



'''simple docstring'''

from __future__ import annotations






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int | str]							)      ->						None:







	"""simple docstring"""
	create_state_space_tree(_SCREAMING_SNAKE_CASE							,		[]							,		0							,		[0 for i in range(len(_SCREAMING_SNAKE_CASE							)							)]							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int | str]							,		_SCREAMING_SNAKE_CASE					:						list[int | str]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		)      ->						None:







	"""simple docstring"""

	if index == len(_SCREAMING_SNAKE_CASE							):
		print(_SCREAMING_SNAKE_CASE							)
		return

	for i in range(len(_SCREAMING_SNAKE_CASE							)							):
		if not index_used[i]:
			current_sequence.append(sequence[i]							)
			lowerCAmelCase   =     True
			create_state_space_tree(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		index + 1							,		_SCREAMING_SNAKE_CASE							)
			current_sequence.pop()
			lowerCAmelCase   =     False
UpperCAmelCase     		=   [3, 1, 2, 4]
generate_all_permutations(sequence)

UpperCAmelCase     		=   ["A", "B", "C"]





generate_all_permutations(sequence_a)




'''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				)				)