

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


lowercase_			=  logging.getLogger(__name__)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'token-classification'


    def __init__(				self			:	str,lowercase_			:	int		)->   Optional[int]:





        '''simple docstring'''







        if type(lowercase_		) == dict:
            A__   				=       Namespace(**lowercase_		)
        A__   				=       import_module('tasks'		)
        try:
            A__   				=       getattr(lowercase_,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__(lowercase_,len(self.labels		),self.mode		)


    def 						snake_case__					(				self			:	int,**lowercase_			:	str		)->   Optional[Any]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	int,lowercase_			:	Optional[Any],lowercase_			:	str		)->   List[str]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self.hparams
        for mode in ["train", "dev", "test"]:
            A__   				=       self._feature_file(lowercase_		)
            if os.path.exists(lowercase_		) and not args.overwrite_cache:
                logger.info('Loading features from cached file %s',lowercase_		)
                A__   				=       torch.load(lowercase_		)
            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,lowercase_		)
                A__   				=       self.token_classification_task.convert_examples_to_features(
                    lowercase_,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=lowercase_,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',lowercase_		)
                torch.save(lowercase_,lowercase_		)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	bool = False		)->   DataLoader:





        '''simple docstring'''







        A__   				=       self._feature_file(lowercase_		)
        logger.info('Loading features from cached file %s',lowercase_		)
        A__   				=       torch.load(lowercase_		)
        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(lowercase_,lowercase_,lowercase_,lowercase_		),batch_size=lowercase_		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Tuple,lowercase_			:	Dict		)->   Optional[Any]:





        '''simple docstring'''







        """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(**lowercase_		)
        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 						snake_case__					(				self			:	Tuple,lowercase_			:	Union[str, Any]		)->   Any:





        '''simple docstring'''







        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(lowercase_,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(lowercase_,lowercase_		),
            'precision': precision_score(lowercase_,lowercase_		),
            'recall': recall_score(lowercase_,lowercase_		),
            'f1': fa_score(lowercase_,lowercase_		),
        }

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


    def 						snake_case__					(				self			:	Any,lowercase_			:	Optional[int]		)->   Tuple:





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__				,						A__				,						A__   				=       self._eval_end(lowercase_		)

        # 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 						snake_case__					(				lowercase_			:	int,lowercase_			:	Optional[Any]		)->   Any:





        '''simple docstring'''







        BaseTransformer.add_model_specific_args(lowercase_,lowercase_		)
        parser.add_argument(
            '--task_type',default='NER',type=lowercase_,help='Task type to fine tune in training (e.g. NER, POS, etc)'		)
        parser.add_argument(
            '--max_seq_length',default=1_2_8,type=lowercase_,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=lowercase_,help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.',)
        parser.add_argument(
            '--gpus',default=0,type=lowercase_,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__":
     lowercase_			=  argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowercase_			=  NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowercase_			=  parser.parse_args()
     lowercase_			=  NERTransformer(args)
     lowercase_			=  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
          lowercase_			=  sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
          lowercase_			=  model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)




from collections.abc import Sequence
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Sequence[int] | None = None				)							->				int:





    '''simple docstring'''






    if nums is None or not nums:
        raise ValueError('Input sequence should not be empty'				)

    A__   				=       nums[0]
    for i in range(1   ,      len(SCREAMING_SNAKE_CASE__				)				):
        A__   				=       nums[i]
        A__   				=       max(SCREAMING_SNAKE_CASE__   ,      ans + num   ,      SCREAMING_SNAKE_CASE__				)

    return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     lowercase_			=  int(input("Enter number of elements : ").strip())
     lowercase_			=  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
     print(max_subsequence_sum(array))

