"""simple docstring"""






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = PhobertTokenizer
					snake_case__				    = False







					def 		__lowerCAmelCase						(				self   :	str			):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									UpperCAmelCase__             =							['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
									UpperCAmelCase__             =							dict(zip(lowerCamelCase__    ,range(len(lowerCamelCase__			)			)			)			)
									UpperCAmelCase__             =							['#version: 0.2', 'l à</w>']
									UpperCAmelCase__             =							{'unk_token': '<unk>'}

									UpperCAmelCase__             =							os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']			)
									UpperCAmelCase__             =							os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['merges_file']			)

									with open(self.vocab_file    ,'w'    ,encoding='utf-8'			) as fp:
													for token in vocab_tokens:
																	fp.write(f'''{token} {vocab_tokens[token]}\n'''			)
									with open(self.merges_file    ,'w'    ,encoding='utf-8'			) as fp:
													fp.write('\n'.join(lowerCamelCase__			)			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,**lowerCamelCase__   :	List[Any]			):
									kwargs.update(self.special_tokens_map			)
									return PhobertTokenizer.from_pretrained(self.tmpdirname    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							'Tôi là VinAI Research'
									UpperCAmelCase__             =							'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
									return input_text, output_text







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							PhobertTokenizer(self.vocab_file    ,self.merges_file    ,**self.special_tokens_map			)
									UpperCAmelCase__             =							'Tôi là VinAI Research'
									UpperCAmelCase__             =							'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
									UpperCAmelCase__             =							tokenizer.tokenize(lowerCamelCase__			)
									print(lowerCamelCase__			)
									self.assertListEqual(lowerCamelCase__    ,lowerCamelCase__			)

									UpperCAmelCase__             =							tokens + [tokenizer.unk_token]

									UpperCAmelCase__             =							[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__			)    ,lowerCamelCase__			)





'''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						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

