



from __future__ import annotations

from collections import Counter
from random import random







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Optional[int])				->				List[str]:

    """simple docstring"""







    _snake_case    :  int					=					{}

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			str)				->				None:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					{}

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			float)				->				None:

    """simple docstring"""







    if nodea not in self.connections:
     self.add_node(lowerCAmelCase)
    if nodea not in self.connections:
     self.add_node(lowerCAmelCase)
    _snake_case    :  Optional[int]					=					probability

   def      UpperCamelCase_  (					self							:			Any)				->				list[str]:

    """simple docstring"""







    return list(self.connections)


   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			str)				->				str:

    """simple docstring"""







    _snake_case    :  Any					=					0
    _snake_case    :  List[str]					=					random()

    for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
      return dest
    return ""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	list[tuple[str, str, float]]				,       SCREAMING_SNAKE_CASE__       :	int				)  ->     dict[str, int]:
 _snake_case    :  Any					=					MarkovChainGraphUndirectedUnweighted()

 for nodea, nodea, probability in transitions:
  graph.add_transition_probability(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					Counter(graph.get_nodes()				)
 _snake_case    :  Dict					=					start

 for _ in range(SCREAMING_SNAKE_CASE__				):
  _snake_case    :  str					=					graph.transition(SCREAMING_SNAKE_CASE__				)
  visited[node] += 1

 return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

  a__    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
