import json
import re
from functools import partial
from pathlib import Path

import torch
from datasets import Dataset
from transformers import (
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorForTokenClassification,
    Trainer,
    TrainingArguments,
)

from enums import TokenLabel

MODEL_NAME = "microsoft/MiniLM-L12-H384-uncased"
MODEL_DIR = "./model"

LABEL_LIST = [label.name for label in TokenLabel]
LABEL2ID = {label: i for i, label in enumerate(LABEL_LIST)}
ID2LABEL = {i: label for i, label in enumerate(LABEL_LIST)}
VALUE_TO_NAME = {label.value: label.name for label in TokenLabel}


def _normalize_label(label: str) -> str:
    if label in LABEL2ID:
        return label
    if label in VALUE_TO_NAME:
        return VALUE_TO_NAME[label]
    raise ValueError(f"Unknown label: {label}")


def _text_to_tokens(text: str) -> tuple[list[str], list[tuple[int, int]]]:
    tokens = []
    spans = []
    for match in re.finditer(r"\S+", text):
        tokens.append(match.group(0))
        spans.append(match.span())
    return tokens, spans


def _extract_spans(task: dict) -> list[tuple[int, int, str]]:
    annotations = task.get("annotations") or []
    if not annotations:
        return []
    results = annotations[0].get("result", []) or []
    spans = []
    for result in results:
        value = result.get("value", {})
        start = value.get("start")
        end = value.get("end")
        labels = value.get("labels") or []
        if start is None or end is None or not labels:
            continue
        spans.append((int(start), int(end), _normalize_label(labels[0])))
    return spans


def parse_annotated_dataset(path: str) -> list[dict]:
    data = json.loads(Path(path).read_text(encoding="utf-8"))
    if not isinstance(data, list):
        raise ValueError("Annotated dataset must be a JSON array.")

    examples = []
    for task in data:
        text = task["data"]["text"]
        spans = _extract_spans(task)
        tokens, token_spans = _text_to_tokens(text)
        labels = []
        for token_start, token_end in token_spans:
            assigned = "RAW_PHRASE"
            for span_start, span_end, span_label in spans:
                if token_start < span_end and token_end > span_start:
                    assigned = span_label
                    break
            labels.append(assigned)
        examples.append({"tokens": tokens, "labels": labels})

    return examples


def tokenize_and_align(example, tokenizer, label2id):
    tokenized = tokenizer(example["tokens"], is_split_into_words=True, truncation=True)
    word_ids = tokenized.word_ids()
    labels = []
    prev = None

    for word_id in word_ids:
        if word_id is None:
            labels.append(-100)
        elif word_id != prev:
            labels.append(label2id[example["labels"][word_id]])
        else:
            labels.append(-100)
        prev = word_id

    tokenized["labels"] = labels
    return tokenized


def train(dataset_path: str = "./datasets/annotated/ffmpeg_gemini_v1.json", model_dir: str = MODEL_DIR):
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    model = AutoModelForTokenClassification.from_pretrained(
        MODEL_NAME, num_labels=len(LABEL_LIST), id2label=ID2LABEL, label2id=LABEL2ID
    )

    examples = parse_annotated_dataset(dataset_path)
    dataset = Dataset.from_list(examples)
    dataset = dataset.map(partial(tokenize_and_align, tokenizer=tokenizer, label2id=LABEL2ID))
    dataset = dataset.train_test_split(test_size=0.1)

    training_args = TrainingArguments(
        output_dir=model_dir,
        learning_rate=3e-5,
        per_device_train_batch_size=8,
        num_train_epochs=5,
        weight_decay=0.01,
        logging_steps=10,
        save_strategy="epoch",
    )

    data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["test"],
        data_collator=data_collator,
    )

    trainer.train()
    trainer.save_model(model_dir)
    tokenizer.save_pretrained(model_dir)


def _load_model(model_dir: str = MODEL_DIR):
    tokenizer = AutoTokenizer.from_pretrained(model_dir)
    model = AutoModelForTokenClassification.from_pretrained(model_dir)
    return tokenizer, model


def predict(text: str, model_dir: str = MODEL_DIR, top_k: int = 3):
    tokenizer, model = _load_model(model_dir)
    inputs = tokenizer(text.split(), return_tensors="pt", is_split_into_words=True)
    outputs = model(**inputs)

    logits = outputs.logits[0]
    probs = torch.softmax(logits, dim=-1)
    k = min(max(1, top_k), probs.shape[-1])
    top_probs, top_ids = torch.topk(probs, k, dim=-1)
    tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])

    for token, prob_row, id_row in zip(tokens, top_probs, top_ids):
        preds = [
            f"{ID2LABEL.get(int(label_id), 'RAW_PHRASE')}:{float(score):.4f}"
            for label_id, score in zip(id_row, prob_row)
        ]
        print(token, ", ".join(preds))


if __name__ == "__main__":
    train()