<reponame>oeg-upm/valkyr-ie-py<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 29 11:44:08 2021

@author: Pablo
"""



import logging
import os
import sys
import pdb
import subprocess

from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import f1_score, precision_score, recall_score
from torch import nn

from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoModel,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from data import NerDataset, Split, get_labels

logger = logging.getLogger(__name__)

from models import DataTrainingArguments,ModelArguments


def main(parser,training_args,model_args,data_args):
    

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Prepare CONLL-2003 task
    labels = get_labels(data_args.labels)
    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
    num_labels = len(labels)

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

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
    )
    tokenizer = 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,
        use_fast=model_args.use_fast,
    )
    model = AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )
    '''
    model_to_save = AutoModel.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )
    model_to_save.save_pretrained(training_args.output_dir)
    tokenizer.save_pretrained(training_args.output_dir)
    import pdb; pdb.set_trace()
    '''

    # Get datasets
    train_dataset = (
        NerDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.train,
        )
        if training_args.do_train
        else None
    )
    eval_dataset = (
        NerDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.dev,
        )
        if training_args.do_eval
        else None
    )

    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
        preds = np.argmax(predictions, axis=2)

        batch_size, seq_len = preds.shape

        out_label_list = [[] for _ in range(batch_size)]
        preds_list = [[] for _ in range(batch_size)]
        
        for i in range(batch_size):
            for j in range(seq_len):
                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                    out_label_list[i].append(label_map[label_ids[i][j]])
                    preds_list[i].append(label_map[preds[i][j]])

        return preds_list, out_label_list

    def compute_metrics(p: EvalPrediction) -> Dict:
        preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
        
        return {
            "precision": precision_score(out_label_list, preds_list),
            "recall": recall_score(out_label_list, preds_list),
            "f1": f1_score(out_label_list, preds_list),
        }

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
    )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
        )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()
        
        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in result.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

            results.update(result)
    
    
    # Predict
    if training_args.do_predict:
        test_dataset = NerDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.test,
        )

        predictions, label_ids, metrics = trainer.predict(test_dataset)
        preds_list, _ = align_predictions(predictions, label_ids)
        
        # Save predictions
        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
        if trainer.is_world_master():
            with open(output_test_results_file, "w") as writer:
                logger.info("***** Test results *****")
                for key, value in metrics.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

        
        output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
        if trainer.is_world_master():
            with open(output_test_predictions_file, "w") as writer:
                with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
                    example_id = 0
                    for line in f:
                        if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                            writer.write(line)
                            if not preds_list[example_id]:
                                example_id += 1
                        elif preds_list[example_id]:
                            entity_label = preds_list[example_id].pop(0)
                            if entity_label == 'O':
                                output_line = line.split()[0] + " " + entity_label + "\n"
                            else:
                                output_line = line.split()[0] + " " + entity_label[0] + "\n"
                            # output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
                            writer.write(output_line)
                        else:
                            logger.warning(
                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
                            )
            

    return results


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()
    
    


import argparse


    
if __name__ == "__main__":
  
  
    parser = argparse.ArgumentParser(description='Valkyr-ie ner model trainer')
    parser.add_argument('-model', '--model_base', type=str, help='Model base')
    parser.add_argument('-data', '--data_dir', type=str, help='Data directory')
    parser.add_argument('-out', '--output_dir', type=str, help='Output directory name')
    parser.add_argument('-epochs', '--epochs', type=int, help='Epochs')
    parser.add_argument('-batch', '--batch', type=int, help='Batch')

    
    args = parser.parse_args()
    
    

    marg = ModelArguments(model_name_or_path=args.model_base)

    darg = DataTrainingArguments(data_dir=args.data_dir,labels=args.data_dir+'/labels.txt',max_seq_length=128)
    
    
    targ = TrainingArguments(output_dir=args.output_dir,overwrite_output_dir=True,do_eval=True,do_train=True,seed=1,save_steps=1000,num_train_epochs=args.epochs,per_device_train_batch_size=args.batch)



  # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((marg, darg, targ))
    main(parser,targ,marg,darg)

