

'''simple docstring'''
from __future__ import annotations

from collections import Counter
from random import random


class        a__  :


   def __init__(			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       {}


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				str      					=       {}


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      if nodea not in self.connections:
         self.add_node(_UpperCamelCase  )
      if nodea not in self.connections:
         self.add_node(_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       probability


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return list(self.connections  )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       0
      _lowercase      :				Dict      					=       random()

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







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     dict[str, int]:
   _lowercase      :				Optional[int]      					=       MarkovChainGraphUndirectedUnweighted()

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

   _lowercase      :				Dict      					=       Counter(graph.get_nodes()					)
   _lowercase      :				List[str]      					=       start

   for _ in range(snake_case					):
      _lowercase      :				Optional[int]      					=       graph.transition(snake_case					)
      visited[node] += 1

   return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






