"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0

							# if input_string is "aba" than new_input_string become "a|b|a"
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    """"""
							_SCREAMING_SNAKE_CASE		:						Dict			  =    """"""

							# append each character + "|" in new_string for range(0, length-1)
							for i in input_string[: len(__SCREAMING_SNAKE_CASE  ) - 1]:
														new_input_string += i + "|"
							# append last character
							new_input_string += input_string[-1]

							# we will store the starting and ending of previous furthest ending palindromic
							# substring
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    0, 0

							# length[i] shows the length of palindromic substring with center i
							_SCREAMING_SNAKE_CASE		:						str			  =    [1 for i in range(len(__SCREAMING_SNAKE_CASE  )  )]

							# for each character in new_string find corresponding palindromic string
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0
							for j in range(len(__SCREAMING_SNAKE_CASE  )  ):
														_SCREAMING_SNAKE_CASE		:						str			  =    1 if j > r else min(length[l + r - j] // 2     ,	r - j + 1  )
														while (
														    j - k >= 0
														    and j + k < len(__SCREAMING_SNAKE_CASE  )
														    and new_input_string[k + j] == new_input_string[j - k]
														):
																					k += 1

														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2 * k - 1

														# does this string is ending after the previously explored end (that is r) ?
														# if yes the update the new r to the last index of this
														if j + k - 1 > r:
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    j - k + 1  # noqa: E741
																					_SCREAMING_SNAKE_CASE		:						str			  =    j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    length[j]
																					_SCREAMING_SNAKE_CASE		:						Any			  =    j

    # create that string
							_SCREAMING_SNAKE_CASE		:						Dict			  =    new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
							for i in s:
														if i != "|":
																					output_string += i

							return output_string


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 		_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))


