





from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst

def 						__magic_name__       (							)						->					List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       9, 14  # noqa: F841
   __SCREAMING_SNAKE_CASE      							=       [
       [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      							=       defaultdict(SCREAMING_SNAKE_CASE_      )
   for nodea, nodea, cost in edges:
      adjancency[nodea].append([nodea, cost]      )
      adjancency[nodea].append([nodea, cost]      )

   __SCREAMING_SNAKE_CASE      							=       mst(SCREAMING_SNAKE_CASE_      )

   __SCREAMING_SNAKE_CASE      							=       [
       [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      							=       tuple(answer[:2]      )
      __SCREAMING_SNAKE_CASE      							=       tuple(edge[::-1]      )
      assert edge in result or reverse in result












'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


a        =						logging.get_logger(__name__)

a        =						{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'camembert'





   def __init__(					self				:			int				,lowerCamelCase				:			List[Any]=3_0522				,lowerCamelCase				:			List[Any]=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			List[str]=12				,lowerCamelCase				:			Optional[Any]=3072				,lowerCamelCase				:			Tuple="gelu"				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=512				,lowerCamelCase				:			Dict=2				,lowerCamelCase				:			Tuple=0.02				,lowerCamelCase				:			List[Any]=1E-1_2				,lowerCamelCase				:			Union[str, Any]=1				,lowerCamelCase				:			Optional[Any]=0				,lowerCamelCase				:			List[Any]=2				,lowerCamelCase				:			List[str]="absolute"				,lowerCamelCase				:			int=True				,lowerCamelCase				:			Any=None				,**lowerCamelCase				:			Optional[Any]				,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       vocab_size
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       type_vocab_size
      __SCREAMING_SNAKE_CASE      							=       initializer_range
      __SCREAMING_SNAKE_CASE      							=       layer_norm_eps
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      __SCREAMING_SNAKE_CASE      							=       use_cache
      __SCREAMING_SNAKE_CASE      							=       classifier_dropout







class __a (   _snake_case			):





   @property
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      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),
          ]    )





