



"""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


lowercase__		:				Union[str, Any]          =	logging.getLogger(__name__)







class       UpperCamelCase__   (	__UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     'token-classification'



      def __init__(      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        if type(SCREAMING_SNAKE_CASE_       ) == dict:
          lowerCAmelCase_   :				str								= Namespace(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= import_module('tasks'       )
        try:
          lowerCAmelCase_   :				Any								= getattr(SCREAMING_SNAKE_CASE_    ,			hparams.task_type       )
          lowerCAmelCase_   :				TokenClassificationTask								= 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_   :				int								= self.token_classification_task.get_labels(hparams.labels       )
        lowerCAmelCase_   :				List[Any]								= CrossEntropyLoss().ignore_index
        super().__init__(SCREAMING_SNAKE_CASE_    ,			len(self.labels       )    ,			self.mode       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        return self.model(**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				List[Any]								= {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
        if self.config.model_type != "distilbert":
          lowerCAmelCase_   :				Tuple								= (
              batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
          )  # XLM and RoBERTa don"t use token_type_ids

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Optional[int]								= self.hparams
        for mode in ["train", "dev", "test"]:
          lowerCAmelCase_   :				str								= self._feature_file(SCREAMING_SNAKE_CASE_       )
          if os.path.exists(SCREAMING_SNAKE_CASE_       ) and not args.overwrite_cache:
            logger.info('Loading features from cached file %s'    ,			SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Tuple								= torch.load(SCREAMING_SNAKE_CASE_       )
          else:
            logger.info('Creating features from dataset file at %s'    ,			args.data_dir       )
            lowerCAmelCase_   :				Union[str, Any]								= self.token_classification_task.read_examples_from_file(args.data_dir    ,			SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				int								= self.token_classification_task.convert_examples_to_features(
                SCREAMING_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=SCREAMING_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'    ,			SCREAMING_SNAKE_CASE_       )
            torch.save(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        """Compute validation""" ""
        lowerCAmelCase_   :				List[Any]								= {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
        if self.config.model_type != "distilbert":
          lowerCAmelCase_   :				List[Any]								= (
              batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
          )  # XLM and RoBERTa don"t use token_type_ids
        lowerCAmelCase_   :				int								= self(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= outputs[:2]
        lowerCAmelCase_   :				List[str]								= logits.detach().cpu().numpy()
        lowerCAmelCase_   :				str								= inputs["""labels"""].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Dict       ):
        lowerCAmelCase_   :				Union[str, Any]								= torch.stack([x['val_loss'] for x in outputs]       ).mean()
        lowerCAmelCase_   :				List[Any]								= np.concatenate([x['pred'] for x in outputs]    ,			axis=0       )
        lowerCAmelCase_   :				Union[str, Any]								= np.argmax(SCREAMING_SNAKE_CASE_    ,			axis=2       )
        lowerCAmelCase_   :				str								= np.concatenate([x['target'] for x in outputs]    ,			axis=0       )

        lowerCAmelCase_   :				Union[str, Any]								= dict(enumerate(self.labels       )       )
        lowerCAmelCase_   :				Optional[int]								= [[] for _ in range(out_label_ids.shape[0]       )]
        lowerCAmelCase_   :				Union[str, Any]								= [[] 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_   :				List[str]								= {
            """val_loss""": val_loss_mean,
            """accuracy_score""": accuracy_score(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ),
            """precision""": precision_score(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ),
            """recall""": recall_score(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ),
            """f1""": fa_score(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ),
        }

        lowerCAmelCase_   :				Dict								= dict(results.items()       )
        lowerCAmelCase_   :				Optional[Any]								= results
        return ret, preds_list, out_label_list



      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        # when stable
        lowerCAmelCase_   :				int								= self._eval_end(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= ret["""log"""]
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        # updating to test_epoch_end instead of deprecated test_end
        lowerCAmelCase_   :				str								= self._eval_end(SCREAMING_SNAKE_CASE_       )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        lowerCAmelCase_   :				Union[str, Any]								= 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      SCREAMING_SNAKE_CASE__ (      SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        # Add NER specific options
        BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
        parser.add_argument(
            '--task_type'    ,			default='NER'    ,			type=SCREAMING_SNAKE_CASE_    ,			help='Task type to fine tune in training (e.g. NER, POS, etc)'       )
        parser.add_argument(
            '--max_seq_length'    ,			default=1_2_8    ,			type=SCREAMING_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=SCREAMING_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=SCREAMING_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__":
     lowercase__		:				Tuple          =	argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowercase__		:				int          =	NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowercase__		:				Optional[Any]          =	parser.parse_args()
     lowercase__		:				str          =	NERTransformer(args)
     lowercase__		:				str          =	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__		:				List[Any]          =	sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
          lowercase__		:				int          =	model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)










'''simple docstring'''




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


if __name__ == "__main__":
						fire.Fire(save_len_file)