

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	int | float | str)     ->						tuple[int, int]:
   '''simple docstring'''


   try:
      __lowercase								=						float(UpperCamelCase_)
   except ValueError:
      raise ValueError("Please enter a valid number")
   __lowercase								=						decimal - int(UpperCamelCase_)
   if fractional_part == 0:
      return int(UpperCamelCase_), 1
   else:
      __lowercase								=						len(str(UpperCamelCase_).split(".")[1])
      __lowercase								=						int(decimal * (10**number_of_frac_digits))
      __lowercase								=						10**number_of_frac_digits
      __lowercase		,__lowercase								=						denominator, numerator
      while True:
         __lowercase								=						dividend % divisor
         if remainder == 0:
            break
         __lowercase		,__lowercase								=						divisor, remainder
      __lowercase		,__lowercase								=						numerator / divisor, denominator / divisor
      return int(UpperCamelCase_), int(UpperCamelCase_)


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"""





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     )     )




