




from __future__ import annotations







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     0
     UpperCAmelCase_     :				Tuple  =     len(_lowercase ) - 1

     while i < j:
          if nums[i] + nums[j] == target:
               return [i, j]
          elif nums[i] + nums[j] < target:
               UpperCAmelCase_     :				Tuple  =     i + 1
          else:
               UpperCAmelCase_     :				Union[str, Any]  =     j - 1

     return []


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")





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     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

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

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )