# Copyright © 2024 Apple Inc.

import json
import types
from pathlib import Path
from typing import Any, Dict, List

from transformers import PreTrainedTokenizer


class TextDataset:
    """
    Light-weight wrapper to hold a dataset.
    """

    def __init__(
        self,
        data: List[Dict[str, str]],
        tokenizer: PreTrainedTokenizer,
        text_key: str = "text",
    ):
        self._data = data
        self.tokenizer = tokenizer
        self.text_key = text_key

    def process(self, d):
        d = self.tokenizer.encode(d[self.text_key])
        if d[-1] != self.tokenizer.eos_token_id:
            d.append(self.tokenizer.eos_token_id)
        return (d, 0)

    def __getitem__(self, idx: int):
        return self._data[idx]

    def __len__(self):
        return len(self._data)


class ChatDataset:
    """
    A dataset for chat data in the format of {"messages": [...]}
    https://platform.openai.com/docs/guides/supervised-fine-tuning#formatting-your-data
    """

    def __init__(
        self,
        data: List[Dict[str, str]],
        tokenizer: PreTrainedTokenizer,
        chat_key: str = "messages",
        mask_prompt: bool = False,
    ):
        self._data = data
        self.chat_key = chat_key
        self.mask_prompt = mask_prompt
        self.tokenizer = tokenizer

    def process(self, d):
        messages = d[self.chat_key]
        tools = d.get("tools", None)
        tokens = self.tokenizer.apply_chat_template(
            messages,
            tools=tools,
            return_dict=False,
        )
        if self.mask_prompt:
            add_generation_prompt = messages[-1].get("role") == "assistant"
            offset = len(
                self.tokenizer.apply_chat_template(
                    messages[:-1],
                    tools=tools,
                    add_generation_prompt=add_generation_prompt,
                    return_dict=False,
                )
            )
            return (tokens, offset)
        else:
            return (tokens, 0)

    def __getitem__(self, idx: int):
        return self._data[idx]

    def __len__(self):
        return len(self._data)


class CompletionsDataset:
    """
    A dataset for prompt-completion data in the format of {"prompt": ..., "completion": ...}
    or using user-provided keys for prompt and completion values
    https://platform.openai.com/docs/guides/fine-tuning/example-format
    """

    def __init__(
        self,
        data: List[Dict[str, str]],
        tokenizer: PreTrainedTokenizer,
        prompt_key: str,
        completion_key: str,
        mask_prompt: bool,
    ):
        self._data = data
        self.prompt_key = prompt_key
        self.completion_key = completion_key
        self.mask_prompt = mask_prompt
        self.tokenizer = tokenizer

    def process(self, d):
        tools = d.get("tools", None)
        messages = [
            {"role": "user", "content": d[self.prompt_key]},
            {"role": "assistant", "content": d[self.completion_key]},
        ]
        tokens = self.tokenizer.apply_chat_template(
            messages, tools=tools, return_dict=False
        )
        if self.mask_prompt:
            offset = len(
                self.tokenizer.apply_chat_template(
                    messages[:-1],
                    tools=tools,
                    add_generation_prompt=True,
                    return_dict=False,
                )
            )
            return (tokens, offset)

        return (tokens, 0)

    def __getitem__(self, idx: int):
        return self._data[idx]

    def __len__(self):
        return len(self._data)


class ConcatenatedDataset:
    def __init__(self, data: List[Any]):
        self._data = data
        self._len = sum(len(d) for d in self._data)

    def __getitem__(self, idx: int):
        for data_idx, data in enumerate(self._data):
            j = idx - len(data)
            if j < 0:
                break
            idx = j
        datum = data[idx]
        datum["_dataset"] = data_idx
        return datum

    def process(self, d):
        return self._data[d["_dataset"]].process(d)

    def __len__(self):
        return self._len


class CacheDataset:
    def __init__(self, data: Any):
        self._data = data
        self._proc_data = [None] * len(data)

    def itemlen(self, idx: int):
        return len(self._data[idx])

    def __getitem__(self, idx: int):
        if self._proc_data[idx] is None:
            self._proc_data[idx] = self._data.process(self._data[idx])
        return self._proc_data[idx]

    def __len__(self):
        return len(self._data)


def create_dataset(
    data,
    tokenizer: PreTrainedTokenizer,
    config,
):
    mask_prompt = getattr(config, "mask_prompt", False)
    prompt_feature = getattr(config, "prompt_feature", "prompt")
    text_feature = getattr(config, "text_feature", "text")
    completion_feature = getattr(config, "completion_feature", "completion")
    chat_feature = getattr(config, "chat_feature", "messages")
    sample = data[0]
    if prompt_feature in sample and completion_feature in sample:
        return CompletionsDataset(
            data, tokenizer, prompt_feature, completion_feature, mask_prompt
        )
    elif chat_feature in sample:
        return ChatDataset(
            data, tokenizer, chat_key=chat_feature, mask_prompt=mask_prompt
        )
    elif text_feature in sample:
        if mask_prompt:
            raise ValueError("Prompt masking not supported for text dataset.")
        return TextDataset(data, tokenizer, text_key=text_feature)
    else:
        raise ValueError(
            "Unsupported data format, check the supported formats here:\n"
            "https://github.com/ml-explore/mlx-lm/blob/main/mlx_lm/LORA.md#Data."
        )


def load_local_dataset(
    data_path: Path,
    tokenizer: PreTrainedTokenizer,
    config,
):
    def load_subset(path):
        if not path.exists():
            return []
        with open(path, "r") as fid:
            data = [json.loads(l) for l in fid]
        return create_dataset(data, tokenizer, config)

    names = ("train", "valid", "test")
    train, valid, test = [load_subset(data_path / f"{n}.jsonl") for n in names]
    return train, valid, test


def load_hf_dataset(
    data_id: str,
    tokenizer: PreTrainedTokenizer,
    config,
):
    from datasets import exceptions, load_dataset

    try:
        dataset = load_dataset(data_id)

        names = ("train", "valid", "test")

        train, valid, test = [
            (
                create_dataset(dataset[n], tokenizer, config)
                if n in dataset.keys()
                else []
            )
            for n in names
        ]

    except exceptions.DatasetNotFoundError:
        raise ValueError(f"Not found Hugging Face dataset: {data_id} .")

    return train, valid, test


def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
    import datasets

    def create_hf_dataset(dataset_name, config, split, hf_config):
        ds = datasets.load_dataset(
            dataset_name,
            split=split,
            **hf_config,
        )
        return create_dataset(ds, tokenizer, config)

    dataset_collection = args.hf_dataset
    if isinstance(dataset_collection, dict):
        dataset_collection = [dataset_collection]

    collection = []
    for ds in dataset_collection:
        ds_path = ds["path"]
        print(f"Loading Hugging Face dataset {ds_path}.")
        ds["mask_prompt"] = getattr(args, "mask_prompt", False)
        config = types.SimpleNamespace(**ds)
        hf_config = ds.get("config", {})
        if args.train:
            train_split = ds.get("train_split", "train[:80%]")
            valid_split = ds.get("valid_split", "train[-10%:]")
            train = create_hf_dataset(
                ds_path,
                config,
                train_split,
                hf_config,
            )
            valid = create_hf_dataset(
                ds_path,
                config,
                valid_split,
                hf_config,
            )
        else:
            train, valid = [], []

        if args.test:
            test_split = ds.get("test_split")
            test = create_hf_dataset(
                ds_path,
                config,
                test_split,
                hf_config,
            )
        else:
            test = []

        collection.append((train, valid, test))

    if len(collection) == 1:
        return collection[0]

    # Otherwise concatenate them
    return tuple(map(ConcatenatedDataset, zip(*collection)))


def load_dataset(args, tokenizer: PreTrainedTokenizer):
    if getattr(args, "hf_dataset", False):
        train, valid, test = load_custom_hf_dataset(args, tokenizer)
    else:
        data_path = Path(args.data)
        if data_path.exists():
            train, valid, test = load_local_dataset(data_path, tokenizer, args)
        else:
            print(f"Loading Hugging Face dataset {args.data}.")
            train, valid, test = load_hf_dataset(args.data, tokenizer, args)

    if args.train and len(train) == 0:
        raise ValueError(
            "Training set not found or empty. Must provide training set for fine-tuning."
        )
    if args.train and len(valid) == 0:
        print(
            "Warning: Validation set not found or empty. Training will proceed without validation."
        )
    if args.test and len(test) == 0:
        raise ValueError(
            "Test set not found or empty. Must provide test set for evaluation."
        )
    return train, valid, test