






"""simple docstring"""


from __future__ import annotations




def       snake_case  (A_				:list[int]     ,      A_				:int				):







  '''simple docstring'''

  a					:      list[list[int]]    =       []
  a					:      list[int]    =       []
  a					:      Optional[Any]    =       0
  a					:      List[str]    =       sum(A_				)
  create_state_space_tree(A_     ,      A_     ,      A_     ,      A_     ,      A_     ,      A_				)
  return result




def       snake_case  (A_				:list[int]     ,      A_				:int     ,      A_				:int     ,      A_				:list[int]     ,      A_				:list[list[int]]     ,      A_				:int     ,      ):







  '''simple docstring'''

  if sum(A_				) > max_sum or (remaining_nums_sum + sum(A_				)) < max_sum:
    return
  if sum(A_				) == max_sum:
    result.append(A_				)
    return
  for index in range(A_     ,      len(A_				)				):
    create_state_space_tree(
        A_     ,      A_     ,      index + 1     ,      [*path, nums[index]]     ,      A_     ,      remaining_nums_sum - nums[index]     ,      )
_UpperCamelCase    :   Tuple   			=						[3, 34, 4, 12, 5, 2]
_UpperCamelCase    :   List[Any]   			=						9
_UpperCamelCase    :   List[str]   			=						generate_sum_of_subsets_soln(nums, max_sum)






print(*result)












"""simple docstring"""


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




