




"""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     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						BarthezTokenizer
				__UpperCamelCase			  =						BarthezTokenizerFast
				__UpperCamelCase			  =						True
				__UpperCamelCase			  =						True





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							super().setUp()

							lowerCamelCase			      =					BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez""" )
							tokenizer.save_pretrained(self.tmpdirname )
							tokenizer.save_pretrained(self.tmpdirname		, legacy_format=_a )
							lowerCamelCase			      =					tokenizer





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					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 )		, 101_122 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.assertEqual(self.get_tokenizer().vocab_size		, 101_122 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							lowerCamelCase			      =					[0, 57, 3_018, 70_307, 91, 2]

							lowerCamelCase			      =					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 )
							lowerCamelCase			      =					batch.input_ids.tolist()[0]
							self.assertListEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# fmt: off
							lowerCamelCase			      =					{"""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.
							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="""moussaKam/mbarthez"""		, revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""		, sequences=_a		, )









"""simple docstring"""




from collections.abc import Generator



def     a__ (				)						->					Generator[int, None, None]:
			lowerCamelCase      ,   lowerCamelCase			      =					0, 1
			while True:
						lowerCamelCase      ,   lowerCamelCase			      =					b, a + b
						yield b



def     a__ (				snake_case__ = 10_00				)						->					int:
			lowerCamelCase			      =					1
			lowerCamelCase			      =					fibonacci_generator()
			while len(str(next(snake_case__				)				)				) < n:
						answer += 1
			return answer + 1


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))



