





'''simple docstring'''


from __future__ import annotations

from collections import Counter
from random import random






class     lowerCAmelCase__ :

       '''simple docstring'''

       def __init__( self					:				Tuple						):
              UpperCAmelCase  =						{}

       def 			__snake_case      ( self					:				Any       ,     a__					:				str						):
              UpperCAmelCase  =						{}

       def 			__snake_case      ( self					:				Optional[Any]       ,     a__					:				str       ,     a__					:				str       ,     a__					:				float						):
              if nodea not in self.connections:
                     self.add_node(a__						)
              if nodea not in self.connections:
                     self.add_node(a__						)
              UpperCAmelCase  =						probability

       def 			__snake_case      ( self					:				Tuple						):
              return list(self.connections						)



       def 			__snake_case      ( self					:				Dict       ,     a__					:				str						):
              UpperCAmelCase  =						0
              UpperCAmelCase  =						random()

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


def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			list[tuple[str, str, float]]			,	SCREAMING_SNAKE_CASE_ :			int )							->     dict[str, int]:

       """simple docstring"""


       UpperCAmelCase  =						MarkovChainGraphUndirectedUnweighted()

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

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

       for _ in range(SCREAMING_SNAKE_CASE_ ):
              UpperCAmelCase  =						graph.transition(SCREAMING_SNAKE_CASE_ )
              visited[node] += 1

       return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

