import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        UpperCamelCase						(				__a	,	unittest.TestCase				):
						a__		:Union[str, Any]      =			BarthezTokenizer
						a__		:str      =			BarthezTokenizerFast
						a__		:List[Any]      =			True
						a__		:Union[str, Any]      =			True





						def        A_							(self						)				->				Dict:
												super().setUp()

												UpperCamelCase_		:							Optional[int]  =		BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""						)
												tokenizer.save_pretrained(self.tmpdirname						)
												tokenizer.save_pretrained(self.tmpdirname			,     legacy_format=__UpperCamelCase						)
												UpperCamelCase_		:							str  =		tokenizer





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Tuple  =		"""<pad>"""
												UpperCamelCase_		:							Any  =		1

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase						)			,     __UpperCamelCase						)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase						)			,     __UpperCamelCase						)





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		list(self.get_tokenizer().get_vocab().keys()						)

												self.assertEqual(vocab_keys[0]			,     """<s>"""						)
												self.assertEqual(vocab_keys[1]			,     """<pad>"""						)
												self.assertEqual(vocab_keys[-1]			,     """<mask>"""						)
												self.assertEqual(len(__UpperCamelCase						)			,     101_122						)





						def        A_							(self						)				->				Optional[int]:
												self.assertEqual(self.get_tokenizer().vocab_size			,     101_122						)





						@require_torch
						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							int  =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												UpperCamelCase_		:							List[str]  =		[0, 57, 3_018, 70_307, 91, 2]

												UpperCamelCase_		:							Union[str, Any]  =		self.tokenizer(
												    __UpperCamelCase			,     max_length=len(__UpperCamelCase						)			,     padding=__UpperCamelCase			,     truncation=__UpperCamelCase			,     return_tensors="""pt"""						)
												self.assertIsInstance(__UpperCamelCase			,     __UpperCamelCase						)

												self.assertEqual((2, 6)			,     batch.input_ids.shape						)
												self.assertEqual((2, 6)			,     batch.attention_mask.shape						)
												UpperCamelCase_		:							Tuple  =		batch.input_ids.tolist()[0]
												self.assertListEqual(__UpperCamelCase			,     __UpperCamelCase						)





						def        A_							(self						)				->				List[str]:
												if not self.test_rust_tokenizer:
																		return

												UpperCamelCase_		:							Any  =		self.get_tokenizer()
												UpperCamelCase_		:							Optional[Any]  =		self.get_rust_tokenizer()

												UpperCamelCase_		:							Tuple  =		"""I was born in 92000, and this is falsé."""

												UpperCamelCase_		:							Union[str, Any]  =		tokenizer.tokenize(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		rust_tokenizer.tokenize(__UpperCamelCase						)
												self.assertListEqual(__UpperCamelCase			,     __UpperCamelCase						)

												UpperCamelCase_		:							List[Any]  =		tokenizer.encode(__UpperCamelCase			,     add_special_tokens=__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		rust_tokenizer.encode(__UpperCamelCase			,     add_special_tokens=__UpperCamelCase						)
												self.assertListEqual(__UpperCamelCase			,     __UpperCamelCase						)

												UpperCamelCase_		:							Dict  =		self.get_rust_tokenizer()
												UpperCamelCase_		:							List[str]  =		tokenizer.encode(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		rust_tokenizer.encode(__UpperCamelCase						)
												self.assertListEqual(__UpperCamelCase			,     __UpperCamelCase						)





						@slow
						def        A_							(self						)				->				Dict:
												# fmt: off
												UpperCamelCase_		:							int  =		{"""input_ids""": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
												# fmt: on

												# moussaKam/mbarthez is a french model. So we also use french texts.
												UpperCamelCase_		:							Any  =		[
												    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
												    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
												    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
												    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
												    """telles que la traduction et la synthèse de texte.""",
												]

												self.tokenizer_integration_test_util(
												    expected_encoding=__UpperCamelCase			,     model_name="""moussaKam/mbarthez"""			,     revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""			,     sequences=__UpperCamelCase			,     )







import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.array			):
						return (2 / (1 + np.exp(-2 * vector			))) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()






