






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 TFXLMRobertaModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@slow
				def 						__a					(					self      ):
											_lowercase							:   str 							=   TFXLMRobertaModel.from_pretrained('jplu/tf-xlm-roberta-base'      )

											_lowercase							:   Tuple 							=   {
											    'input_ids': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]] ,						dtype=tf.intaa      ),  # "My dog is cute"
											    'attention_mask': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] ,						dtype=tf.intaa      ),
											}

											_lowercase							:   Optional[int] 							=   model(_lowerCAmelCase      )['last_hidden_state']
											_lowercase							:   Union[str, Any] 							=   tf.TensorShape((1, 6, 7_6_8)      )
											self.assertEqual(output.shape ,						_lowerCAmelCase      )
											# compare the actual values for a slice.
											_lowercase							:   List[Any] 							=   tf.convert_to_tensor(
											    [
											        [
											            [0.0_68_17_62, 0.10_89_44_51, 0.06_77_25_04],
											            [-0.06_42_36_68, 0.02_36_66_15, 0.04_32_93_44],
											            [-0.06_05_72_95, 0.09_97_41_35, -0.00_07_05_84],
											        ]
											    ] ,						dtype=tf.floataa ,						)

											self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,						expected_slice.numpy() ,						atol=1E-4      )      )








'''simple docstring'''





import re

from filelock import FileLock


try:
					import nltk

					UpperCAmelCase__  			=				True
except (ImportError, ModuleNotFoundError):
					UpperCAmelCase__  			=				False

if NLTK_AVAILABLE:
					with FileLock('''.lock''') as lock:
										nltk.download('''punkt''', quiet=True)






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			re.sub('<n>','',_SCREAMING_SNAKE_CASE )  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE ) )



