
"""simple docstring"""







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 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: Dict            =			BarthezTokenizer
		A_		: List[Any]            =			BarthezTokenizerFast
		A_		: int            =			True
		A_		: Optional[Any]            =			True

		def 							__lowerCamelCase							(   self				):
			super().setUp()

			__lowerCAmelCase    :		Union[str, Any]		    =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'				)
			tokenizer.save_pretrained(self.tmpdirname				)
			tokenizer.save_pretrained(self.tmpdirname ,	legacy_format=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Tuple		    =   tokenizer

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   '<pad>'
			__lowerCAmelCase    :		int		    =   1

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   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(_SCREAMING_SNAKE_CASE				) ,	10_11_22				)

		def 							__lowerCamelCase							(   self				):
			self.assertEqual(self.get_tokenizer().vocab_size ,	10_11_22				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
			__lowerCAmelCase    :		List[Any]		    =   [0, 57, 30_18, 7_03_07, 91, 2]

			__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
			    _SCREAMING_SNAKE_CASE ,	max_length=len(_SCREAMING_SNAKE_CASE				) ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				)
			self.assertIsInstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			self.assertEqual((2, 6) ,	batch.input_ids.shape				)
			self.assertEqual((2, 6) ,	batch.attention_mask.shape				)
			__lowerCAmelCase    :		int		    =   batch.input_ids.tolist()[0]
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			if not self.test_rust_tokenizer:
				return

			__lowerCAmelCase    :		Dict		    =   self.get_tokenizer()
			__lowerCAmelCase    :		int		    =   self.get_rust_tokenizer()

			__lowerCAmelCase    :		Tuple		    =   'I was born in 92000, and this is falsé.'

			__lowerCAmelCase    :		Tuple		    =   tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   tokenizer.encode(_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[int]		    =   rust_tokenizer.encode(_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   self.get_rust_tokenizer()
			__lowerCAmelCase    :		Tuple		    =   tokenizer.encode(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Union[str, Any]		    =   rust_tokenizer.encode(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			# fmt: off
			__lowerCAmelCase    :		Any		    =   {'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 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.
			__lowerCAmelCase    :		Dict		    =   [
			    '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=_SCREAMING_SNAKE_CASE ,	model_name='moussaKam/mbarthez' ,	revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' ,	sequences=_SCREAMING_SNAKE_CASE ,	)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()