





from math import factorial



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if successes > trials:
            raise ValueError("""successes must be lower or equal to trials"""	)
      if trials < 0 or successes < 0:
            raise ValueError("""the function is defined for non-negative integers"""	)
      if not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) or not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	):
            raise ValueError("""the function is defined for non-negative integers"""	)
      if not 0 < prob < 1:
            raise ValueError("""prob has to be in range of 1 - 0"""	)
      SCREAMING_SNAKE_CASE		=						(prob**successes) * ((1 - prob) ** (trials - successes))
      # Calculate the binomial coefficient: n! / k!(n-k)!
      SCREAMING_SNAKE_CASE		=						float(factorial(_SCREAMING_SNAKE_CASE	)	)
      coefficient /= factorial(_SCREAMING_SNAKE_CASE	) * factorial(trials - successes	)
      return probability * coefficient


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print("""Probability of 2 successes out of 4 trails""")
      print("""with probability of 0.75 is:""", end=""" """)
      print(binomial_distribution(2, 4, 0.75))




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):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      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 !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      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      )      )




