



"""simple docstring"""





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__lowerCAmelCase       :					Optional[Any]      					=					logging.get_logger(__name__)



@dataclass
class    _lowerCAmelCase    :





		"""simple docstring"""






		_lowerCamelCase			  =       field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()		)}		)
		_lowerCamelCase			  =       field(
		    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}		)
		_lowerCamelCase			  =       field(
		    default=128  ,				metadata={
		        '''help''': (
		            '''The maximum total input sequence length after tokenization. Sequences longer '''
		            '''than this will be truncated, sequences shorter will be padded.'''
		        )
		    }  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Dict						=			self.task_name.lower()




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''train'''
		_lowerCamelCase			  =       '''dev'''
		_lowerCamelCase			  =       '''test'''




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       42
		_lowerCamelCase			  =       42
		_lowerCamelCase			  =       42





		def __init__(					self							,    _lowercase							,    _lowercase							,    _lowercase = None							,    _lowercase = Split.train							,    _lowercase = None							,    ) ->						Optional[Any]:







								'''simple docstring'''

								warnings.warn(
								    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
								    """library. You can have a look at this example script for pointers: """
								    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""							,    _lowercase							,    )
								snake_case_   : List[Any]						=			args
								snake_case_   : List[str]						=			glue_processors[args.task_name]()
								snake_case_   : Union[str, Any]						=			glue_output_modes[args.task_name]
								if isinstance(_lowercase							,    _lowercase   ):
														try:
																				snake_case_   : Optional[Any]						=			Split[mode]
														except KeyError:
																				raise KeyError("""mode is not a valid split name"""   )
        # Load data features from cache or dataset file
								snake_case_   : Optional[int]						=			os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir							,    f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'							,    )
								snake_case_   : str						=			self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														snake_case_			,		snake_case_   : List[Any]						=			label_list[2], label_list[1]
								snake_case_   : Dict						=			label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_   : Optional[Any]						=			cached_features_file + """.lock"""
								with FileLock(_lowercase   ):
														if os.path.exists(_lowercase   ) and not args.overwrite_cache:
																				snake_case_   : str						=			time.time()
																				snake_case_   : Tuple						=			torch.load(_lowercase   )
																				logger.info(
																				    f'Loading features from cached file {cached_features_file} [took %.3f s]'							,    time.time() - start   )
														else:
																				logger.info(f'Creating features from dataset file at {args.data_dir}'   )

																				if mode == Split.dev:
																										snake_case_   : Any						=			self.processor.get_dev_examples(args.data_dir   )
																				elif mode == Split.test:
																										snake_case_   : Optional[Any]						=			self.processor.get_test_examples(args.data_dir   )
																				else:
																										snake_case_   : int						=			self.processor.get_train_examples(args.data_dir   )
																				if limit_length is not None:
																										snake_case_   : str						=			examples[:limit_length]
																				snake_case_   : Tuple						=			glue_convert_examples_to_features(
																				    _lowercase							,    _lowercase							,    max_length=args.max_seq_length							,    label_list=_lowercase							,    output_mode=self.output_mode							,    )
																				snake_case_   : List[str]						=			time.time()
																				torch.save(self.features							,    _lowercase   )
																				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																				logger.info(
																				    f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'   )





		def __len__(					self   ) ->						int:







								'''simple docstring'''

								return len(self.features   )





		def __getitem__(					self							,    _lowercase   ) ->						InputFeatures:







								'''simple docstring'''

								return self.features[i]





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return self.label_list






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			bool = False    ):


						'''simple docstring'''






						if n == 2:
												return True
						if not n % 2 or n < 2:
												return False
						if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
												return False
						if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
												raise ValueError(
												    """Warning: upper bound of deterministic test is exceeded. """
												    """Pass allow_probable=True to allow probabilistic test. """
												    """A return value of True indicates a probable prime."""    )
						# array bounds provided by analysis
						snake_case_   : List[Any]						=			[
						    2_0_4_7,
						    1_3_7_3_6_5_3,
						    2_5_3_2_6_0_0_1,
						    3_2_1_5_0_3_1_7_5_1,
						    2_1_5_2_3_0_2_8_9_8_7_4_7,
						    3_4_7_4_7_4_9_6_6_0_3_8_3,
						    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
						    1,
						    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
						    1,
						    1,
						    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
						    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
						]

						snake_case_   : Dict						=			[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
						for idx, _p in enumerate(__UpperCamelCase					, 1    ):
												if n < _p:
																		# then we have our last prime to check
																		snake_case_   : Optional[int]						=			primes[:idx]
																		break
						snake_case_			,		snake_case_   : Tuple						=			n - 1, 0
						# break up n -1 into a power of 2 (s) and
						# remaining odd component
						# essentially, solve for d * 2 ** s == n - 1
						while d % 2 == 0:
												d //= 2
												s += 1
						for prime in plist:
												snake_case_   : List[str]						=			False
												for r in range(__UpperCamelCase    ):
																		snake_case_   : int						=			pow(__UpperCamelCase					, d * 2**r					, __UpperCamelCase    )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								snake_case_   : Optional[Any]						=			True
																								# this loop will not determine compositeness
																								break
												if pr:
																		continue
												# if pr is False, then the above loop never evaluated to true,
												# and the n MUST be composite
												return False
						return True
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						assert not miller_rabin(5_6_1    )
						assert miller_rabin(5_6_3    )
						# 2047

						assert not miller_rabin(8_3_8_2_0_1    )
						assert miller_rabin(8_3_8_2_0_7    )
						# 1_373_653

						assert not miller_rabin(1_7_3_1_6_0_0_1    )
						assert miller_rabin(1_7_3_1_6_0_1_7    )
						# 25_326_001

						assert not miller_rabin(3_0_7_8_3_8_6_6_4_1    )
						assert miller_rabin(3_0_7_8_3_8_6_6_5_3    )
						# 3_215_031_751

						assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1    )
						assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9    )
						# 2_152_302_898_747

						assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7    )
						assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7    )
						# 3_474_749_660_383

						assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1    )
						assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7    )
						# 341_550_071_728_321

						assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1    )
						assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1    )
						# 3_825_123_056_546_413_051

						assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7    )
						assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1    )
						# 318_665_857_834_031_151_167_461

						assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3    )
						assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9    )
						# 3_317_044_064_679_887_385_961_981
						# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()

