
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 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@slow
	def 		_lowercase    (			self    :     Optional[int]			)			->	Union[str, Any]:
					lowercase_	    =   TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''			)

					lowercase_	    =   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 !"

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)['''last_hidden_state''']
					lowercase_	    =   tf.TensorShape((1, 1_0, 7_6_8)			)
					self.assertEqual(output.shape			,				SCREAMING_SNAKE_CASE_			)
					# compare the actual values for a slice.
					lowercase_	    =   tf.convert_to_tensor(
					    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,				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			)			)






import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]   )
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]   )
def 							a		(				snake_case__:   int		,  snake_case__:   Dict		,  snake_case__:   Dict   ):


				'''simple docstring'''
				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  snake_case__   )
				lowercase_	    =   datasets.config.IN_MEMORY_MAX_SIZE
				if input_in_memory_max_size == "default":
								assert in_memory_max_size == 0
				else:
								assert in_memory_max_size == input_in_memory_max_size
				if dataset_size and in_memory_max_size:
								lowercase_	    =   dataset_size < in_memory_max_size
				else:
								lowercase_	    =   False
				lowercase_	    =   is_small_dataset(snake_case__   )
				assert result == expected




