"""
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, CTRL, BERT, RoBERTa, XLNet).
GPT, GPT-2 and CTRL are fine-tuned using a causal language modeling (CLM) loss. BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss. XLNet is fine-tuned using a permutation language modeling (PLM) loss.
"""


import logging
import math
import os
from dataclasses import dataclass, field
from typing import Optional
import time
import torch

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


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)


@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"}
    )


@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"}
    )
    plm_probability: float = field(
        default=1 / 6,
        metadata={
            "help": "Ratio of length of a span of masked tokens to surrounding context length for permutation language modeling."
        },
    )
    max_span_length: int = field(
        default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}
    )

    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"}
    )


class ParLineByLineTextDataset(LineByLineTextDataset):
    """
    This will be superseded by a framework-agnostic approach
    soon.
    """

    def __init__(self, tokenizer: PreTrainedTokenizer, file_path: str, block_size: int):
        assert os.path.isfile(file_path), f"Input file path {file_path} not found"
        logger.info("Creating features from dataset file at %s", file_path)

        st_time = time.time()
        with open(file_path, encoding="utf-8") as f:
            lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]

        print(time.time() - st_time)

        self.examples = [None] * len(lines)
        self.tokenizer = tokenizer
        self.block_size = block_size
        self.lines = lines

    def __getitem__(self, i) -> torch.Tensor:
        if self.examples[i] is None:
            self.examples[i] = self.tokenizer(self.lines[i], add_special_tokens=True, truncation=True, max_length=self.block_size)["input_ids"]
        return torch.tensor(self.examples[i], dtype=torch.long)


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 ParLineByLineTextDataset(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)

    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

    if config.model_type == "xlnet":
        data_collator = DataCollatorForPermutationLanguageModeling(
            tokenizer=tokenizer, plm_probability=data_args.plm_probability, max_span_length=data_args.max_span_length,
        )
    else:
        data_collator = DataCollatorForLanguageModeling(
            tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability
        )

    # Initialize our Trainer
    trainer = Trainer(
        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()
