





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							:			Dict         =			logging.getLogger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'token-classification'




     def __init__(    self				,     snake_case_							):
         if type(snake_case_							) == dict:
             _A     =							Namespace(**snake_case_							)
         _A     =							import_module('tasks'							)
         try:
             _A     =							getattr(snake_case_				,     hparams.task_type							)
             _A     =							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__()}"							)
         _A     =							self.token_classification_task.get_labels(hparams.labels							)
         _A     =							CrossEntropyLoss().ignore_index
         super().__init__(snake_case_				,     len(self.labels							)				,     self.mode							)




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         return self.model(**snake_case_							)




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

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.hparams
         for mode in ["train", "dev", "test"]:
             _A     =							self._feature_file(snake_case_							)
             if os.path.exists(snake_case_							) and not args.overwrite_cache:
                 logger.info('Loading features from cached file %s'				,     snake_case_							)
                 _A     =							torch.load(snake_case_							)
             else:
                 logger.info('Creating features from dataset file at %s'				,     args.data_dir							)
                 _A     =							self.token_classification_task.read_examples_from_file(args.data_dir				,     snake_case_							)
                 _A     =							self.token_classification_task.convert_examples_to_features(
                     snake_case_				,     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=snake_case_				,     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'				,     snake_case_							)
                 torch.save(snake_case_				,     snake_case_							)




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




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




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							torch.stack([x['val_loss'] for x in outputs]							).mean()
         _A     =							np.concatenate([x['pred'] for x in outputs]				,     axis=0							)
         _A     =							np.argmax(snake_case_				,     axis=2							)
         _A     =							np.concatenate([x['target'] for x in outputs]				,     axis=0							)

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

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

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




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         # when stable
         _A,			_A,			_A     =							self._eval_end(snake_case_							)
         _A     =							ret['log']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         # updating to test_epoch_end instead of deprecated test_end
         _A,			_A,			_A     =							self._eval_end(snake_case_							)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         _A     =							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        lowerCAmelCase__		(    snake_case_				,     snake_case_							):
         # Add NER specific options
         BaseTransformer.add_model_specific_args(snake_case_				,     snake_case_							)
         parser.add_argument(
             '--task_type'				,     default='NER'				,     type=snake_case_				,     help='Task type to fine tune in training (e.g. NER, POS, etc)'							)
         parser.add_argument(
             '--max_seq_length'				,     default=128				,     type=snake_case_				,     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=snake_case_				,     help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'				,     )
         parser.add_argument(
             '--gpus'				,     default=0				,     type=snake_case_				,     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							:			Dict         =			argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 __A							:			Any         =			NERTransformer.add_model_specific_args(parser, os.getcwd())
 __A							:			str         =			parser.parse_args()
 __A							:			List[str]         =			NERTransformer(args)
 __A							:			Dict         =			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							:			Union[str, Any]         =			sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
  __A							:			Optional[int]         =			model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)












from __future__ import annotations

import math





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> list[int]:




    """simple docstring"""




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

    _A     =							[True] * (num + 1)
    _A     =							[]
    _A     =							2
    _A     =							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:
                    _A     =							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())))





