



'''simple docstring'''

from collections.abc import Iterable
from typing import Generic, TypeVar

UpperCAmelCase     		=   TypeVar('_T')

class __snake_case(				Generic[_T] ):


						'''simple docstring'''



						def __init__( self     ,	A_ = None				)     ->     None:
							lowerCAmelCase   =     list(iterable or []				)
							lowerCAmelCase   =     []


						def __len__( self				)     ->     int:
							return len(self._stacka				) + len(self._stacka				)


						def __repr__( self				)     ->     str:
							return f'Queue({tuple(self._stacka[::-1] + self._stacka				)})'


						def     __snake_case   ( self     ,	A_				)     ->     None:
							self._stacka.append(A_				)




						def     __snake_case   ( self				)     ->     _T:
							lowerCAmelCase   =     self._stacka.pop
							lowerCAmelCase   =     self._stacka.append

							if not self._stacka:
								while self._stacka:
									stacka_append(stacka_pop()				)

							if not self._stacka:
								raise IndexError("""Queue is empty"""				)
							return self._stacka.pop()


if __name__ == "__main__":
				from doctest import testmod

				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				)				)