





'''simple docstring'''




from __future__ import annotations

from collections import Counter
from random import random







class   A_	:




 def __init__(					self       :    List[str]    ):
      _UpperCAmelCase     							=   {}




 def      lowercase	(					self       :    int	,			snake_case_       :    str    ):
      _UpperCAmelCase     							=   {}




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    str	,			snake_case_       :    float    ):
      if nodea not in self.connections:
           self.add_node(snake_case_    )
      if nodea not in self.connections:
           self.add_node(snake_case_    )
      _UpperCAmelCase     							=   probability




 def      lowercase	(					self       :    Tuple    ):
      return list(self.connections    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    str    ):
      _UpperCAmelCase     							=   0
      _UpperCAmelCase     							=   random()

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





def 	UpperCAmelCase_							(					__lowercase							:      str			,     __lowercase							:      list[tuple[str, str, float]]			,     __lowercase							:      int       )		->       dict[str, int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   MarkovChainGraphUndirectedUnweighted()

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

     _UpperCAmelCase     							=   Counter(graph.get_nodes()       )
     _UpperCAmelCase     							=   start

     for _ in range(__lowercase       ):
          _UpperCAmelCase     							=   graph.transition(__lowercase       )
          visited[node] += 1

     return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()





"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__  :  str						     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						pass

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[str]						     =	[
						    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RoCBertForCausalLM',
						    'RoCBertForMaskedLM',
						    'RoCBertForMultipleChoice',
						    'RoCBertForPreTraining',
						    'RoCBertForQuestionAnswering',
						    'RoCBertForSequenceClassification',
						    'RoCBertForTokenClassification',
						    'RoCBertLayer',
						    'RoCBertModel',
						    'RoCBertPreTrainedModel',
						    'load_tf_weights_in_roc_bert',
						]

if TYPE_CHECKING:
						from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
						from .tokenization_roc_bert import RoCBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												raise OptionalDependencyNotAvailable()

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roc_bert import (
												    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoCBertForCausalLM,
												    RoCBertForMaskedLM,
												    RoCBertForMultipleChoice,
												    RoCBertForPreTraining,
												    RoCBertForQuestionAnswering,
												    RoCBertForSequenceClassification,
												    RoCBertForTokenClassification,
												    RoCBertLayer,
												    RoCBertModel,
												    RoCBertPreTrainedModel,
												    load_tf_weights_in_roc_bert,
												)


else:
						import sys

						lowerCAmelCase__  :  str						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



