



"""simple docstring"""
from __future__ import annotations

from collections import Counter
from random import random
class 							_UpperCAmelCase							:





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self       ,	A		)							->					None:
         _UpperCAmelCase							:  Tuple											=    {}





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					None:
         if nodea not in self.connections:
             self.add_node(A		)
         if nodea not in self.connections:
             self.add_node(A		)
         _UpperCAmelCase							:  Optional[Any]											=    probability





     def  __lowerCAmelCase	(					self		)							->					list[str]:
         return list(self.connections		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					str:
         _UpperCAmelCase							:  Dict											=    0
         _UpperCAmelCase							:  int											=    random()

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





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		list[tuple[str, str, float]]     ,       UpperCamelCase__		:		int							):
    _UpperCAmelCase							:  str											=    MarkovChainGraphUndirectedUnweighted()

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

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

    for _ in range(UpperCamelCase__							):
        _UpperCAmelCase							:  Union[str, Any]											=    graph.transition(UpperCamelCase__							)
        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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    :List[Any]     =					{'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'OPTForCausalLM',
      'OPTModel',
      'OPTPreTrainedModel',
      'OPTForSequenceClassification',
      'OPTForQuestionAnswering',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'FlaxOPTForCausalLM',
      'FlaxOPTModel',
      'FlaxOPTPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_opt import (
        OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OPTForCausalLM,
        OPTForQuestionAnswering,
        OPTForSequenceClassification,
        OPTModel,
        OPTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


