

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	list[int],      UpperCamelCase_					:	int)     ->						bool:
   '''simple docstring'''


   __lowercase								=						len(UpperCamelCase_)
   __lowercase								=						[[False] * (required_sum + 1) for _ in range(arr_len + 1)]

   # for each arr value, a sum of zero(0) can be formed by not taking any element
   # hence True/1
   for i in range(arr_len + 1):
      __lowercase								=						True

   # sum is not zero and set is empty then false
   for i in range(1,      required_sum + 1):
      __lowercase								=						False

   for i in range(1,      arr_len + 1):
      for j in range(1,      required_sum + 1):
         if arr[i - 1] > j:
            __lowercase								=						subset[i - 1][j]
         if arr[i - 1] <= j:
            __lowercase								=						subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

   return subset[arr_len][required_sum]


if __name__ == "__main__":
   import doctest

   doctest.testmod()







"""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  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], 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     )     )




