






"""simple docstring"""


def       snake_case  (A_				:int | float | str				):







  '''simple docstring'''

  try:
    a					:      str    =       float(A_				)
  except ValueError:
    raise ValueError('Please enter a valid number'				)
  a					:      Optional[int]    =       decimal - int(A_				)
  if fractional_part == 0:
    return int(A_				), 1
  else:
    a					:      Union[str, Any]    =       len(str(A_				).split('.'				)[1]				)
    a					:      Optional[int]    =       int(decimal * (1_0**number_of_frac_digits)				)
    a					:      str    =       1_0**number_of_frac_digits
    a					:      str    =       denominator, numerator
    while True:
      a					:      Any    =       dividend % divisor
      if remainder == 0:
        break
      a					:      Dict    =       divisor, remainder
    a					:      str    =       numerator / divisor, denominator / divisor
    return int(A_				), int(A_				)


if __name__ == "__main__":
   print(f'''{decimal_to_fraction(2) = }''')
   print(f'''{decimal_to_fraction(89.0) = }''')
   print(f'''{decimal_to_fraction("67") = }''')
   print(f'''{decimal_to_fraction("45.0") = }''')
   print(f'''{decimal_to_fraction(1.5) = }''')
   print(f'''{decimal_to_fraction("6.25") = }''')
   print(f'''{decimal_to_fraction("78td") = }''')












"""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      )      )




