






'''simple docstring'''




import math







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     int:
		UpperCamelCase__				:					List[str]					=					len(lowerCamelCase_)
		UpperCamelCase__				:					Optional[int]					=					int(math.floor(math.sqrt(lowerCamelCase_)))
		UpperCamelCase__				:					List[str]					=					0
		while arr[min(lowerCamelCase_     ,      lowerCamelCase_) - 1] < x:
				UpperCamelCase__				:					Tuple					=					step
				step += int(math.floor(math.sqrt(lowerCamelCase_)))
				if prev >= n:
						return -1

		while arr[prev] < x:
				UpperCamelCase__				:					int					=					prev + 1
				if prev == min(lowerCamelCase_     ,      lowerCamelCase_):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
		lowerCAmelCase__          =						input('Enter numbers separated by a comma:\n').strip()
		lowerCAmelCase__          =						[int(item) for item in user_input.split(',')]
		lowerCAmelCase__          =						int(input('Enter the number to be searched:\n'))
		lowerCAmelCase__          =						jump_search(arr, x)
		if res == -1:
				print('Number not found!')
		else:
				print(f'''Number {x} is at index {res}''')












'''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       __lowercase					(unittest.TestCase    ):

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

			from datasets import load_dataset

			UpperCamelCase__				:					Optional[Any]					=					load_dataset('nielsr/rvlcdip-demo')

			UpperCamelCase__				:					int					=					dataset['train'][0]['image'].convert('RGB')

			UpperCamelCase__				:					Union[str, Any]					=					image_processor(UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

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

			UpperCamelCase__				:					str					=					torch.Size((1, 16))
			self.assertEqual(logits.shape  ,					UpperCAmelCase_)

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




