

'''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   UpperCamelCase__    (   unittest.TestCase   ):


		"""simple docstring"""

		@slow
		def       A_       ( self			):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]   					=   TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

							UpperCAmelCase		:							Tuple   					=   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 !"

							UpperCAmelCase		:							Optional[int]   					=   model(lowerCAmelCase__			)["last_hidden_state"]
							UpperCAmelCase		:							Optional[Any]   					=   tf.TensorShape((1, 1_0, 7_6_8)			)
							self.assertEqual(output.shape    ,			lowerCAmelCase__			)
							# compare the actual values for a slice.
							UpperCAmelCase		:							str   					=   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			)			)



def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = False    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       f"Expected string as input, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )
						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase    )}"
												raise ValueError(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: Tuple  		=       input_str.split("_"    )

						SCREAMING_SNAKE_CASE_: str  		=       0 if use_pascal else 1

						SCREAMING_SNAKE_CASE_: int  		=       words[start_index:]

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

						SCREAMING_SNAKE_CASE_: List[Any]  		=       "" if use_pascal else words[0]

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


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

						testmod()






