'''simple docstring'''




from __future__ import annotations

from collections import Counter
from random import random






class 		__A       :






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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		{}






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              if nodea not in self.connections:
                     self.add_node(UpperCamelCase_      )
              if nodea not in self.connections:
                     self.add_node(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		probability






       def 			_snake_case						(      self      ):
              return list(self.connections      )







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		0
              __UpperCAmelCase  :				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	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       dict[str, int]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		MarkovChainGraphUndirectedUnweighted()

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

       __UpperCAmelCase  :				Union[str, Any]       				=		Counter(graph.get_nodes()						)
       __UpperCAmelCase  :				Tuple       				=		start

       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				str       				=		graph.transition(lowerCamelCase__						)
              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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






