

"""simple docstring"""


from decimal import Decimal, getcontext
from math import ceil, factorial




def  _lowercase					(    __lowerCAmelCase							)       ->				Dict:

      '''simple docstring'''





      if not isinstance(_lowerCamelCase  ,					_lowerCamelCase							):
            raise TypeError("""Undefined for non-integers"""							)
      elif precision < 1:
            raise ValueError("""Undefined for non-natural numbers"""							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	precision
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	ceil(precision / 14							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	42_6880 * Decimal(1_0005							).sqrt()
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	1
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	1359_1409
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Decimal(_lowerCamelCase							)
      for k in range(1  ,					_lowerCamelCase							):
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	factorial(6 * k							) // (factorial(3 * k							) * factorial(_lowerCamelCase							) ** 3)
            linear_term += 5_4514_0134
            exponential_term *= -26_2537_4126_4076_8000
            partial_sum += Decimal(multinomial_term * linear_term							) / exponential_term
      return str(constant_term / partial_sum							)[:-1]


if __name__ == "__main__":
  a					:Optional[Any]    = 50
  print(f'The first {n} digits of pi is: {pi(n)}')







"""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):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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  )  )




