




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 			UpperCAmelCase_      (   lowercase__		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     BarthezTokenizer
							a__						  =     BarthezTokenizerFast
							a__						  =     True
							a__						  =     True





							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										super().setUp()

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





							def    _UpperCAmelCase     (					self       :	Dict			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					"<pad>"
										SCREAMING_SNAKE_CASE           =					1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase			)       ,     __lowerCamelCase			)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase			)       ,     __lowerCamelCase			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					str:
										SCREAMING_SNAKE_CASE           =					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(__lowerCamelCase			)       ,     101_122			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										self.assertEqual(self.get_tokenizer().vocab_size       ,     101_122			)





							@require_torch
							def    _UpperCAmelCase     (					self       :	int			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					["A long paragraph for summarization.", "Another paragraph for summarization."]
										SCREAMING_SNAKE_CASE           =					[0, 57, 3_018, 70_307, 91, 2]

										SCREAMING_SNAKE_CASE           =					self.tokenizer(
										    __lowerCamelCase       ,     max_length=len(__lowerCamelCase			)       ,     padding=__lowerCamelCase       ,     truncation=__lowerCamelCase       ,     return_tensors="""pt"""			)
										self.assertIsInstance(__lowerCamelCase       ,     __lowerCamelCase			)

										self.assertEqual((2, 6)       ,     batch.input_ids.shape			)
										self.assertEqual((2, 6)       ,     batch.attention_mask.shape			)
										SCREAMING_SNAKE_CASE           =					batch.input_ids.tolist()[0]
										self.assertListEqual(__lowerCamelCase       ,     __lowerCamelCase			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:



										if not self.test_rust_tokenizer:
													return

										SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
										SCREAMING_SNAKE_CASE           =					self.get_rust_tokenizer()

										SCREAMING_SNAKE_CASE           =					"I was born in 92000, and this is falsé."

										SCREAMING_SNAKE_CASE           =					tokenizer.tokenize(__lowerCamelCase			)
										SCREAMING_SNAKE_CASE           =					rust_tokenizer.tokenize(__lowerCamelCase			)
										self.assertListEqual(__lowerCamelCase       ,     __lowerCamelCase			)

										SCREAMING_SNAKE_CASE           =					tokenizer.encode(__lowerCamelCase       ,     add_special_tokens=__lowerCamelCase			)
										SCREAMING_SNAKE_CASE           =					rust_tokenizer.encode(__lowerCamelCase       ,     add_special_tokens=__lowerCamelCase			)
										self.assertListEqual(__lowerCamelCase       ,     __lowerCamelCase			)

										SCREAMING_SNAKE_CASE           =					self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE           =					tokenizer.encode(__lowerCamelCase			)
										SCREAMING_SNAKE_CASE           =					rust_tokenizer.encode(__lowerCamelCase			)
										self.assertListEqual(__lowerCamelCase       ,     __lowerCamelCase			)







							@slow
							def    _UpperCAmelCase     (					self       :	Dict			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					{"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.
										SCREAMING_SNAKE_CASE           =					[
										    "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=__lowerCamelCase       ,     model_name="""moussaKam/mbarthez"""       ,     revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""       ,     sequences=__lowerCamelCase       ,     )







from __future__ import annotations

import math
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

			if num <= 0:
						SCREAMING_SNAKE_CASE           =					f"""{num}: Invalid input, please enter a positive integer."""
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					[True] * (num + 1)
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					2
			SCREAMING_SNAKE_CASE           =					int(math.sqrt(SCREAMING_SNAKE_CASE   )   )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(SCREAMING_SNAKE_CASE   )

									# Set multiples of start be False
									for i in range(start * start					,	num + 1					,	SCREAMING_SNAKE_CASE   ):
												if sieve[i] is True:
															SCREAMING_SNAKE_CASE           =					False

						start += 1

			for j in range(end + 1					,	num + 1   ):
						if sieve[j] is True:
									prime.append(SCREAMING_SNAKE_CASE   )

			return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("""Enter a positive integer: """).strip())))






