


"""simple docstring"""




from math import factorial




class 			__snake_case   :




							def __init__(     self:    Optional[Any]     ,							A_:    Optional[Any]     ,							A_:    Dict   ):
										__lowerCamelCase          =      real
										if isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      [1] * rank
										else:
													__lowerCamelCase          =      rank




							def __repr__(     self:    Union[str, Any]   ):
										return (
										    f'{self.real}+'
										    f'{"+".join(str(A_   )+"E"+str(n+1   )for n,dual in enumerate(self.duals   )   )}'
										)




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.duals.copy()
										while cur[-1] == 0:
													cur.pop(-1   )
										return Dual(self.real     ,							A_   )




							def __add__(     self:    List[str]     ,							A_:    str   ):
										if not isinstance(A_     ,							A_   ):
													return Dual(self.real + other     ,							self.duals   )
										__lowerCamelCase          =      self.duals.copy()
										__lowerCamelCase          =      other.duals.copy()
										if len(A_   ) > len(A_   ):
													o_dual.extend([1] * (len(A_   ) - len(A_   ))   )
										elif len(A_   ) < len(A_   ):
													s_dual.extend([1] * (len(A_   ) - len(A_   ))   )
										__lowerCamelCase          =      []
										for i in range(len(A_   )   ):
													new_duals.append(s_dual[i] + o_dual[i]   )
										return Dual(self.real + other.real     ,							A_   )

							__a					 =       __add__




							def __sub__(     self:    Optional[Any]     ,							A_:    int   ):
										return self + other * -1




							def __mul__(     self:    List[Any]     ,							A_:    Dict   ):
										if not isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      []
													for i in self.duals:
																new_duals.append(i * other   )
													return Dual(self.real * other     ,							A_   )
										__lowerCamelCase          =      [0] * (len(self.duals   ) + len(other.duals   ) + 1)
										for i, item in enumerate(self.duals   ):
													for j, jtem in enumerate(other.duals   ):
																new_duals[i + j + 1] += item * jtem
										for k in range(len(self.duals   )   ):
													new_duals[k] += self.duals[k] * other.real
										for index in range(len(other.duals   )   ):
													new_duals[index] += other.duals[index] * self.real
										return Dual(self.real * other.real     ,							A_   )

							__a					 =       __mul__




							def __truediv__(     self:    List[str]     ,							A_:    List[Any]   ):
										if not isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      []
													for i in self.duals:
																new_duals.append(i / other   )
													return Dual(self.real / other     ,							A_   )
										raise ValueError




							def __floordiv__(     self:    str     ,							A_:    List[Any]   ):
										if not isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      []
													for i in self.duals:
																new_duals.append(i // other   )
													return Dual(self.real // other     ,							A_   )
										raise ValueError




							def __pow__(     self:    List[str]     ,							A_:    int   ):
										if n < 0 or isinstance(A_     ,							A_   ):
													raise ValueError("""power must be a positive integer"""   )
										if n == 0:
													return 1
										if n == 1:
													return self
										__lowerCamelCase          =      self
										for _ in range(n - 1   ):
													x *= self
										return x





def       a_ (	lowercase__ :Tuple,			lowercase__ :Tuple,			lowercase__ :Optional[Any]     ):
			if not callable(lowercase__     ):
						raise ValueError("""differentiate() requires a function as input for func"""     )
			if not isinstance(lowercase__,			(float, int)     ):
						raise ValueError("""differentiate() requires a float as input for position"""     )
			if not isinstance(lowercase__,			lowercase__     ):
						raise ValueError("""differentiate() requires an int as input for order"""     )
			__lowerCamelCase          =      Dual(lowercase__,			1     )
			__lowerCamelCase          =      func(lowercase__     )
			if order == 0:
						return result.real
			return result.duals[order - 1] * factorial(lowercase__     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





					def       a_ (	lowercase__ :Optional[int]     ):
								return y**2 * y**4


					print(differentiate(f, 9, 2))








"""simple docstring"""




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__						: Dict    =				get_tests_dir('fixtures/test_sentencepiece.model')
__magic_name__						: int    =				get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__magic_name__						: Any    =				'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       CamembertTokenizer
							__a					 =       CamembertTokenizerFast
							__a					 =       True
							__a					 =       True




							def   __a   (     self:    List[Any]   ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      """<pad>"""
										__lowerCamelCase          =      1

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




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      list(self.get_tokenizer().get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]     ,							"""<s>NOTUSED"""   )
										self.assertEqual(vocab_keys[1]     ,							"""<pad>"""   )
										self.assertEqual(vocab_keys[-1]     ,							"""<mask>"""   )
										self.assertEqual(len(A_   )     ,							10_04   )




							def   __a   (     self:    Any   ):
										self.assertEqual(self.get_tokenizer().vocab_size     ,							10_05   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      CamembertTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										# <unk> tokens are not the same for `rust` than for `slow`.
										# Because spm gives back raw token instead of `unk` in EncodeAsPieces
										# tokens = tokenizer.tokenize(sequence)
										__lowerCamelCase          =      tokenizer.convert_ids_to_tokens(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )




							def   __a   (     self:    List[str]   ):
										if not self.test_rust_tokenizer:
													return

										__lowerCamelCase          =      self.get_tokenizer()
										__lowerCamelCase          =      self.get_rust_tokenizer()

										__lowerCamelCase          =      """I was born in 92000, and this is falsé."""

										__lowerCamelCase          =      tokenizer.tokenize(A_   )
										__lowerCamelCase          =      rust_tokenizer.tokenize(A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      tokenizer.encode(A_     ,							add_special_tokens=A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_     ,							add_special_tokens=A_   )
										self.assertListEqual(A_     ,							A_   )

										__lowerCamelCase          =      self.get_rust_tokenizer()
										__lowerCamelCase          =      tokenizer.encode(A_   )
										__lowerCamelCase          =      rust_tokenizer.encode(A_   )
										self.assertListEqual(A_     ,							A_   )




							@slow
							def   __a   (     self:    Optional[Any]   ):
										# fmt: off
										__lowerCamelCase          =      {"""input_ids""": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], """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, 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]]}  # noqa: E501
										# fmt: on

										# camembert is a french model. So we also use french texts.
										__lowerCamelCase          =      [
										    """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="""camembert-base"""     ,							revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""     ,							sequences=A_     ,							)




