




"""simple docstring"""




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase							:			Optional[int]     							=							get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")






@require_sentencepiece
@require_tokenizers
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						PegasusTokenizer
				__UpperCamelCase			  =						PegasusTokenizerFast
				__UpperCamelCase			  =						True
				__UpperCamelCase			  =						True





				def    _lowerCAmelCase				(       self ):





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

							# We have a SentencePiece fixture for testing
							lowerCamelCase			      =					PegasusTokenizer(_a )
							tokenizer.save_pretrained(self.tmpdirname )





				@cached_property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return PegasusTokenizer.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return ("This is a test", "This is a test")





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					"""</s>"""
							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]		, """<pad>""" )
							self.assertEqual(vocab_keys[1]		, """</s>""" )
							self.assertEqual(vocab_keys[-1]		, """v""" )
							self.assertEqual(len(_a )		, 1_103 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.assertEqual(self.get_tokenizer().vocab_size		, 1_103 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
							lowerCamelCase			      =					self.tokenizer_class.from_pretrained(self.tmpdirname )
							lowerCamelCase			      =					(
							    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
							    """ </s> <pad> <pad> <pad>"""
							)
							lowerCamelCase			      =					rust_tokenizer([raw_input_str]		, return_tensors=_a		, add_special_tokens=_a ).input_ids[0]
							lowerCamelCase			      =					py_tokenizer([raw_input_str]		, return_tensors=_a		, add_special_tokens=_a ).input_ids[0]
							self.assertListEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							lowerCamelCase			      =					"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
							lowerCamelCase			      =					[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
							lowerCamelCase			      =					tokenizer([raw_input_str]		, return_tensors=_a ).input_ids[0]
							self.assertListEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 96_103
							assert tokenizer.pad_token_id == 0
							assert tokenizer.eos_token_id == 1
							assert tokenizer.offset == 103
							assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
							assert tokenizer.unk_token == "<unk>"
							assert tokenizer.model_max_length == 1_024
							lowerCamelCase			      =					"""To ensure a smooth flow of bank resolutions."""
							lowerCamelCase			      =					[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
							lowerCamelCase			      =					tokenizer([raw_input_str]		, return_tensors=_a ).input_ids[0]
							self.assertListEqual(_a		, _a )
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					["""This is going to be way too long.""" * 150, """short example"""]
							lowerCamelCase			      =					["""not super long but more than 5 tokens""", """tiny"""]
							lowerCamelCase			      =					self._large_tokenizer(_a		, padding=_a		, truncation=_a		, return_tensors="""pt""" )
							lowerCamelCase			      =					self._large_tokenizer(
							    text_target=_a		, max_length=5		, padding=_a		, truncation=_a		, return_tensors="""pt""" )

							assert batch.input_ids.shape == (2, 1_024)
							assert batch.attention_mask.shape == (2, 1_024)
							assert targets["input_ids"].shape == (2, 5)
							assert len(_a ) == 2  # input_ids, attention_mask.





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# fmt: off
							lowerCamelCase			      =					{"""input_ids""": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_a		, model_name="""google/bigbird-pegasus-large-arxiv"""		, revision="""ba85d0851d708441f91440d509690f1ab6353415"""		, )






@require_sentencepiece
@require_tokenizers
class     __magic_name__							(       UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						PegasusTokenizer
				__UpperCamelCase			  =						PegasusTokenizerFast
				__UpperCamelCase			  =						True
				__UpperCamelCase			  =						True





				def    _lowerCAmelCase				(       self ):





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

							# We have a SentencePiece fixture for testing
							lowerCamelCase			      =					PegasusTokenizer(_a		, offset=0		, mask_token_sent=_a		, mask_token="""[MASK]""" )
							tokenizer.save_pretrained(self.tmpdirname )





				@cached_property
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return PegasusTokenizer.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return ("This is a test", "This is a test")





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
							lowerCamelCase			      =					self.tokenizer_class.from_pretrained(self.tmpdirname )
							lowerCamelCase			      =					(
							    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
							    """ <pad> <pad> <pad>"""
							)
							lowerCamelCase			      =					rust_tokenizer([raw_input_str]		, return_tensors=_a		, add_special_tokens=_a ).input_ids[0]
							lowerCamelCase			      =					py_tokenizer([raw_input_str]		, return_tensors=_a		, add_special_tokens=_a ).input_ids[0]
							self.assertListEqual(_a		, _a )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					["""This is going to be way too long.""" * 1_000, """short example"""]
							lowerCamelCase			      =					["""not super long but more than 5 tokens""", """tiny"""]
							lowerCamelCase			      =					self._large_tokenizer(_a		, padding=_a		, truncation=_a		, return_tensors="""pt""" )
							lowerCamelCase			      =					self._large_tokenizer(
							    text_target=_a		, max_length=5		, padding=_a		, truncation=_a		, return_tensors="""pt""" )

							assert batch.input_ids.shape == (2, 4_096)
							assert batch.attention_mask.shape == (2, 4_096)
							assert targets["input_ids"].shape == (2, 5)
							assert len(_a ) == 2  # input_ids, attention_mask.





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					(
							    """This is an example string that is used to test the original TF implementation against the HF"""
							    """ implementation"""
							)

							lowerCamelCase			      =					self._large_tokenizer(_a ).input_ids

							self.assertListEqual(
							    _a		, [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]		, )









"""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())))



