






"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Optional[int] = None					, ):


						'''simple docstring'''






						snake_case_   : str						=			{}

						if train_file is not None:
												snake_case_   : Any						=			[train_file]
						if eval_file is not None:
												snake_case_   : Tuple						=			[eval_file]
						if test_file is not None:
												snake_case_   : Optional[int]						=			[test_file]

						snake_case_   : Union[str, Any]						=			datasets.load_dataset("""csv"""					, data_files=SCREAMING_SNAKE_CASE_    )
						snake_case_   : Dict						=			list(ds[list(files.keys()    )[0]].features.keys()    )
						snake_case_   : Optional[int]						=			features_name.pop(SCREAMING_SNAKE_CASE_    )
						snake_case_   : Union[str, Any]						=			list(set(ds[list(files.keys()    )[0]][label_name]    )    )
						snake_case_   : int						=			{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_    )}
						snake_case_   : int						=			tokenizer.model_input_names
						snake_case_   : Optional[int]						=			{}

						if len(SCREAMING_SNAKE_CASE_    ) == 1:
												for k in files.keys():
																		snake_case_   : Any						=			ds[k].map(
																		    lambda __UpperCamelCase    : tokenizer.batch_encode_plus(
																		        example[features_name[0]]					, truncation=SCREAMING_SNAKE_CASE_					, max_length=SCREAMING_SNAKE_CASE_					, padding="""max_length"""    )					, batched=SCREAMING_SNAKE_CASE_					, )
						elif len(SCREAMING_SNAKE_CASE_    ) == 2:
												for k in files.keys():
																		snake_case_   : Dict						=			ds[k].map(
																		    lambda __UpperCamelCase    : tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])					, truncation=SCREAMING_SNAKE_CASE_					, max_length=SCREAMING_SNAKE_CASE_					, padding="""max_length"""					, )					, batched=SCREAMING_SNAKE_CASE_					, )

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		snake_case_   : Optional[Any]						=			{k: v for k, v in ex.items() if k in input_names}
																		snake_case_   : Tuple						=			labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		snake_case_   : Dict						=			{k: v for k, v in ex.items() if k in input_names}
																		snake_case_   : Dict						=			labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		snake_case_   : List[str]						=			{k: v for k, v in ex.items() if k in input_names}
																		snake_case_   : str						=			labelaid[ex[label_name]]
																		yield (d, label)

						snake_case_   : Optional[int]						=			(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE_					, ({k: tf.intaa for k in input_names}, tf.intaa)					, ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))					, )
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												snake_case_   : int						=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

						snake_case_   : Dict						=			(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE_					, ({k: tf.intaa for k in input_names}, tf.intaa)					, ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))					, )
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												snake_case_   : Dict						=			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

						snake_case_   : Dict						=			(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE_					, ({k: tf.intaa for k in input_names}, tf.intaa)					, ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))					, )
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												snake_case_   : List[Any]						=			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

						return train_ds, val_ds, test_ds, labelaid


__lowerCAmelCase       :					str      					=					logging.getLogger(__name__)



@dataclass
class    _lowerCAmelCase    :





		"""simple docstring"""






		_lowerCamelCase			  =       field(metadata={'''help''': '''Which column contains the label'''}		)
		_lowerCamelCase			  =       field(default=lowercase__  ,				metadata={'''help''': '''The path of the training file'''}		)
		_lowerCamelCase			  =       field(default=lowercase__  ,				metadata={'''help''': '''The path of the development file'''}		)
		_lowerCamelCase			  =       field(default=lowercase__  ,				metadata={'''help''': '''The path of the test file'''}		)
		_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=lowercase__  ,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)




@dataclass
class    _lowerCAmelCase    :





		"""simple docstring"""






		_lowerCamelCase			  =       field(
		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
		_lowerCamelCase			  =       field(
		    default=lowercase__  ,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
		_lowerCamelCase			  =       field(
		    default=lowercase__  ,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
		_lowerCamelCase			  =       field(default=lowercase__  ,				metadata={'''help''': '''Set this flag to use fast tokenization.'''}		)
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		_lowerCamelCase			  =       field(
		    default=lowercase__  ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}  ,				)




def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : int						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
						snake_case_			,		snake_case_			,		snake_case_   : List[str]						=			parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir    )
						    and os.listdir(training_args.output_dir    )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
												    """ --overwrite_output_dir to overcome."""    )

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					, datefmt="""%m/%d/%Y %H:%M:%S"""					, level=logging.INFO					, )
						logger.info(
						    F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, '
						    F'16-bits training: {training_args.fpaa}'    )
						logger.info(F'Training/evaluation parameters {training_args}'    )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						snake_case_   : str						=			AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					, cache_dir=model_args.cache_dir					, )

						snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Optional[Any]						=			get_tfds(
						    train_file=data_args.train_file					, eval_file=data_args.dev_file					, test_file=data_args.test_file					, tokenizer=SCREAMING_SNAKE_CASE_					, label_column_id=data_args.label_column_id					, max_seq_length=data_args.max_seq_length					, )

						snake_case_   : Optional[Any]						=			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path					, num_labels=len(SCREAMING_SNAKE_CASE_    )					, labelaid=SCREAMING_SNAKE_CASE_					, idalabel={id: label for label, id in labelaid.items()}					, finetuning_task="""text-classification"""					, cache_dir=model_args.cache_dir					, )

						with training_args.strategy.scope():
												snake_case_   : Any						=			TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path					, from_pt=bool(""".bin""" in model_args.model_name_or_path    )					, config=SCREAMING_SNAKE_CASE_					, cache_dir=model_args.cache_dir					, )

						def compute_metrics(__UpperCamelCase      :			Any    ) -> Dict:
												snake_case_   : Optional[int]						=			np.argmax(p.predictions					, axis=1    )

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						snake_case_   : str						=			TFTrainer(
						    model=SCREAMING_SNAKE_CASE_					, args=SCREAMING_SNAKE_CASE_					, train_dataset=SCREAMING_SNAKE_CASE_					, eval_dataset=SCREAMING_SNAKE_CASE_					, compute_metrics=SCREAMING_SNAKE_CASE_					, )

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir    )

						# Evaluation
						snake_case_   : int						=			{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""    )
												snake_case_   : str						=			trainer.evaluate()
												snake_case_   : int						=			os.path.join(training_args.output_dir					, """eval_results.txt"""    )

												with open(SCREAMING_SNAKE_CASE_					, """w"""    ) as writer:
																		logger.info("""***** Eval results *****"""    )

																		for key, value in result.items():
																								logger.info(F'  {key} = {value}'    )
																								writer.write(F'{key} = {value}\n'    )

																		results.update(SCREAMING_SNAKE_CASE_    )

						return results


if __name__ == "__main__":
					main()






"""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()

