





from __future__ import annotations

from collections import Counter
from random import random





class 						_A:


				"""simple docstring"""


				def __init__(			self     ):
									__A	:						Optional[int]       =				{}



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						List[str]       =				{}



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if nodea not in self.connections:
														self.add_node(_A     )
									if nodea not in self.connections:
														self.add_node(_A     )
									__A	:						Union[str, Any]       =				probability



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





				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Optional[int]       =				0
									__A	:						Any       =				random()

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







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      dict[str, int]:
					__A	:						Union[str, Any]       =				MarkovChainGraphUndirectedUnweighted()

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

					__A	:						Optional[int]       =				Counter(graph.get_nodes()  )
					__A	:						List[Any]       =				start

					for _ in range(a  ):
										__A	:						Optional[int]       =				graph.transition(a  )
										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


UpperCAmelCase						:   Any			  =     {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   int			  =     ['''MvpTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   str			  =     [
				    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''MvpForCausalLM''',
				    '''MvpForConditionalGeneration''',
				    '''MvpForQuestionAnswering''',
				    '''MvpForSequenceClassification''',
				    '''MvpModel''',
				    '''MvpPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
				from .tokenization_mvp import MvpTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mvp_fast import MvpTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mvp import (
								    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MvpForCausalLM,
								    MvpForConditionalGeneration,
								    MvpForQuestionAnswering,
								    MvpForSequenceClassification,
								    MvpModel,
								    MvpPreTrainedModel,
								)

else:
				import sys

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




