






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   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@slow
							def lowerCAmelCase_ (     self:      str						)		->		str:
											snake_case__									=     TFCamembertModel.from_pretrained('jplu/tf-camembert-base'						)

											snake_case__									=     tf.convert_to_tensor(
											    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]       ,	dtype=tf.intaa       ,	)  # J'aime le camembert !"

											snake_case__									=     model(UpperCamelCase						)['last_hidden_state']
											snake_case__									=     tf.TensorShape((1, 10, 7_68)						)
											self.assertEqual(output.shape       ,	UpperCamelCase						)
											# compare the actual values for a slice.
											snake_case__									=     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						)						)












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




