






'''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


_A     :      List[Any]   					=     get_tests_dir('''fixtures/test_sentencepiece.model''')
_A     :      Tuple   					=     get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

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

@require_sentencepiece
@require_tokenizers
class _lowercase     (    _lowercase    ,				unittest.TestCase		):
							a    				=	CamembertTokenizer
							a    				=	CamembertTokenizerFast
							a    				=	True
							a    				=	True




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														super().setUp()

														# We have a SentencePiece fixture for testing
														lowerCamelCase__ :     List[str]	  =	CamembertTokenizer(UpperCamelCase__					)
														tokenizer.save_pretrained(self.tmpdirname					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[Any]	  =	"""<pad>"""
														lowerCamelCase__ :     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 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[Any]	  =	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(UpperCamelCase__					)      ,							1_004					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														self.assertEqual(self.get_tokenizer().vocab_size      ,							1_005					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Dict	  =	CamembertTokenizer(UpperCamelCase__					)
														tokenizer.save_pretrained(self.tmpdirname					)
														lowerCamelCase__ :     Tuple	  =	CamembertTokenizerFast.from_pretrained(self.tmpdirname					)

														lowerCamelCase__ :     Any	  =	"""I was born in 92000, and this is falsé."""

														lowerCamelCase__ :     Tuple	  =	tokenizer.encode(UpperCamelCase__					)
														lowerCamelCase__ :     Any	  =	rust_tokenizer.encode(UpperCamelCase__					)
														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)

														lowerCamelCase__ :     int	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
														lowerCamelCase__ :     List[Any]	  =	rust_tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)

														# <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__ :     Union[str, Any]	  =	tokenizer.convert_ids_to_tokens(UpperCamelCase__					)
														lowerCamelCase__ :     Union[str, Any]	  =	rust_tokenizer.tokenize(UpperCamelCase__					)
														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    str					):
														if not self.test_rust_tokenizer:
																					return

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

														lowerCamelCase__ :     int	  =	"""I was born in 92000, and this is falsé."""

														lowerCamelCase__ :     Any	  =	tokenizer.tokenize(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	rust_tokenizer.tokenize(UpperCamelCase__					)
														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)

														lowerCamelCase__ :     List[Any]	  =	tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
														lowerCamelCase__ :     Optional[int]	  =	rust_tokenizer.encode(UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__					)
														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)

														lowerCamelCase__ :     Union[str, Any]	  =	self.get_rust_tokenizer()
														lowerCamelCase__ :     str	  =	tokenizer.encode(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	rust_tokenizer.encode(UpperCamelCase__					)
														self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__					)




							@slow
							def 		lowerCamelCase_    (							self:    str					):
														# fmt: off
														lowerCamelCase__ :     Any	  =	{"""input_ids""": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 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__ :     Optional[int]	  =	[
														    """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="""camembert-base"""      ,							revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""      ,							sequences=UpperCamelCase__      ,							)









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

