
"""simple docstring"""





from __future__ import annotations



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



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		if len(a_				) == 0:
				return 0

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

		__SCREAMING_SNAKE_CASE            = v[0]

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

		return length


if __name__ == "__main__":
							import doctest

							doctest.testmod()












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  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								snake_case_				=							AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								model.to(a					)

								from datasets import load_dataset

								snake_case_				=							load_dataset('nielsr/rvlcdip-demo'					)

								snake_case_				=							dataset['train'][0]['image'].convert('RGB'					)

								snake_case_				=							image_processor(a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.logits

								snake_case_				=							torch.Size((1, 16)					)
								self.assertEqual(logits.shape       ,			a					)

								snake_case_				=							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					)					)





