# coding=utf-8
# Copyright 2021 Intel Corporation. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Finetuning the library models for sequence classification on GLUE."""
# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
# cola,qnli,qqp,sst2,wnli, mnli,mrpc,rte,stsb
import json
import logging
import os
import random
import sys
from argparse import Namespace
from pretraining.args.deepspeed_args import remove_cuda_compatibility_for_kernel_compilation
from pretraining.modeling import BertForSequenceClassification
from pretraining.configs import PretrainedBertConfig
from dataclasses import dataclass, field
from typing import Optional
import uuid

import numpy as np
import transformers
from datasets import load_dataset, load_metric
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EarlyStoppingCallback,
    EvalPrediction,
    HfArgumentParser,
    PretrainedConfig,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import SchedulerType, is_main_process
import time
import ipdb
import os

os.environ["WANDB_PROJECT"] = "huggingface"

task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}

logger = logging.getLogger(__name__)


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.

    Using `HfArgumentParser` we can turn this class
    into argparse arguments to be able to specify them on
    the command line.
    """

    task_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
    )
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
    )
    pad_to_max_length: bool = field(
        default=True,
        metadata={
            "help": "Whether to pad all samples to `max_seq_length`. "
            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
        },
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "A csv or a json file containing the training data."}
    )
    validation_file: Optional[str] = field(
        default=None, metadata={"help": "A csv or a json file containing the validation data."}
    )

    def __post_init__(self):
        if self.task_name is not None:
            self.task_name = self.task_name.lower()
            if self.task_name not in task_to_keys.keys():
                raise ValueError(
                    "Unknown task, you should pick one in " + ",".join(task_to_keys.keys())
                )
        elif self.train_file is None or self.validation_file is None:
            raise ValueError("Need either a GLUE task or a training/validation file.")
        else:
            extension = self.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            extension = self.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."


@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 huggingface.co"
        },
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={
            "help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."
        },
    )


@dataclass
class FinetuneTrainingArguments(TrainingArguments):
    group_name: Optional[str] = field(default=None, metadata={"help": "W&B group name"})
    project_name: Optional[str] = field(default=None, metadata={"help": "Project name (W&B)"})
    early_stopping_patience: Optional[int] = field(
        default=-1, metadata={"help": "Early stopping patience value (default=-1 (disable))"}
    )
    # overriding to be True, for consistency with final_eval_{metric_name}
    fp16_full_eval: bool = field(
        default=True,
        metadata={"help": "Whether to use full 16-bit precision evaluation instead of 32-bit"},
    )


def main():
    # 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.
    unique_run_id = str(uuid.uuid1())
    
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FinetuneTrainingArguments))
    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 is_main_process(training_args.local_rank) else logging.WARN,
    )

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        print("###{}###".format(data_args.task_name))
        datasets = load_dataset("glue", data_args.task_name)
        print(datasets)
    elif data_args.train_file.endswith(".csv"):
        # Loading a dataset from local csv files
        datasets = load_dataset(
            "csv",
            data_files={"train": data_args.train_file, "validation": data_args.validation_file},
        )
    else:
        # Loading a dataset from local json files
        datasets = load_dataset(
            "json",
            data_files={"train": data_args.train_file, "validation": data_args.validation_file},
        )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            label_list = datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = datasets["train"].features["label"].dtype in ["float32", "float64"]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    pretrain_run_args = json.load(open(f"{model_args.model_name_or_path}/args.json", "r"))

    def get_correct_ds_args(pretrain_run_args):
        ds_args = Namespace()

        for k, v in pretrain_run_args.items():
            setattr(ds_args, k, v)

        # to enable HF integration
        #         ds_args.huggingface = True
        return ds_args

    ds_args = get_correct_ds_args(pretrain_run_args)

    # in so, deepspeed is required
    if (
        "deepspeed_transformer_kernel" in pretrain_run_args
        and pretrain_run_args["deepspeed_transformer_kernel"]
    ):
        logger.warning("Using deepspeed_config due to kernel usage")

        remove_cuda_compatibility_for_kernel_compilation()

    if os.path.isdir(model_args.model_name_or_path):
        config = PretrainedBertConfig.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,
        )
        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,
        )
        model = BertForSequenceClassification.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,
            args=ds_args,
        )
    else:
        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,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
        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_tokenizer,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
        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,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )

    # Preprocessing the datasets
    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [
            name for name in datasets["train"].column_names if name != "label"
        ]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
        max_length = data_args.max_seq_length
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False
        max_length = None

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (
        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
        and data_args.task_name is not None
        and is_regression
    ):
        # Some have all caps in their config, some don't.
        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {i: label_name_to_id[label_list[i]] for i in range(num_labels)}
        else:
            logger.warn(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    def preprocess_function(examples):
        # Tokenize the texts
        args = (
            (examples[sentence1_key],)
            if sentence2_key is None
            else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(*args, padding=padding, max_length=max_length, truncation=True)

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [label_to_id[l] for l in examples["label"]]
        return result

    datasets = datasets.map(
        preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache
    )

    train_dataset = datasets["train"]
    eval_dataset = datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
    if data_args.task_name is not None:
        test_dataset = datasets["test_matched" if data_args.task_name == "mnli" else "test"]

    # Log a few random samples from the training set:
    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric("glue", data_args.task_name)
    # TODO: When datasets metrics include regular accuracy, make an else here and remove special branch from
    # compute_metrics

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
        if data_args.task_name is not None:
            result = metric.compute(predictions=preds, references=p.label_ids)
            if len(result) > 1:
                result["combined_score"] = np.mean(list(result.values())).item()
            return result
        elif is_regression:
            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
        else:
            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}

    try:
        import wandb

        wandb.init(
            project=training_args.project_name,
            group=training_args.group_name,
            name=training_args.run_name,
            dir="/tmp",
        )
        wandb.config.update(model_args)
        wandb.config.update(data_args)
        wandb.config.update(training_args)

    except Exception as e:
        logger.warning("W&B logger is not available, please install to get proper logging")
        logger.error(e)

    # init early stopping callback and metric to monitor
    callbacks = None
    if training_args.early_stopping_patience > 0:
        early_cb = EarlyStoppingCallback(training_args.early_stopping_patience)
        callbacks = [early_cb]

        metric_monitor = {
            "mrpc": "f1",
            "sst2": "accuracy",
            "mnli": "accuracy",
            "mnli_mismatched": "accuracy",
            "mnli_matched": "accuracy",
            "cola": "matthews_correlation",
            "stsb": "spearmanr",
            "qqp": "f1",
            "qnli": "accuracy",
            "rte": "accuracy",
            "wnli": "accuracy",
        }
        metric_to_monitor = metric_monitor[data_args.task_name]
        setattr(training_args, "metric_for_best_model", metric_to_monitor)

    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    elif training_args.fp16:
        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
    else:
        data_collator = None

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        callbacks=callbacks,
        data_collator=data_collator,
    )

    # Training
    if training_args.do_train:
        trainer.train()

    # Evaluation
    if training_args.do_eval:
        print("*** Evaluate ***")
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        try:
            wandb.run.summary.update(metrics)
            log_metrics = {}
            for k, v in metrics.items():
                log_metrics["final_" + k] = v
            print(log_metrics)
            wandb.log(log_metrics)
        except Exception as e:
            logger.warning("W&B logger is not available, please install to get proper logging")
            logger.error(e)

    if training_args.do_predict:
        logger.info("*** Test ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        test_datasets = [test_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            test_datasets.append(datasets["test_mismatched"])

        for test_dataset, task in zip(test_datasets, tasks):
            # Removing the `label` columns because it contains -1 and Trainer won't like that.
            test_dataset.remove_columns_("label")
            predictions = trainer.predict(test_dataset=test_dataset).predictions
            predictions = (
                np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
            )

            test_results_file_name = f"test_results_{task}_{unique_run_id}.txt"
            # if os.path.isdir(model_args.model_name_or_path):
            #     output_test_file = os.path.join(
            #         model_args.model_name_or_path, test_results_file_name
            #     )
            # else:
            #     output_test_file = os.path.join(training_args.output_dir, test_results_file_name)
            directory = "./predict_file/" + data_args.task_name
            if not os.path.exists(directory):
                os.makedirs(directory)
            output_test_file = os.path.join(directory, test_results_file_name)

            print(f"####### output_test_file: {output_test_file} #######")

            if trainer.is_world_process_zero():
                with open(output_test_file, "w") as writer:
                    logger.info(f"***** Test results {task} *****")
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(predictions):
                        if is_regression:
                            writer.write(f"{index}\t{item:3.3f}\n")
                        else:
                            item = label_list[item]
                            writer.write(f"{index}\t{item}\n")


if __name__ == "__main__":
    main()
