

import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_a:    Dict 							=			logging.get_logger(__name__)

class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						Optional[Any]		,	*lowerCAmelCase	:						int		,	**lowerCAmelCase	:						str							):


		'''simple docstring'''







		warnings.warn(
		    "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
		    " use DonutImageProcessor instead."		,	lowerCAmelCase		,	)
		super().__init__(*lowerCAmelCase		,	**lowerCAmelCase							)


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()