
from __future__ import annotations

from collections import Counter
from random import random





class     lowerCamelCase      :







	def __init__(     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {}







	def  snake_case__     (     self      :Tuple   ,     lowercase      :str			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {}







	def  snake_case__     (     self      :List[str]   ,     lowercase      :str   ,     lowercase      :str   ,     lowercase      :float			)		->      None:







				"""simple docstring"""






				if nodea not in self.connections:
							self.add_node(lowercase			)
				if nodea not in self.connections:
							self.add_node(lowercase			)
				SCREAMING_SNAKE_CASE     						= probability







	def  snake_case__     (     self      :int			)		->      list[str]:







				"""simple docstring"""






				return list(self.connections			)







	def  snake_case__     (     self      :Dict   ,     lowercase      :str			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= 0
				SCREAMING_SNAKE_CASE     						= random()

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



def    a      ( a		,						a		,						a	)     ->dict[str, int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= MarkovChainGraphUndirectedUnweighted()

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

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

			for _ in range(a	):
						SCREAMING_SNAKE_CASE     						= 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


__lowerCAmelCase					   =       {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BloomTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BloomForCausalLM',
							    'BloomModel',
							    'BloomPreTrainedModel',
							    'BloomForSequenceClassification',
							    'BloomForTokenClassification',
							    'BloomForQuestionAnswering',
							]

if TYPE_CHECKING:
							from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bloom_fast import BloomTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bloom import (
														    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BloomForCausalLM,
														    BloomForQuestionAnswering,
														    BloomForSequenceClassification,
														    BloomForTokenClassification,
														    BloomModel,
														    BloomPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)