





import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_        =							logging.getLogger(__name__)



@dataclass(frozen=_a	)
class    __lowerCAmelCase			:
			lowerCamelCase_	:	str
			lowerCamelCase_	:	str
			lowerCamelCase_	:	Optional[str]			     = None
			lowerCamelCase_	:	Optional[str]			     = None
			lowerCamelCase_	:	Optional[str]			     = None




@dataclass(frozen=_a	)
class    __lowerCAmelCase			:
			lowerCamelCase_	:	List[int]
			lowerCamelCase_	:	Optional[List[int]]			     = None
			lowerCamelCase_	:	Optional[List[int]]			     = None
			lowerCamelCase_	:	Optional[Union[int, float]]			     = None
			lowerCamelCase_	:	Optional[int]			     = None


if is_torch_available():
	import torch
	from torch.utils.data import Dataset



	class    __lowerCAmelCase			(     _a	):
				lowerCamelCase_	:	List[InputFeatures]






				def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__=False   ,				__magic_name__ = False   ,				)	->  int:


								'''simple docstring'''



								snake_case_		:       List[str]           =    hans_processors[task]()

								snake_case_		:       str           =    os.path.join(
								    __magic_name__   ,				'''cached_{}_{}_{}_{}'''.format(
								        '''dev''' if evaluate else '''train'''   ,				tokenizer.__class__.__name__   ,				str(__magic_name__       )   ,				__magic_name__   ,				)   ,				)
								snake_case_		:       List[Any]           =    processor.get_labels()
								if tokenizer.__class__ in (
								    RobertaTokenizer,
								    RobertaTokenizerFast,
								    XLMRobertaTokenizer,
								    BartTokenizer,
								    BartTokenizerFast,
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												snake_case_     ,				snake_case_		:       Any           =    label_list[2], label_list[1]
								snake_case_		:       Any           =    label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_		:       List[Any]           =    cached_features_file + '''.lock'''
								with FileLock(__magic_name__       ):
												if os.path.exists(__magic_name__       ) and not overwrite_cache:
																logger.info(F'''Loading features from cached file {cached_features_file}'''       )
																snake_case_		:       int           =    torch.load(__magic_name__       )
												else:
																logger.info(F'''Creating features from dataset file at {data_dir}'''       )

																snake_case_		:       List[str]           =    (
																    processor.get_dev_examples(__magic_name__       ) if evaluate else processor.get_train_examples(__magic_name__       )
																)

																logger.info('''Training examples: %s'''   ,				len(__magic_name__       )       )
																snake_case_		:       List[str]           =    hans_convert_examples_to_features(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )
																logger.info('''Saving features into cached file %s'''   ,				__magic_name__       )
																torch.save(self.features   ,				__magic_name__       )






				def __len__(self       )	->  List[str]:


								'''simple docstring'''



								return len(self.features       )






				def __getitem__(self   ,				__magic_name__       )	->  InputFeatures:


								'''simple docstring'''



								return self.features[i]






				def      lowerCamelCase    (self       )	->  Optional[Any]:


								'''simple docstring'''



								return self.label_list


if is_tf_available():
	import tensorflow as tf



	class    __lowerCAmelCase			:
				lowerCamelCase_	:	List[InputFeatures]






				def __init__(self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = 128   ,				__magic_name__=False   ,				__magic_name__ = False   ,				)	->  List[str]:


								'''simple docstring'''



								snake_case_		:       int           =    hans_processors[task]()
								snake_case_		:       int           =    processor.get_labels()
								if tokenizer.__class__ in (
								    RobertaTokenizer,
								    RobertaTokenizerFast,
								    XLMRobertaTokenizer,
								    BartTokenizer,
								    BartTokenizerFast,
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												snake_case_     ,				snake_case_		:       Any           =    label_list[2], label_list[1]
								snake_case_		:       Any           =    label_list

								snake_case_		:       List[Any]           =    processor.get_dev_examples(__magic_name__       ) if evaluate else processor.get_train_examples(__magic_name__       )
								snake_case_		:       Dict           =    hans_convert_examples_to_features(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       )

								def gen():
												for ex_index, ex in tqdm.tqdm(enumerate(self.features       )   ,				desc='''convert examples to features'''       ):
																if ex_index % 1_0000 == 0:
																				logger.info('''Writing example %d of %d''' % (ex_index, len(__magic_name__       ))       )

																yield (
																    {
																        "example_id": 0,
																        "input_ids": ex.input_ids,
																        "attention_mask": ex.attention_mask,
																        "token_type_ids": ex.token_type_ids,
																    },
																    ex.label,
																)

								snake_case_		:       str           =    tf.data.Dataset.from_generator(
								    __magic_name__   ,				(
								        {
								            '''example_id''': tf.intaa,
								            '''input_ids''': tf.intaa,
								            '''attention_mask''': tf.intaa,
								            '''token_type_ids''': tf.intaa,
								        },
								        tf.intaa,
								    )   ,				(
								        {
								            '''example_id''': tf.TensorShape([]       ),
								            '''input_ids''': tf.TensorShape([None, None]       ),
								            '''attention_mask''': tf.TensorShape([None, None]       ),
								            '''token_type_ids''': tf.TensorShape([None, None]       ),
								        },
								        tf.TensorShape([]       ),
								    )   ,				)






				def      lowerCamelCase    (self       )	->  Union[str, Any]:


								'''simple docstring'''



								return self.dataset






				def __len__(self       )	->  int:


								'''simple docstring'''



								return len(self.features       )






				def __getitem__(self   ,				__magic_name__       )	->  InputFeatures:


								'''simple docstring'''



								return self.features[i]






				def      lowerCamelCase    (self       )	->  Dict:


								'''simple docstring'''



								return self.label_list



class    __lowerCAmelCase			(     _a	):






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							return self._create_examples(self._read_tsv(os.path.join(__magic_name__   ,				'''heuristics_train_set.txt'''       )       )   ,				'''train'''       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  List[str]:


							'''simple docstring'''



							return self._create_examples(self._read_tsv(os.path.join(__magic_name__   ,				'''heuristics_evaluation_set.txt'''       )       )   ,				'''dev'''       )






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return ["contradiction", "entailment", "neutral"]






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__       )	->  Any:


							'''simple docstring'''



							snake_case_		:       List[Any]           =    []
							for i, line in enumerate(__magic_name__       ):
											if i == 0:
															continue
											snake_case_		:       List[Any]           =    '''%s-%s''' % (set_type, line[0])
											snake_case_		:       int           =    line[5]
											snake_case_		:       Tuple           =    line[6]
											snake_case_		:       Any           =    line[7][2:] if line[7].startswith('''ex'''       ) else line[7]
											snake_case_		:       List[str]           =    line[0]
											examples.append(InputExample(guid=__magic_name__   ,				text_a=__magic_name__   ,				text_b=__magic_name__   ,				label=__magic_name__   ,				pairID=__magic_name__       )       )
							return examples






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							)						->		Any:






				"""simple docstring"""


				snake_case_		:       Optional[Any]           =    {label: i for i, label in enumerate(_UpperCamelCase					)}

				snake_case_		:       Any           =    []
				for ex_index, example in tqdm.tqdm(enumerate(_UpperCamelCase					)					,							desc='''convert examples to features'''					):
								if ex_index % 10_000 == 0:
												logger.info('''Writing example %d''' % (ex_index)					)

								snake_case_		:       int           =    tokenizer(
								    example.text_a					,							example.text_b					,							add_special_tokens=_UpperCamelCase					,							max_length=_UpperCamelCase					,							padding='''max_length'''					,							truncation=_UpperCamelCase					,							return_overflowing_tokens=_UpperCamelCase					,							)

								snake_case_		:       List[Any]           =    label_map[example.label] if example.label in label_map else 0

								snake_case_		:       Any           =    int(example.pairID					)

								features.append(InputFeatures(**_UpperCamelCase					,							label=_UpperCamelCase					,							pairID=_UpperCamelCase					)					)

				for i, example in enumerate(examples[:5]					):
								logger.info('''*** Example ***'''					)
								logger.info(f'''guid: {example}'''					)
								logger.info(f'''features: {features[i]}'''					)

				return features


lowerCAmelCase_        =							{
    '''hans''': 3,
}





lowerCAmelCase_        =							{
    '''hans''': HansProcessor,
}

'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


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

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

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

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

				start += 1

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

		return prime


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




