




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       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase   ):
						lowerCAmelCase__ 			=BartTokenizer
						lowerCAmelCase__ 			=BartTokenizerFast
						lowerCAmelCase__ 			=True
						lowerCAmelCase__ 			=filter_roberta_detectors



						def   __a				( self  :      str     ):

								"""simple docstring"""

								super().setUp()
								SCREAMING_SNAKE_CASE_							=       [
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    '\u0120',
								    '\u0120l',
								    '\u0120n',
								    '\u0120lo',
								    '\u0120low',
								    'er',
								    '\u0120lowest',
								    '\u0120newer',
								    '\u0120wider',
								    '<unk>',
								]
								SCREAMING_SNAKE_CASE_							=       dict(zip(snake_case__				,       range(len(snake_case__     )     )     )     )
								SCREAMING_SNAKE_CASE_							=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
								SCREAMING_SNAKE_CASE_							=       {'unk_token': '<unk>'}

								SCREAMING_SNAKE_CASE_							=       os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['vocab_file']     )
								SCREAMING_SNAKE_CASE_							=       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(snake_case__     ) + '\n'     )
								with open(self.merges_file				,       'w'				,       encoding='utf-8'     ) as fp:
										fp.write('\n'.join(snake_case__     )     )



						def   __a				( self  :      List[Any]				,       **snake_case__  :      Dict     ):

								"""simple docstring"""

								kwargs.update(self.special_tokens_map     )
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,       **snake_case__     )



						def   __a				( self  :      int				,       **snake_case__  :      Any     ):

								"""simple docstring"""

								kwargs.update(self.special_tokens_map     )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,       **snake_case__     )



						def   __a				( self  :      int				,       snake_case__  :      Tuple     ):

								"""simple docstring"""

								return "lower newer", "lower newer"



						@cached_property
						def   __a				( self  :      str     ):

								"""simple docstring"""

								return BartTokenizer.from_pretrained('facebook/bart-large'     )



						@cached_property
						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								return BartTokenizerFast.from_pretrained('facebook/bart-large'     )



						@require_torch
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								SCREAMING_SNAKE_CASE_							=       [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__				,       max_length=len(snake_case__     )				,       padding=snake_case__				,       return_tensors='pt'     )
										self.assertIsInstance(snake_case__				,       snake_case__     )

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



						@require_torch
						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__				,       padding=snake_case__				,       return_tensors='pt'     )
										# check if input_ids are returned and no labels
										self.assertIn('input_ids'				,       snake_case__     )
										self.assertIn('attention_mask'				,       snake_case__     )
										self.assertNotIn('labels'				,       snake_case__     )
										self.assertNotIn('decoder_attention_mask'				,       snake_case__     )



						@require_torch
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       [
								    'Summary of the text.',
								    'Another summary.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(text_target=snake_case__				,       max_length=32				,       padding='max_length'				,       return_tensors='pt'     )
										self.assertEqual(32				,       targets['input_ids'].shape[1]     )



						@require_torch
						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(
										    ['I am a small frog' * 10_24, 'I am a small frog']				,       padding=snake_case__				,       truncation=snake_case__				,       return_tensors='pt'     )
										self.assertIsInstance(snake_case__				,       snake_case__     )
										self.assertEqual(batch.input_ids.shape				,       (2, 10_24)     )



						@require_torch
						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ['A long paragraph for summarization.']
								SCREAMING_SNAKE_CASE_							=       [
								    'Summary of the text.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										SCREAMING_SNAKE_CASE_							=       tokenizer(snake_case__				,       return_tensors='pt'     )
										SCREAMING_SNAKE_CASE_							=       tokenizer(text_target=snake_case__				,       return_tensors='pt'     )
										SCREAMING_SNAKE_CASE_							=       inputs['input_ids']
										SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      str     ):

								"""simple docstring"""

								pass






						def   __a				( self  :      Union[str, Any]     ):

								"""simple docstring"""

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
												SCREAMING_SNAKE_CASE_							=       self.rust_tokenizer_class.from_pretrained(snake_case__				,       **snake_case__     )
												SCREAMING_SNAKE_CASE_							=       self.tokenizer_class.from_pretrained(snake_case__				,       **snake_case__     )
												SCREAMING_SNAKE_CASE_							=       'A, <mask> AllenNLP sentence.'
												SCREAMING_SNAKE_CASE_							=       tokenizer_r.encode_plus(snake_case__				,       add_special_tokens=snake_case__				,       return_token_type_ids=snake_case__     )
												SCREAMING_SNAKE_CASE_							=       tokenizer_p.encode_plus(snake_case__				,       add_special_tokens=snake_case__				,       return_token_type_ids=snake_case__     )

												# 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']     )				,       )

												SCREAMING_SNAKE_CASE_							=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
												SCREAMING_SNAKE_CASE_							=       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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )
												self.assertSequenceEqual(tokens_r['input_ids']				,       [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )

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





from __future__ import annotations

import sys


def 				_a       (				lowerCAmelCase   )->  int:
		if n < 0:
				raise ValueError('Negative arguments are not supported'   )
		return _fib(lowerCAmelCase   )[0]


def 				_a       (				lowerCAmelCase   )->  tuple[int, int]:
		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
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       _fib(n // 2   )
		SCREAMING_SNAKE_CASE_							=       a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_							=       a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    Optional[int]											=   int(sys.argv[1])
						print(f"""fibonacci({n}) is {fibonacci(n)}""")