






"""simple docstring"""




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : str    =      BartTokenizer
 UpperCamelCase      : Union[str, Any]    =      BartTokenizerFast
 UpperCamelCase      : int    =      True
 UpperCamelCase      : Union[str, Any]    =      filter_roberta_detectors


 def _lowercase			(  self	:		Dict							)      -> Dict:
   super().setUp()
   _a						: Tuple    =			[
       """l""",
       """o""",
       """w""",
       """e""",
       """r""",
       """s""",
       """t""",
       """i""",
       """d""",
       """n""",
       """\u0120""",
       """\u0120l""",
       """\u0120n""",
       """\u0120lo""",
       """\u0120low""",
       """er""",
       """\u0120lowest""",
       """\u0120newer""",
       """\u0120wider""",
       """<unk>""",
   ]
   _a						: Optional[int]    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: List[Any]    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
   _a						: Optional[Any]    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: Tuple    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write(json.dumps(UpperCAmelCase__							) + """\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Optional[Any]							,		**UpperCAmelCase__	:		Union[str, Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return self.tokenizer_class.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		List[Any]							,		**UpperCAmelCase__	:		Optional[Any]							)      -> List[str]:
   kwargs.update(self.special_tokens_map							)
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Tuple							,		UpperCAmelCase__	:		int							)      -> Optional[Any]:
   return "lower newer", "lower newer"


 @cached_property
 def _lowercase			(  self	:		List[Any]							)      -> List[Any]:
   return BartTokenizer.from_pretrained("""facebook/bart-large"""							)


 @cached_property
 def _lowercase			(  self	:		Optional[int]							)      -> Tuple:
   return BartTokenizerFast.from_pretrained("""facebook/bart-large"""							)


 @require_torch
 def _lowercase			(  self	:		List[str]							)      -> List[Any]:
   _a						: Dict    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   _a						: Union[str, Any]    =			[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     _a						: Tuple    =			tokenizer(UpperCAmelCase__							,		max_length=len(UpperCAmelCase__							)							,		padding=UpperCAmelCase__							,		return_tensors="""pt"""							)
     self.assertIsInstance(UpperCAmelCase__							,		UpperCAmelCase__							)

     self.assertEqual((2, 9)							,		batch.input_ids.shape							)
     self.assertEqual((2, 9)							,		batch.attention_mask.shape							)
     _a						: Any    =			batch.input_ids.tolist()[0]
     self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)
     # Test that special tokens are reset


 @require_torch
 def _lowercase			(  self	:		Optional[Any]							)      -> Optional[Any]:
   _a						: Optional[Any]    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     _a						: Optional[Any]    =			tokenizer(UpperCAmelCase__							,		padding=UpperCAmelCase__							,		return_tensors="""pt"""							)
     # check if input_ids are returned and no labels
     self.assertIn("""input_ids"""							,		UpperCAmelCase__							)
     self.assertIn("""attention_mask"""							,		UpperCAmelCase__							)
     self.assertNotIn("""labels"""							,		UpperCAmelCase__							)
     self.assertNotIn("""decoder_attention_mask"""							,		UpperCAmelCase__							)


 @require_torch
 def _lowercase			(  self	:		List[str]							)      -> Dict:
   _a						: Union[str, Any]    =			[
       """Summary of the text.""",
       """Another summary.""",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     _a						: List[Any]    =			tokenizer(text_target=UpperCAmelCase__							,		max_length=32							,		padding="""max_length"""							,		return_tensors="""pt"""							)
     self.assertEqual(32							,		targets["""input_ids"""].shape[1]							)


 @require_torch
 def _lowercase			(  self	:		List[Any]							)      -> int:
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     _a						: Tuple    =			tokenizer(
         ["""I am a small frog""" * 1024, """I am a small frog"""]							,		padding=UpperCAmelCase__							,		truncation=UpperCAmelCase__							,		return_tensors="""pt"""							)
     self.assertIsInstance(UpperCAmelCase__							,		UpperCAmelCase__							)
     self.assertEqual(batch.input_ids.shape							,		(2, 1024)							)


 @require_torch
 def _lowercase			(  self	:		List[Any]							)      -> Optional[int]:
   _a						: int    =			["""A long paragraph for summarization."""]
   _a						: int    =			[
       """Summary of the text.""",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     _a						: Tuple    =			tokenizer(UpperCAmelCase__							,		return_tensors="""pt"""							)
     _a						: Optional[Any]    =			tokenizer(text_target=UpperCAmelCase__							,		return_tensors="""pt"""							)
     _a						: Tuple    =			inputs["""input_ids"""]
     _a						: Union[str, Any]    =			targets["""input_ids"""]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)


 def _lowercase			(  self	:		Tuple							)      -> str:
   pass







 def _lowercase			(  self	:		Union[str, Any]							)      -> Any:
   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
       _a						: Optional[Any]    =			self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__							,		**UpperCAmelCase__							)
       _a						: List[str]    =			self.tokenizer_class.from_pretrained(UpperCAmelCase__							,		**UpperCAmelCase__							)
       _a						: Any    =			"""A, <mask> AllenNLP sentence."""
       _a						: List[str]    =			tokenizer_r.encode_plus(UpperCAmelCase__							,		add_special_tokens=UpperCAmelCase__							,		return_token_type_ids=UpperCAmelCase__							)
       _a						: Union[str, Any]    =			tokenizer_p.encode_plus(UpperCAmelCase__							,		add_special_tokens=UpperCAmelCase__							,		return_token_type_ids=UpperCAmelCase__							)

       # token_type_ids should put 0 everywhere
       self.assertEqual(sum(tokens_r["""token_type_ids"""]							)							,		sum(tokens_p["""token_type_ids"""]							)							)

       # attention_mask should put 1 everywhere, so sum over length should be 1
       self.assertEqual(
           sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)							,		sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)							,		)

       _a						: Tuple    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
       _a						: Dict    =			tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

       # Rust correctly handles the space before the mask while python doesnt
       self.assertSequenceEqual(tokens_p["""input_ids"""]							,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
       self.assertSequenceEqual(tokens_r["""input_ids"""]							,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

       self.assertSequenceEqual(
           UpperCAmelCase__							,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
       self.assertSequenceEqual(
           UpperCAmelCase__							,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)









"""simple docstring"""




from __future__ import annotations

import sys

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n < 0:
    raise ValueError("""Negative arguments are not supported"""       )
  return _fib(UpperCamelCase__       )[0]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  _a   ,						_a						: Dict    =			_fib(n // 2       )
  _a						: Dict    =			a * (b * 2 - a)
  _a						: Optional[Any]    =			a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 _snake_case					   =     int(sys.argv[1])
 print(F'''fibonacci({n}) is {fibonacci(n)}''')

