






'''simple docstring'''





import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


a_							=    logging.getLogger(__name__)


class      lowercase__     (			_UpperCamelCase	):
	a_         ="""token-classification"""

	def __init__(							self		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							if type(_UpperCAmelCase       ) == dict:
													lowerCAmelCase__       =						Namespace(**_UpperCAmelCase       )
							lowerCAmelCase__       =						import_module("tasks"       )
							try:
													lowerCAmelCase__       =						getattr(_UpperCAmelCase		,    hparams.task_type       )
													lowerCAmelCase__       =						token_classification_task_clazz()
							except AttributeError:
													raise ValueError(
													    F"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
													    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"       )
							lowerCAmelCase__       =						self.token_classification_task.get_labels(hparams.labels       )
							lowerCAmelCase__       =						CrossEntropyLoss().ignore_index
							super().__init__(_UpperCAmelCase		,    len(self.labels       )		,    self.mode       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					Any:







							'''simple docstring'''





							return self.model(**_UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
							if self.config.model_type != "distilbert":
													lowerCAmelCase__       =						(
													    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
													)  # XLM and RoBERTa don"t use token_type_ids

							lowerCAmelCase__       =						self(**_UpperCAmelCase       )
							lowerCAmelCase__       =						outputs[0]
							# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
							return {"loss": loss}

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						self.hparams
							for mode in ["train", "dev", "test"]:
													lowerCAmelCase__       =						self._feature_file(_UpperCAmelCase       )
													if os.path.exists(_UpperCAmelCase       ) and not args.overwrite_cache:
																			logger.info("Loading features from cached file %s"		,    _UpperCAmelCase       )
																			lowerCAmelCase__       =						torch.load(_UpperCAmelCase       )
													else:
																			logger.info("Creating features from dataset file at %s"		,    args.data_dir       )
																			lowerCAmelCase__       =						self.token_classification_task.read_examples_from_file(args.data_dir		,    _UpperCAmelCase       )
																			lowerCAmelCase__       =						self.token_classification_task.convert_examples_to_features(
																			    _UpperCAmelCase		,    self.labels		,    args.max_seq_length		,    self.tokenizer		,    cls_token_at_end=bool(self.config.model_type in ["xlnet"]       )		,    cls_token=self.tokenizer.cls_token		,    cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0		,    sep_token=self.tokenizer.sep_token		,    sep_token_extra=_UpperCAmelCase		,    pad_on_left=bool(self.config.model_type in ["xlnet"]       )		,    pad_token=self.tokenizer.pad_token_id		,    pad_token_segment_id=self.tokenizer.pad_token_type_id		,    pad_token_label_id=self.pad_token_label_id		,    )
																			logger.info("Saving features into cached file %s"		,    _UpperCAmelCase       )
																			torch.save(_UpperCAmelCase		,    _UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = False       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self._feature_file(_UpperCAmelCase       )
							logger.info("Loading features from cached file %s"		,    _UpperCAmelCase       )
							lowerCAmelCase__       =						torch.load(_UpperCAmelCase       )
							lowerCAmelCase__       =						torch.tensor([f.input_ids for f in features]		,    dtype=torch.long       )
							lowerCAmelCase__       =						torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long       )
							if features[0].token_type_ids is not None:
													lowerCAmelCase__       =						torch.tensor([f.token_type_ids for f in features]		,    dtype=torch.long       )
							else:
													lowerCAmelCase__       =						torch.tensor([0 for f in features]		,    dtype=torch.long       )
													# HACK(we will not use this anymore soon)
							lowerCAmelCase__       =						torch.tensor([f.label_ids for f in features]		,    dtype=torch.long       )
							return DataLoader(
							    TensorDataset(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase       )		,    batch_size=_UpperCAmelCase       )

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







							'''simple docstring'''





							"""Compute validation""" ""
							lowerCAmelCase__       =						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
							if self.config.model_type != "distilbert":
													lowerCAmelCase__       =						(
													    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
													)  # XLM and RoBERTa don"t use token_type_ids
							lowerCAmelCase__       =						self(**_UpperCAmelCase       )
							lowerCAmelCase__       =						outputs[:2]
							lowerCAmelCase__       =						logits.detach().cpu().numpy()
							lowerCAmelCase__       =						inputs['''labels'''].detach().cpu().numpy()
							return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						torch.stack([x["val_loss"] for x in outputs]       ).mean()
							lowerCAmelCase__       =						np.concatenate([x["pred"] for x in outputs]		,    axis=0       )
							lowerCAmelCase__       =						np.argmax(_UpperCAmelCase		,    axis=2       )
							lowerCAmelCase__       =						np.concatenate([x["target"] for x in outputs]		,    axis=0       )

							lowerCAmelCase__       =						dict(enumerate(self.labels       )       )
							lowerCAmelCase__       =						[[] for _ in range(out_label_ids.shape[0]       )]
							lowerCAmelCase__       =						[[] for _ in range(out_label_ids.shape[0]       )]

							for i in range(out_label_ids.shape[0]       ):
													for j in range(out_label_ids.shape[1]       ):
																			if out_label_ids[i, j] != self.pad_token_label_id:
																									out_label_list[i].append(label_map[out_label_ids[i][j]]       )
																									preds_list[i].append(label_map[preds[i][j]]       )

							lowerCAmelCase__       =						{
							    '''val_loss''': val_loss_mean,
							    '''accuracy_score''': accuracy_score(_UpperCAmelCase		,    _UpperCAmelCase       ),
							    '''precision''': precision_score(_UpperCAmelCase		,    _UpperCAmelCase       ),
							    '''recall''': recall_score(_UpperCAmelCase		,    _UpperCAmelCase       ),
							    '''f1''': fa_score(_UpperCAmelCase		,    _UpperCAmelCase       ),
							}

							lowerCAmelCase__       =						dict(results.items()       )
							lowerCAmelCase__       =						results
							return ret, preds_list, out_label_list

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self._eval_end(_UpperCAmelCase       )
							lowerCAmelCase__       =						ret['''log''']
							return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self._eval_end(_UpperCAmelCase       )

							# Converting to the dict required by pl
							# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
							# pytorch_lightning/trainer/logging.py#L139
							lowerCAmelCase__       =						ret['''log''']
							# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
							return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


	@staticmethod
	def 		UpperCAmelCase  (							__UpperCAmelCase		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							BaseTransformer.add_model_specific_args(_UpperCAmelCase		,    _UpperCAmelCase       )
							parser.add_argument(
							    "--task_type"		,    default="NER"		,    type=_UpperCAmelCase		,    help="Task type to fine tune in training (e.g. NER, POS, etc)"       )
							parser.add_argument(
							    "--max_seq_length"		,    default=128		,    type=_UpperCAmelCase		,    help=(
							        "The maximum total input sequence length after tokenization. Sequences longer "
							        "than this will be truncated, sequences shorter will be padded."
							    )		,    )

							parser.add_argument(
							    "--labels"		,    default=""		,    type=_UpperCAmelCase		,    help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,    )
							parser.add_argument(
							    "--gpus"		,    default=0		,    type=_UpperCAmelCase		,    help="The number of GPUs allocated for this, it is by default 0 meaning none"		,    )

							parser.add_argument(
							    "--overwrite_cache"		,    action="store_true"		,    help="Overwrite the cached training and evaluation sets"       )

							return parser


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	a_							=    NERTransformer.add_model_specific_args(parser, os.getcwd())
	a_							=    parser.parse_args()
	a_							=    NERTransformer(args)
	a_							=    generic_train(model, args)

	if args.do_predict:
		# See https://github.com/huggingface/transformers/issues/3159
		# pl use this default format to create a checkpoint:
		# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
		# /pytorch_lightning/callbacks/model_checkpoint.py#L322
		a_							=    sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
		a_							=    model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)














import random


def        _a  (     UpperCamelCase_					:				int   ) ->   bool:
						"""simple docstring"""







						lowerCAmelCase__       =						num - 1
						lowerCAmelCase__       =						0

						while s % 2 == 0:
												lowerCAmelCase__       =						s // 2
												t += 1

						for _ in range(5   ):
												lowerCAmelCase__       =						random.randrange(2    ,   num - 1   )
												lowerCAmelCase__       =						pow(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
												if v != 1:
																		lowerCAmelCase__       =						0
																		while v != (num - 1):
																								if i == t - 1:
																														return False
																								else:
																														lowerCAmelCase__       =						i + 1
																														lowerCAmelCase__       =						(v**2) % num
						return True


def        _a  (     UpperCamelCase_					:				int   ) ->   bool:
						"""simple docstring"""







						if num < 2:
												return False

						lowerCAmelCase__       =						[
						    2,
						    3,
						    5,
						    7,
						    11,
						    13,
						    17,
						    19,
						    23,
						    29,
						    31,
						    37,
						    41,
						    43,
						    47,
						    53,
						    59,
						    61,
						    67,
						    71,
						    73,
						    79,
						    83,
						    89,
						    97,
						    101,
						    103,
						    107,
						    109,
						    113,
						    127,
						    131,
						    137,
						    139,
						    149,
						    151,
						    157,
						    163,
						    167,
						    173,
						    179,
						    181,
						    191,
						    193,
						    197,
						    199,
						    211,
						    223,
						    227,
						    229,
						    233,
						    239,
						    241,
						    251,
						    257,
						    263,
						    269,
						    271,
						    277,
						    281,
						    283,
						    293,
						    307,
						    311,
						    313,
						    317,
						    331,
						    337,
						    347,
						    349,
						    353,
						    359,
						    367,
						    373,
						    379,
						    383,
						    389,
						    397,
						    401,
						    409,
						    419,
						    421,
						    431,
						    433,
						    439,
						    443,
						    449,
						    457,
						    461,
						    463,
						    467,
						    479,
						    487,
						    491,
						    499,
						    503,
						    509,
						    521,
						    523,
						    541,
						    547,
						    557,
						    563,
						    569,
						    571,
						    577,
						    587,
						    593,
						    599,
						    601,
						    607,
						    613,
						    617,
						    619,
						    631,
						    641,
						    643,
						    647,
						    653,
						    659,
						    661,
						    673,
						    677,
						    683,
						    691,
						    701,
						    709,
						    719,
						    727,
						    733,
						    739,
						    743,
						    751,
						    757,
						    761,
						    769,
						    773,
						    787,
						    797,
						    809,
						    811,
						    821,
						    823,
						    827,
						    829,
						    839,
						    853,
						    857,
						    859,
						    863,
						    877,
						    881,
						    883,
						    887,
						    907,
						    911,
						    919,
						    929,
						    937,
						    941,
						    947,
						    953,
						    967,
						    971,
						    977,
						    983,
						    991,
						    997,
						]

						if num in low_primes:
												return True

						for prime in low_primes:
												if (num % prime) == 0:
																		return False

						return rabin_miller(UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				int = 1_024   ) ->   int:
						"""simple docstring"""







						while True:
												lowerCAmelCase__       =						random.randrange(2 ** (keysize - 1)    ,   2 ** (keysize)   )
												if is_prime_low_num(UpperCamelCase_   ):
																		return num


if __name__ == "__main__":
	a_							=    generate_large_prime()
	print(('''Prime number:''', num))
	print(('''is_prime_low_num:''', is_prime_low_num(num)))






