



'''simple docstring'''

from __future__ import annotations

from collections import Counter
from random import random

class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     str:
							lowerCAmelCase   =     {}


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     {}


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     None:
							if nodea not in self.connections:
								self.add_node(A_				)
							if nodea not in self.connections:
								self.add_node(A_				)
							lowerCAmelCase   =     probability


						def     __snake_case   ( self				)     ->     list[str]:
							return list(self.connections				)




						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     0
							lowerCAmelCase   =     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"""
	lowerCAmelCase   =     MarkovChainGraphUndirectedUnweighted()

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

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

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

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)