import dataclasses

import logging

import os

import sys

from dataclasses import dataclass, field

from typing import Callable, Dict, Optional

import numpy as np

from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction

from transformers import GlueDataTrainingArguments as DataTrainingArguments

from transformers import (

    HfArgumentParser,

    Trainer,

    TrainingArguments,

    set_seed,

)

from data_processor import glue_output_modes, glue_tasks_num_labels

from utils_re import REDataset, get_eval_results

from metrics import glue_compute_metrics

logger = logging.getLogger(__name__)

@dataclass

class ModelArguments:

    """

    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.

    """

    model_name_or_path: str = field(

        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}

    )

    config_name: Optional[str] = field(

        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}

    )

    tokenizer_name: Optional[str] = field(

        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}

    )

    cache_dir: Optional[str] = field(

        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}

    )

    warmup_proportion: Optional[float] = field(

        default=0.1, metadata={"help": "Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% of training."}

    )

def main():

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):

        # If we pass only one argument to the script and it's the path to a json file,

        # let's parse it to get our arguments.

        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))

    else:

        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if (

        os.path.exists(training_args.output_dir)

        and os.listdir(training_args.output_dir)

        and training_args.do_train

        and not training_args.overwrite_output_dir

    ):

        raise ValueError(

            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."

        )

    # 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,

    )

    # Set seed

    set_seed(training_args.seed)

    try:

        num_labels = glue_tasks_num_labels[data_args.task_name]

        output_mode = glue_output_modes[data_args.task_name]

    except KeyError:

        raise ValueError("Task not found: %s" % data_args.task_name)

    # Load tokenizer

    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,

    )

    # Get datasets

    train_dataset = (

        REDataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None

    )

    eval_dataset = (

        REDataset(data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)

        if training_args.do_eval

        else None

    )

    test_dataset = (

        REDataset(data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)

        if training_args.do_predict

        else None

    )

    # Load pretrained model

    # Distributed training:

    # The .from_pretrained methods guarantee that only one local process can concurrently

    # download model & vocab.

    # Currently, this code do not support distributed training.

    training_args.warmup_steps = int(model_args.warmup_proportion * (len(train_dataset) / training_args.per_device_train_batch_size) * training_args.num_train_epochs)

    logger.info("Training/evaluation parameters %s", training_args)

    config = AutoConfig.from_pretrained(

        model_args.config_name if model_args.config_name else model_args.model_name_or_path,

        num_labels=num_labels,

        finetuning_task=data_args.task_name,

        cache_dir=model_args.cache_dir,

    )

    model = AutoModelForSequenceClassification.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,

    )

    def build_compute_metrics_fn(task_name: str) -> Callable[[EvalPrediction], Dict]:

        def compute_metrics_fn(p: EvalPrediction):

            preds = np.argmax(p.predictions, axis=1)

            return glue_compute_metrics(preds, p.label_ids)

        return compute_metrics_fn

    # Initialize our Trainer

    trainer = Trainer(

        model=model,

        args=training_args,

        train_dataset=train_dataset,

        eval_dataset=eval_dataset,

        compute_metrics=build_compute_metrics_fn(data_args.task_name),

    )

    # 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

    eval_results = {}

    if training_args.do_eval:

        logger.info("*** Evaluate ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)

        eval_datasets = [eval_dataset]

        for eval_dataset in eval_datasets:

            trainer.compute_metrics = build_compute_metrics_fn(eval_dataset.args.task_name)

            eval_result = trainer.evaluate(eval_dataset=eval_dataset)

            output_eval_file = os.path.join(

                training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"

            )

            if trainer.is_world_master():

                with open(output_eval_file, "w") as writer:

                    logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))

                    for key, value in eval_result.items():

                        logger.info("  %s = %s", key, value)

                        writer.write("%s = %s\n" % (key, value))

            eval_results.update(eval_result)

    if training_args.do_predict:

        logging.info("*** Test ***")

        test_datasets = [test_dataset]

        for test_dataset in test_datasets:

            predictions = trainer.predict(test_dataset=test_dataset).predictions

            if output_mode == "classification":

                predictions = np.argmax(predictions, axis=1)

            output_test_file = os.path.join(

                training_args.output_dir,

                f"test_predictions.txt"

                )

            if trainer.is_world_master():

                with open(output_test_file, "w") as writer:

                    logger.info("***** Test results {} *****".format(test_dataset.args.task_name))

                    writer.write("index\tprediction\n")

                    for index, item in enumerate(predictions):

                        item = test_dataset.get_labels()[item]

                        writer.write("%d\t%s\n" % (index, item))

                output_label_file = os.path.join(

                    data_args.data_dir,

                    f"test_labels.tsv"

                    )

                output_test_result_file = os.path.join(

                    training_args.output_dir,

                    f"test_results.txt"

                    )

                test_result = get_eval_results(output_label_file, output_test_file)

                with open(output_test_result_file, "w") as writer:

                    for key, value in test_result.items():

                        logger.info("  %s = %s", key, value)

                        writer.write("%s = %s\n" % (key, value))

    return eval_results

if __name__ == "__main__":

    main()