# -*- coding: utf-8 -*-
import json
import os
import jieba
import dataclasses as dc
import functools
from collections.abc import Callable, Mapping, Sequence
from pathlib import Path
from typing import Annotated, Any, Optional, Union
import numpy as np
import ruamel.yaml as yaml
import torch
import typer
from datasets import Dataset, NamedSplit, Split
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from peft import PeftConfig, get_peft_config, get_peft_model
from rouge_chinese import Rouge
from torch import nn
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    EvalPrediction,
    GenerationConfig,
    PreTrainedTokenizer,
    Seq2SeqTrainingArguments,
)
from transformers import DataCollatorForSeq2Seq as _DataCollatorForSeq2Seq
from transformers import Seq2SeqTrainer as _Seq2SeqTrainer

app = typer.Typer(pretty_exceptions_show_locals=False)


class DataCollatorForSeq2Seq(_DataCollatorForSeq2Seq):
    def __call__(self, features, return_tensors=None):
        output_ids = ([feature['output_ids'] for feature in features] if 'output_ids' in features[0].keys() else None)
        if output_ids is not None:
            max_output_length = max(len(out) for out in output_ids)
            if self.pad_to_multiple_of is not None:
                max_output_length = (
                        (
                                max_output_length + self.pad_to_multiple_of - 1) //
                        self.pad_to_multiple_of * self.pad_to_multiple_of
                )
            for feature in features:
                remainder = [self.tokenizer.pad_token_id] * (
                        max_output_length - len(feature['output_ids'])
                )
                if isinstance(feature['output_ids'], list):
                    feature['output_ids'] = feature['output_ids'] + remainder
                else:
                    feature['output_ids'] = np.concatenate(
                        [feature['output_ids'], remainder]
                    ).astype(np.int64)
        return super().__call__(features, return_tensors)


class Seq2SeqTrainer(_Seq2SeqTrainer):
    def prediction_step(
            self,
            model: nn.Module,
            inputs: dict[str, Any],
            prediction_loss_only: bool,
            ignore_keys=None,
            **gen_kwargs,
    ) -> tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        if self.args.predict_with_generate:
            output_ids = inputs.pop('output_ids')
        input_ids = inputs['input_ids']
        loss, generated_tokens, labels = super().prediction_step(
            model, inputs, prediction_loss_only, ignore_keys, **gen_kwargs
        )
        generated_tokens = generated_tokens[:, input_ids.size()[1]:]
        labels = output_ids
        return loss, generated_tokens, labels


@dc.dataclass
class DataConfig(object):
    train_file: Optional[str] = None
    val_file: Optional[str] = None
    test_file: Optional[str] = None
    num_proc: Optional[int] = None

    @property
    def data_format(self) -> str:
        return Path(self.train_file).suffix

    @property
    def data_files(self) -> dict[NamedSplit, str]:
        return {
            split: data_file
            for split, data_file in zip(
                [Split.TRAIN, Split.VALIDATION, Split.TEST],
                [self.train_file, self.val_file, self.test_file],
            )
            if data_file is not None
        }


@dc.dataclass
class FinetuningConfig(object):
    data_config: DataConfig

    max_input_length: int
    max_output_length: int

    training_args: Seq2SeqTrainingArguments = dc.field(
        default_factory=lambda: Seq2SeqTrainingArguments(output_dir='./output')
    )
    peft_config: Optional[PeftConfig] = None

    def __post_init__(self):
        if not self.training_args.do_eval or self.data_config.val_file is None:
            self.training_args.do_eval = False
            self.training_args.evaluation_strategy = 'no'
            self.data_config.val_file = None
        else:
            self.training_args.per_device_eval_batch_size = (
                    self.training_args.per_device_eval_batch_size
                    or self.training_args.per_device_train_batch_size
            )

    @classmethod
    def from_dict(cls, **kwargs) -> 'FinetuningConfig':
        training_args = kwargs.get('training_args', None)
        if training_args is not None and not isinstance(
                training_args, Seq2SeqTrainingArguments
        ):
            gen_config = training_args.get('generation_config')
            # TODO: a bit hacky
            if not isinstance(gen_config, GenerationConfig):
                training_args['generation_config'] = GenerationConfig(
                    **gen_config
                )
            kwargs['training_args'] = Seq2SeqTrainingArguments(**training_args)

        data_config = kwargs.get('data_config')
        if not isinstance(data_config, DataConfig):
            kwargs['data_config'] = DataConfig(**data_config)

        peft_config = kwargs.get('peft_config', None)
        if peft_config is not None and not isinstance(peft_config, PeftConfig):
            kwargs['peft_config'] = get_peft_config(config_dict=peft_config)
        return cls(**kwargs)

    @classmethod
    def from_file(cls, path: Union[str, Path]) -> 'FinetuningConfig':
        path = Path(path)
        parser = yaml.YAML(typ='safe', pure=True)
        parser.indent(mapping=2, offset=2, sequence=4)
        parser.default_flow_style = False
        kwargs = parser.load(path)
        return cls.from_dict(**kwargs)


from datasets import load_dataset, DatasetDict, NamedSplit
from typing import Optional


def _load_datasets(
        data_dir: str,
        data_format: str,
        data_files: dict[NamedSplit, str],
        num_proc: Optional[int],
) -> DatasetDict:
    if data_format == '.jsonl':
        dataset_dct = load_dataset(
            data_dir,
            data_files=data_files,
            split=None,
            num_proc=num_proc,
        )
    else:
        raise NotImplementedError(f"Cannot load dataset in the '{data_format}' format.")
    return dataset_dct


class DataManager(object):
    def __init__(self, data_dir: str, data_config: DataConfig):
        self._num_proc = data_config.num_proc

        self._dataset_dct = _load_datasets(
            data_dir,
            data_config.data_format,
            data_config.data_files,
            self._num_proc,
        )

    def _get_dataset(self, split: NamedSplit) -> Optional[Dataset]:
        return self._dataset_dct.get(split, None)

    def get_dataset(
            self,
            split: NamedSplit,
            process_fn: Callable[[dict[str, Any]], dict[str, Any]],
            batched: bool = True,
            remove_orig_columns: bool = True,
    ) -> Optional[Dataset]:
        orig_dataset = self._get_dataset(split)
        if orig_dataset is None:
            return

        if remove_orig_columns:
            remove_columns = orig_dataset.column_names
        else:
            remove_columns = None
        return orig_dataset.map(
            process_fn,
            batched=batched,
            remove_columns=remove_columns,
            num_proc=self._num_proc,
        )


def process_message(message):
    if 'tools' in message and message['role'] == 'system':
        for tool in message['tools']:
            parameters = tool['function']['parameters']['properties']
            tool['function']['parameters']['properties'] = \
                {k: v for k, v in parameters.items() if
                 v is not None}
    elif 'tools' in message:
        del message['tools']
    return message


def process_batch(
        batch: Mapping[str, Sequence],
        tokenizer: PreTrainedTokenizer,
        max_input_length: int,
        max_output_length: int,
) -> dict[str, list]:
    batched_conv = batch['messages']
    batched_input_ids = []
    batched_labels = []

    for conv in batched_conv:
        input_ids = [151331, 151333]
        loss_masks = [False, False]
        for message in conv:
            message = process_message(message)
            loss_mask_val = False if message['role'] in ('system', 'user', 'observation') else True
            new_input_ids = tokenizer.apply_chat_template([message], tokenize=True, return_dict=False)[0][2:]
            new_loss_masks = [loss_mask_val] * len(new_input_ids)
            input_ids += new_input_ids
            loss_masks += new_loss_masks
        input_ids.append(tokenizer.eos_token_id)
        loss_masks = [False, *loss_masks]
        labels = []
        for input_id, mask in zip(input_ids, loss_masks):
            if mask:
                labels.append(input_id)
            else:
                labels.append(-100)
        max_length = max_input_length + max_output_length + 1
        batched_input_ids.append(input_ids[:max_length])
        batched_labels.append(labels[:max_length])
    return {'input_ids': batched_input_ids, 'labels': batched_labels}


def process_batch_eval(
        batch: Mapping[str, Sequence],
        tokenizer: PreTrainedTokenizer,
        max_input_length: int,
        max_output_length: int,
) -> dict[str, list]:
    batched_conv = batch['messages']
    batched_input_ids = []
    batched_output_ids = []

    for conv in batched_conv:

        input_ids = [151331, 151333]
        for message in conv:
            if len(input_ids) >= max_input_length:
                break
            else:
                message = process_message(message)
                new_input_ids = tokenizer.apply_chat_template([message], tokenize=True, return_dict=False)[0][2:]
                if message['role'] == 'assistant':
                    output_prompt, output_ids = (
                        new_input_ids[:1],
                        new_input_ids[1:],
                    )
                    output_ids.append(tokenizer.eos_token_id)
                    batched_input_ids.append(
                        input_ids[:max_input_length] + output_prompt[:1]
                    )
                    batched_output_ids.append(output_ids[:max_output_length])
                input_ids += new_input_ids
    return {'input_ids': batched_input_ids, 'output_ids': batched_output_ids}


def load_tokenizer_and_model(
        model_dir: str,
        peft_config: Optional[PeftConfig] = None,
):
    tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
    if peft_config is not None:
        model = AutoModelForCausalLM.from_pretrained(
            model_dir,
            trust_remote_code=True,
            empty_init=False,
            use_cache=False,
            torch_dtype=torch.bfloat16  # Must use BFloat 16
        )
        model = get_peft_model(model, peft_config)
        model.print_trainable_parameters()
    else:
        model = AutoModelForCausalLM.from_pretrained(
            model_dir,
            trust_remote_code=True,
            empty_init=False,
            use_cache=False,
            torch_dtype=torch.bfloat16
        )
    return tokenizer, model


def compute_metrics(eval_preds: EvalPrediction, tokenizer):
    batched_pred_ids, batched_label_ids = eval_preds
    metrics_dct = {'rouge-1': [], 'rouge-2': [], 'rouge-l': [], 'bleu-4': []}
    for pred_ids, label_ids in zip(batched_pred_ids, batched_label_ids):
        pred_txt = tokenizer.decode(pred_ids).strip()
        label_txt = tokenizer.decode(label_ids).strip()
        pred_tokens = list(jieba.cut(pred_txt))
        label_tokens = list(jieba.cut(label_txt))
        rouge = Rouge()
        scores = rouge.get_scores(' '.join(pred_tokens), ' '.join(label_tokens))
        for k, v in scores[0].items():
            metrics_dct[k].append(round(v['f'] * 100, 4))
        metrics_dct['bleu-4'].append(
            sentence_bleu([label_tokens], pred_tokens, smoothing_function=SmoothingFunction().method3))
    return {k: np.mean(v) for k, v in metrics_dct.items()}


@app.command()
def main(
        data_dir: Annotated[str, typer.Argument(help='')],
        model_dir: Annotated[
            str,
            typer.Argument(
                help='A string that specifies the model id of a pretrained model configuration hosted on huggingface.co, or a path to a directory containing a model configuration file.'
            ),
        ],
        config_file: Annotated[str, typer.Argument(help='')],
        auto_resume_from_checkpoint: str = typer.Argument(
            default='',
            help='If entered as yes, automatically use the latest save checkpoint. If it is a numerical example 12 15, use the corresponding save checkpoint. If the input is no, restart training'
        ),

):
    ft_config = FinetuningConfig.from_file(config_file)
    tokenizer, model = load_tokenizer_and_model(model_dir, peft_config=ft_config.peft_config)
    data_manager = DataManager(data_dir, ft_config.data_config)

    train_dataset = data_manager.get_dataset(
        Split.TRAIN,
        functools.partial(
            process_batch,
            tokenizer=tokenizer,
            max_input_length=ft_config.max_input_length,
            max_output_length=ft_config.max_output_length,
        ),
        batched=True,
    )
    print('train_dataset:', train_dataset)
    val_dataset = data_manager.get_dataset(
        Split.VALIDATION,
        functools.partial(
            process_batch_eval,
            tokenizer=tokenizer,
            max_input_length=ft_config.max_input_length,
            max_output_length=ft_config.max_output_length,
        ),
        batched=True,
    )
    if val_dataset is not None:
        print('val_dataset:', val_dataset)
    test_dataset = data_manager.get_dataset(
        Split.TEST,
        functools.partial(
            process_batch_eval,
            tokenizer=tokenizer,
            max_input_length=ft_config.max_input_length,
            max_output_length=ft_config.max_output_length,
        ),
        batched=True,
    )
    if test_dataset is not None:
        print('test_dataset:', test_dataset)

    model.gradient_checkpointing_enable()
    model.enable_input_require_grads()

    trainer = Seq2SeqTrainer(
        model=model,
        args=ft_config.training_args,
        data_collator=DataCollatorForSeq2Seq(
            tokenizer=tokenizer,
            padding='longest',
            return_tensors='pt',
        ),
        train_dataset=train_dataset,
        eval_dataset=val_dataset.select(list(range(50))),
        compute_metrics=functools.partial(compute_metrics, tokenizer=tokenizer),
    )

    if auto_resume_from_checkpoint.upper() == "" or auto_resume_from_checkpoint is None:
        trainer.train()
    else:
        output_dir = ft_config.training_args.output_dir
        dirlist = os.listdir(output_dir)
        checkpoint_sn = 0
        for checkpoint_str in dirlist:
            if checkpoint_str.find("eckpoint") > 0 and checkpoint_str.find("tmp") == -1:
                checkpoint = int(checkpoint_str.replace("checkpoint-", ""))
                if checkpoint > checkpoint_sn:
                    checkpoint_sn = checkpoint
        if auto_resume_from_checkpoint.upper() == "YES":
            if checkpoint_sn > 0:
                model.gradient_checkpointing_enable()
                model.enable_input_require_grads()
                checkpoint_directory = os.path.join(output_dir, "checkpoint-" + str(checkpoint_sn))
                print("resume checkpoint from  checkpoint-" + str(checkpoint_sn))
                trainer.train(resume_from_checkpoint=checkpoint_directory)
            else:
                trainer.train()
        else:
            if auto_resume_from_checkpoint.isdigit():
                if int(auto_resume_from_checkpoint) > 0:
                    checkpoint_sn = int(auto_resume_from_checkpoint)
                    model.gradient_checkpointing_enable()
                    model.enable_input_require_grads()
                    checkpoint_directory = os.path.join(output_dir, "checkpoint-" + str(checkpoint_sn))
                    print("resume checkpoint from  checkpoint-" + str(checkpoint_sn))
                    trainer.train(resume_from_checkpoint=checkpoint_directory)
            else:
                print(auto_resume_from_checkpoint,
                      "The specified checkpoint sn(" + auto_resume_from_checkpoint + ") has not been saved. Please search for the correct checkpoint in the model output directory")

    if test_dataset is not None:
        trainer.predict(test_dataset)


if __name__ == '__main__':
    app()
