


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 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @slow
       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"       )
             lowerCamelCase__		:				Any    =  AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"       )
             model.to(__lowerCamelCase       )

             from datasets import load_dataset

             lowerCamelCase__		:				Optional[int]    =  load_dataset("nielsr/rvlcdip-demo"       )

             lowerCamelCase__		:				Optional[int]    =  dataset["train"][0]["image"].convert("RGB"       )

             lowerCamelCase__		:				Optional[int]    =  image_processor(__lowerCamelCase					,      return_tensors="pt"       ).to(__lowerCamelCase       )

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__		:				List[Any]    =  model(**__lowerCamelCase       )
                   lowerCamelCase__		:				Dict    =  outputs.logits

             lowerCamelCase__		:				Dict    =  torch.Size((1, 16)       )
             self.assertEqual(logits.shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				List[str]    =  torch.tensor(
                 [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]					,      device=__lowerCamelCase					,      dtype=torch.float					,      )
             self.assertTrue(torch.allclose(logits[0, :3]					,      __lowerCamelCase					,      atol=1E-4       )       )










from __future__ import annotations





def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_):  # noqa: E741
							while r - l > 1:
														snake_case_				=							(l + r) // 2
														if v[m] >= key:
																					snake_case_				=							m
														else:
																					snake_case_				=							m  # noqa: E741
							return r





def 						__UpperCAmelCase			(     a_):
							if len(a_) == 0:
														return 0

							snake_case_				=							[0] * len(a_)
							snake_case_				=							1

							snake_case_				=							v[0]

							for i in range(1							,     len(a_)):
														if v[i] < tail[0]:
																					snake_case_				=							v[i]
														elif v[i] > tail[length - 1]:
																					snake_case_				=							v[i]
																					length += 1
														else:
																					snake_case_				=							v[i]

							return length


if __name__ == "__main__":
	import doctest

	doctest.testmod()





