'''simple docstring'''





import math





def      lowerCAmelCase_    ( snake_case_	:			list ,   snake_case_	:			int      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       len(snake_case_      )
     UpperCAmelCase_	   =       int(math.floor(math.sqrt(snake_case_      )      )      )
     UpperCAmelCase_	   =       0
     while arr[min(snake_case_ ,   snake_case_      ) - 1] < x:
          UpperCAmelCase_	   =       step
          step += int(math.floor(math.sqrt(snake_case_      )      )      )
          if prev >= n:
               return -1

     while arr[prev] < x:
          UpperCAmelCase_	   =       prev + 1
          if prev == min(snake_case_ ,   snake_case_      ):
               return -1
     if arr[prev] == x:
          return prev
     return -1


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





'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,      dtype=tf.intaa       ,      )  # J'aime le camembert !"

         UpperCAmelCase_	   =       model(__a			)["last_hidden_state"]
         UpperCAmelCase_	   =       tf.TensorShape((1, 10, 768)			)
         self.assertEqual(output.shape       ,      __a			)
         # compare the actual values for a slice.
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      dtype=tf.floataa       ,      )
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,      expected_slice.numpy()       ,      atol=1E-4			)			)




