# coding=utf-8
# 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.
"""
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss.
"""


import logging

import transformers

logging.basicConfig(filename='run_language_modelling.log', level=logging.WARN)

import math
import os
from dataclasses import dataclass, field
from typing import Optional
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader, DistributedSampler
from tqdm.auto import tqdm, trange
from typing import Tuple, Callable, Dict
from packaging import version

from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    HfArgumentParser,
    LineByLineTextDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
    PreTrainedModel,
    DataCollator,
    EvalPrediction
)

from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, TrainOutput
from torch.cuda.amp import GradScaler, autocast

logger = logging.getLogger(__name__)


MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

longformer_finetune_conf = {
    "longformer": {
        "embeddings": {
            "finetune": False
        },
        "encoder": {
            "finetune": False,
            "layer": {
                "6": {
                    "finetune": False,
                },
                "7": {
                    "finetune": True,
                },
                "8": {
                    "finetune": True,
                },
                "9": {
                    "finetune": True,
                },
                "10": {
                    "finetune": True,
                },
                "11": {
                    "finetune": True,
                },
            }
        },
        "pooler": {
            "finetune": True,
        },
        "finetune": False,
    },
    "lm_head": {
        "finetune": True,
    },

}

def group_wise_finetune(model, group_finetune_conf: Dict, path=""):
    """
    Copied from ..training/model_params.py
    :param model:
    :param group_finetune_conf:
    :param path:
    :return:
    """
    assert type(group_finetune_conf) == dict
    nms = []
    for kl, vl in group_finetune_conf.items():
        assert type(kl) == str
        assert type(vl) == dict or type(vl) == float or type(vl) == int or type(vl) == bool

        if type(vl) == dict:
            assert hasattr(model, kl)
            names = group_wise_finetune(getattr(model, kl), vl, path=path + kl + ".")
            names = list(map(lambda n: kl + "." + n, names))
            nms.extend(names)

    primitives = {kk: vk for kk, vk in group_finetune_conf.items() if type(vk) == bool}
    remaining_params = [(k, p) for k, p in model.named_parameters() if k not in nms]
    if len(remaining_params) > 0 and "finetune" in primitives:
        names, params = zip(*remaining_params)
        nms.extend(names)
        finetune = primitives["finetune"]
        assert type(finetune) == bool
        for p in params:
            p.requires_grad = finetune

    return nms


class TorchAmpTrainer(Trainer):
    def __init__(
            self,
            model: PreTrainedModel,
            args: TrainingArguments,
            data_collator: Optional[DataCollator] = None,
            train_dataset: Optional[Dataset] = None,
            eval_dataset: Optional[Dataset] = None,
            compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
            prediction_loss_only=False,
            tb_writer: Optional["SummaryWriter"] = None,
            optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = None,
            scaler: GradScaler = None,
    ):
        super().__init__(model, args, data_collator, train_dataset, eval_dataset, compute_metrics, prediction_loss_only, tb_writer, optimizers)
        self.scaler = scaler if scaler is not None else GradScaler()

    def _training_step(
        self, model: nn.Module, inputs: Dict[str, torch.Tensor], optimizer: torch.optim.Optimizer
    ) -> float:
        scaler = self.scaler
        model.train()
        for k, v in inputs.items():
            inputs[k] = v.to(self.args.device)
        with autocast():
            outputs = model(**inputs)
        loss = outputs[0]  # model outputs are always tuple in transformers (see doc)

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training
        if self.args.gradient_accumulation_steps > 1:
            loss = loss / self.args.gradient_accumulation_steps

        scaled_loss = scaler.scale(loss)
        scaled_loss.backward()

        return loss.item()

    def train(self, model_path: Optional[str] = None):
        """
        Main training entry point.

        Args:
            model_path:
                (Optional) Local path to model if model to train has been instantiated from a local path
                If present, we will try reloading the optimizer/scheduler states from there.
        """
        train_dataloader = self.get_train_dataloader()
        if self.args.max_steps > 0:
            t_total = self.args.max_steps
            num_train_epochs = (
                self.args.max_steps // (len(train_dataloader) // self.args.gradient_accumulation_steps) + 1
            )
        else:
            t_total = int(len(train_dataloader) // self.args.gradient_accumulation_steps * self.args.num_train_epochs)
            num_train_epochs = self.args.num_train_epochs

        optimizer, scheduler = self.get_optimizers(num_training_steps=t_total)
        scaler = self.scaler
        # Check if saved optimizer or scheduler states exist
        if (
            model_path is not None
            and os.path.isfile(os.path.join(model_path, "optimizer.pt"))
            and os.path.isfile(os.path.join(model_path, "scheduler.pt"))
            and os.path.isfile(os.path.join(model_path, "scaler.pt"))
        ):
            # Load in optimizer and scheduler states
            optimizer.load_state_dict(
                torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device)
            )
            scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
            scaler.load_state_dict(torch.load(os.path.join(model_path, "scaler.pt")))

        model = self.model

        # multi-gpu training (should be after apex fp16 initialization)
        if self.args.n_gpu > 1:
            model = torch.nn.DataParallel(model)

        # Distributed training (should be after apex fp16 initialization)
        if self.args.local_rank != -1:
            model = torch.nn.parallel.DistributedDataParallel(
                model,
                device_ids=[self.args.local_rank],
                output_device=self.args.local_rank,
                find_unused_parameters=True,
            )

        if self.tb_writer is not None:
            self.tb_writer.add_text("args", self.args.to_json_string())
            self.tb_writer.add_hparams(self.args.to_sanitized_dict(), metric_dict={})

        # Train!

        total_train_batch_size = (
            self.args.train_batch_size
            * self.args.gradient_accumulation_steps
            * (torch.distributed.get_world_size() if self.args.local_rank != -1 else 1)
        )
        logger.info("***** Running training *****")
        logger.info("  Num examples = %d", self.num_examples(train_dataloader))
        logger.info("  Num Epochs = %d", num_train_epochs)
        logger.info("  Instantaneous batch size per device = %d", self.args.per_device_train_batch_size)
        logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size)
        logger.info("  Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
        logger.info("  Total optimization steps = %d", t_total)

        self.global_step = 0
        self.epoch = 0
        epochs_trained = 0
        steps_trained_in_current_epoch = 0
        # Check if continuing training from a checkpoint
        if model_path is not None:
            # set global_step to global_step of last saved checkpoint from model path
            try:
                self.global_step = int(model_path.split("-")[-1].split("/")[0])
                epochs_trained = self.global_step // (len(train_dataloader) // self.args.gradient_accumulation_steps)
                steps_trained_in_current_epoch = self.global_step % (
                    len(train_dataloader) // self.args.gradient_accumulation_steps
                )

                logger.info("  Continuing training from checkpoint, will skip to saved global_step")
                logger.info("  Continuing training from epoch %d", epochs_trained)
                logger.info("  Continuing training from global step %d", self.global_step)
                logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
            except ValueError:
                self.global_step = 0
                logger.info("  Starting fine-tuning.")

        tr_loss = 0.0
        logging_loss = 0.0
        model.zero_grad()
        train_iterator = trange(
            epochs_trained, int(num_train_epochs), desc="Epoch", disable=not self.is_local_master()
        )
        for epoch in train_iterator:
            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                train_dataloader.sampler.set_epoch(epoch)

            epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=not self.is_local_master())

            for step, inputs in enumerate(epoch_iterator):

                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    continue


                tr_loss += self._training_step(model, inputs, optimizer)

                if (step + 1) % self.args.gradient_accumulation_steps == 0 or (
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    len(epoch_iterator) <= self.args.gradient_accumulation_steps
                    and (step + 1) == len(epoch_iterator)
                ):

                    scaler.unscale_(optimizer)
                    torch.nn.utils.clip_grad_norm_(model.parameters(), self.args.max_grad_norm)
                    scaler.step(optimizer)
                    scaler.update()
                    scheduler.step()
                    model.zero_grad()
                    self.global_step += 1
                    self.epoch = epoch + (step + 1) / len(epoch_iterator)

                    if (self.args.logging_steps > 0 and self.global_step % self.args.logging_steps == 0) or (
                        self.global_step == 1 and self.args.logging_first_step
                    ):
                        logs: Dict[str, float] = {}
                        logs["loss"] = (tr_loss - logging_loss) / self.args.logging_steps
                        # backward compatibility for pytorch schedulers
                        logs["learning_rate"] = (
                            scheduler.get_last_lr()[0]
                            if version.parse(torch.__version__) >= version.parse("1.4")
                            else scheduler.get_lr()[0]
                        )
                        logging_loss = tr_loss

                        self._log(logs)

                        if self.args.evaluate_during_training:
                            self.evaluate()

                    if self.args.save_steps > 0 and self.global_step % self.args.save_steps == 0:
                        # In all cases (even distributed/parallel), self.model is always a reference
                        # to the model we want to save.
                        if hasattr(model, "module"):
                            assert model.module is self.model
                        else:
                            assert model is self.model
                        # Save model checkpoint
                        output_dir = os.path.join(self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.global_step}")

                        self.save_model(output_dir)

                        if self.is_world_master():
                            self._rotate_checkpoints()

                        if self.is_world_master():
                            torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
                            torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
                            torch.save(scaler.state_dict(), os.path.join(output_dir, "scaler.pt"))

                if self.args.max_steps > 0 and self.global_step > self.args.max_steps:
                    epoch_iterator.close()
                    break
            if self.args.max_steps > 0 and self.global_step > self.args.max_steps:
                train_iterator.close()
                break

        if self.tb_writer:
            self.tb_writer.close()

        logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
        return TrainOutput(self.global_step, tr_loss / self.global_step)



@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
    """

    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={
            "help": "The model checkpoint for weights initialization. Leave None if you want to train a model from scratch."
        },
    )
    model_type: Optional[str] = field(
        default=None,
        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
    )
    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"}
    )
    apply_finetune_config: bool = field(
        default=False,
        metadata={
            "help": "Fine tuning config to be applied so less layers are trained."
        },
    )


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

    train_data_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a text file)."}
    )
    eval_data_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
    )
    line_by_line: bool = field(
        default=False,
        metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
    )

    mlm: bool = field(
        default=False, metadata={"help": "Train with masked-language modeling loss instead of language modeling."}
    )
    mlm_probability: float = field(
        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
    )

    block_size: int = field(
        default=-1,
        metadata={
            "help": "Optional input sequence length after tokenization."
            "The training dataset will be truncated in block of this size for training."
            "Default to the model max input length for single sentence inputs (take into account special tokens)."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )


def get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):
    file_path = args.eval_data_file if evaluate else args.train_data_file
    if args.line_by_line:
        return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
    else:
        return TextDataset(
            tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache
        )


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.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if data_args.eval_data_file is None and training_args.do_eval:
        raise ValueError(
            "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
            "or remove the --do_eval argument."
        )

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

    # Set seed
    set_seed(training_args.seed)

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

    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,"
            "and load it from here, using --tokenizer_name"
        )

    if model_args.model_name_or_path:
        model = AutoModelWithLMHead.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,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelWithLMHead.from_config(config)

    if model_args.apply_finetune_config:
        print("Apply Finetune conf", longformer_finetune_conf)
        if "longformer" in str(type(model)).lower():
            group_wise_finetune(model, longformer_finetune_conf)

    model.resize_token_embeddings(len(tokenizer))

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
        raise ValueError(
            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "
            "flag (masked language modeling)."
        )

    if data_args.block_size <= 0:
        data_args.block_size = tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
        data_args.block_size = min(data_args.block_size, tokenizer.max_len)

    # Get datasets

    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
    eval_dataset = get_dataset(data_args, tokenizer=tokenizer, evaluate=True) if training_args.do_eval else None
    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
    )

    # Initialize our Trainer
    trainer = TorchAmpTrainer(
        model=model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        prediction_loss_only=True,
    )

    # Training
    if training_args.do_train:
        model_path = (
            model_args.model_name_or_path
            if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path)
            else None
        )
        trainer.train(model_path=model_path)
        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 ***")

        eval_output = trainer.evaluate()

        perplexity = math.exp(eval_output["eval_loss"])
        result = {"perplexity": perplexity}

        output_eval_file = os.path.join(training_args.output_dir, "eval_results_lm.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key in sorted(result.keys()):
                    logger.info("  %s = %s", key, str(result[key]))
                    writer.write("%s = %s\n" % (key, str(result[key])))

        results.update(result)

    return results


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


if __name__ == "__main__":
    main()
