any							=	list(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	default_language




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


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










"""simple docstring"""






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	9, 14  # noqa: F841
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    SCREAMING_SNAKE_CASE_   :						Dict							=	defaultdict(__a							)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]							)
        adjancency[nodea].append([nodea, cost]							)

    SCREAMING_SNAKE_CASE_   :						Any							=	mst(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        SCREAMING_SNAKE_CASE_   :						Dict							=	tuple(answer[:2]							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tuple(edge[::-1]							)
        assert edge in result or reverse in result


