
"""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):

		@slow
		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[str]									=      TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )

			__lowerCAmelCase			:						Optional[Any]									=      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 !"

			__lowerCAmelCase			:						Optional[int]									=      model(_SCREAMING_SNAKE_CASE )['last_hidden_state']
			__lowerCAmelCase			:						Optional[Any]									=      tf.TensorShape((1, 10, 7_68) )
			self.assertEqual(output.shape       ,		_SCREAMING_SNAKE_CASE )
			# compare the actual values for a slice.
			__lowerCAmelCase			:						Optional[Any]									=      tf.convert_to_tensor(
			    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]       ,		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 ) )
"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = False   )	->str:

  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     Optional[int]										=	F'Expected string as input, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
    a__:     List[str]										=	F'Expected boolean as use_pascal parameter, found {type(_SCREAMING_SNAKE_CASE   )}'
    raise ValueError(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	input_str.split('_'   )

  a__:     List[str]										=	0 if use_pascal else 1

  a__:     List[str]										=	words[start_index:]

  a__:     List[str]										=	[word[0].upper() + word[1:] for word in words_to_capitalize]

  a__:     List[str]										=	'' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]   )


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

 testmod()


