

from typing import TYPE_CHECKING

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


_a:    Any 							=			{"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    Any 							=			["""ViTFeatureExtractor"""]
				_a:    int 							=			["""ViTImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			[
				    """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """ViTForImageClassification""",
				    """ViTForMaskedImageModeling""",
				    """ViTModel""",
				    """ViTPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    Any 							=			[
				    """TFViTForImageClassification""",
				    """TFViTModel""",
				    """TFViTPreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    int 							=			[
				    """FlaxViTForImageClassification""",
				    """FlaxViTModel""",
				    """FlaxViTPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

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


from __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		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       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

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

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

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

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()