"""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  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						BarthezTokenizer
				a			=						BarthezTokenizerFast
				a			=						True
				a			=						True





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											super().setUp()

											_SCREAMING_SNAKE_CASE		:						int			  =    BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""")
											tokenizer.save_pretrained(self.tmpdirname)
											tokenizer.save_pretrained(self.tmpdirname      ,    legacy_format=_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    tokenizer





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    """<pad>"""
											_SCREAMING_SNAKE_CASE		:						Any			  =    1

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





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    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(_A)      ,    1_0_1_1_2_2)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											self.assertEqual(self.get_tokenizer().vocab_size      ,    1_0_1_1_2_2)





				@require_torch
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											_SCREAMING_SNAKE_CASE		:						Any			  =    [0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(
											    _A      ,    max_length=len(_A)      ,    padding=_A      ,    truncation=_A      ,    return_tensors="""pt""")
											self.assertIsInstance(_A      ,    _A)

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





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											if not self.test_rust_tokenizer:
																		return

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.get_tokenizer()
											_SCREAMING_SNAKE_CASE		:						str			  =    self.get_rust_tokenizer()

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

											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.tokenize(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    rust_tokenizer.tokenize(_A)
											self.assertListEqual(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer.encode(_A      ,    add_special_tokens=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    rust_tokenizer.encode(_A      ,    add_special_tokens=_A)
											self.assertListEqual(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.get_rust_tokenizer()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tokenizer.encode(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    rust_tokenizer.encode(_A)
											self.assertListEqual(_A      ,    _A)





				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 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_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 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.
											_SCREAMING_SNAKE_CASE		:						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=_A      ,    model_name="""moussaKam/mbarthez"""      ,    revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""      ,    sequences=_A      ,    )



"""simple docstring"""





from typing import Any

import numpy as np



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    bool:
							return np.array_equal(__SCREAMING_SNAKE_CASE     ,	matrix.conjugate().T  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    v.conjugate().T
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    v_star.dot(__SCREAMING_SNAKE_CASE  )
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray  )
							return (v_star_dot.dot(__SCREAMING_SNAKE_CASE  )) / (v_star.dot(__SCREAMING_SNAKE_CASE  ))



def      lowerCamelCase_()->    None:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    np.array([[1], [2], [3]]  )
							assert is_hermitian(__SCREAMING_SNAKE_CASE  ), F"""{a} is not hermitian."""
							print(rayleigh_quotient(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  )

							_SCREAMING_SNAKE_CASE		:						int			  =    np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]  )
							assert is_hermitian(__SCREAMING_SNAKE_CASE  ), F"""{a} is not hermitian."""
							assert rayleigh_quotient(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) == float(3  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			tests()


