"""simple docstring"""





lowerCAmelCase_     =	65521



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						int			  =    1
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0
							for plain_chr in plain_text:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (a + ord(__SCREAMING_SNAKE_CASE  )) % MOD_ADLER
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (b + a) % MOD_ADLER
							return (b << 16) | 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 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											_SCREAMING_SNAKE_CASE		:						str			  =    AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											model.to(_A)

											from datasets import load_dataset

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""nielsr/rvlcdip-demo""")

											_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""][0]["""image"""].convert("""RGB""")

											_SCREAMING_SNAKE_CASE		:						str			  =    image_processor(_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Any			  =    model(**_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_6))
											self.assertEqual(logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
											    [-0.4_158, -0.4_092, -0.4_347]      ,    device=_A      ,    dtype=torch.float      ,    )
											self.assertTrue(torch.allclose(logits[0, :3]      ,    _A      ,    atol=1e-4))


