"""Interactive, on-demand J-lens explorer for held-out DAPO Math samples."""

from __future__ import annotations

import argparse
import json
import random
import re
import shlex
from dataclasses import dataclass
from pathlib import Path

import torch

from .model import load_qwen


def extract_boxed(text: str) -> str | None:
    """Return the content of the last balanced ``\\boxed{...}``."""
    marker = r"\boxed{"
    start = text.rfind(marker)
    if start < 0:
        return None
    content_start = start + len(marker)
    depth = 1
    for index in range(content_start, len(text)):
        if text[index] == "{":
            depth += 1
        elif text[index] == "}":
            depth -= 1
            if depth == 0:
                return text[content_start:index]
    return None


def normalize_answer(answer: str | None) -> str | None:
    if answer is None:
        return None
    value = answer.strip().strip("$")
    value = re.sub(r"\\(?:,|!|;|:|quad|qquad)", "", value)
    value = value.replace(" ", "").replace(",", "")
    return value


def response_answer(text: str) -> str | None:
    boxed = extract_boxed(text)
    if boxed is not None:
        return boxed
    matches = re.findall(r"(?im)^\s*Answer\s*:\s*(.+?)\s*$", text)
    return matches[-1] if matches else None


@dataclass(frozen=True)
class DapoSample:
    original_index: int
    messages: list[dict]
    label: str


def load_held_out_sample(path: str, *, seed: int, fit_count: int, sample_index: int) -> DapoSample:
    records: list[tuple[int, dict]] = []
    with Path(path).open(encoding="utf-8") as handle:
        for index, line in enumerate(handle):
            records.append((index, json.loads(line)))
    random.Random(seed).shuffle(records)
    held_out_index = fit_count + sample_index
    if not 0 <= held_out_index < len(records):
        raise IndexError(f"held-out sample {sample_index} is outside the dataset")
    original_index, record = records[held_out_index]
    prompt = record["prompt"]
    messages = prompt if isinstance(prompt, list) else [{"role": "user", "content": prompt}]
    return DapoSample(original_index, messages, str(record["label"]))


def find_last_subsequence(sequence: list[int], pattern: list[int]) -> int | None:
    if not pattern or len(pattern) > len(sequence):
        return None
    for start in range(len(sequence) - len(pattern), -1, -1):
        if sequence[start:start + len(pattern)] == pattern:
            return start
    return None


class Explorer:
    def __init__(self, args: argparse.Namespace) -> None:
        self.args = args
        self.adapter = load_qwen(args.model, device=args.device, dtype=torch.bfloat16)
        self.model = self.adapter.model
        self.tokenizer = self.adapter.tokenizer
        lens_state = torch.load(args.lens, map_location="cpu", weights_only=True)
        if lens_state["d_model"] != self.adapter.d_model:
            raise ValueError(
                f"lens width {lens_state['d_model']} does not match model width {self.adapter.d_model}"
            )
        self.layer_numbers = sorted(lens_state["J"])
        self.jacobians = {
            int(layer): matrix.to(args.device)
            for layer, matrix in lens_state["J"].items()
        }
        self.cache_dir = Path(args.cache_dir)
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        self.activations: dict[int, torch.Tensor] = {}
        self.sample: DapoSample | None = None
        self.full_ids: torch.Tensor | None = None
        self.prompt_length = 0
        self.response_text = ""
        self.current_position = 0
        self.load_sample(args.sample_index)

    def _cache_path(self, sample_index: int) -> Path:
        return self.cache_dir / f"seed-{self.args.seed}-fit-{self.args.fit_count}-sample-{sample_index}.json"

    def _chat_prompt_ids(self, messages: list[dict]) -> torch.Tensor:
        ids = self.tokenizer.apply_chat_template(
            messages,
            tokenize=True,
            add_generation_prompt=True,
            return_tensors="pt",
        )
        return ids.to(self.args.device)

    @torch.inference_mode()
    def _generate(self, sample: DapoSample) -> tuple[list[int], str]:
        prompt_ids = self._chat_prompt_ids(sample.messages)
        attention_mask = torch.ones_like(prompt_ids)
        generated = self.model.generate(
            input_ids=prompt_ids,
            attention_mask=attention_mask,
            do_sample=False,
            max_new_tokens=self.args.max_new_tokens,
            use_cache=True,
            pad_token_id=self.tokenizer.eos_token_id,
        )
        response_ids = generated[0, prompt_ids.shape[1]:].tolist()
        response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
        return response_ids, response

    def _load_or_generate(self, sample_index: int, sample: DapoSample) -> tuple[list[int], str]:
        path = self._cache_path(sample_index)
        if path.exists() and not self.args.regenerate:
            cached = json.loads(path.read_text(encoding="utf-8"))
            if cached["original_index"] != sample.original_index:
                raise ValueError(f"cache identity mismatch at {path}")
            return cached["response_ids"], cached["response_text"]
        response_ids, response = self._generate(sample)
        path.write_text(
            json.dumps(
                {
                    "sample_index": sample_index,
                    "original_index": sample.original_index,
                    "label": sample.label,
                    "response_ids": response_ids,
                    "response_text": response,
                    "max_new_tokens": self.args.max_new_tokens,
                    "do_sample": False,
                },
                ensure_ascii=False,
                indent=2,
            ),
            encoding="utf-8",
        )
        return response_ids, response

    @torch.inference_mode()
    def _capture(self, full_ids: torch.Tensor) -> None:
        self.activations.clear()
        handles = []
        for layer, block in enumerate(self.adapter.layers):
            def capture(_module, _inputs, output, layer=layer):
                tensor = output if torch.is_tensor(output) else output[0]
                self.activations[layer] = tensor[0].detach()
            handles.append(block.register_forward_hook(capture))
        try:
            self.adapter.forward(full_ids)
        finally:
            for handle in handles:
                handle.remove()

    def load_sample(self, sample_index: int) -> None:
        sample = load_held_out_sample(
            self.args.data,
            seed=self.args.seed,
            fit_count=self.args.fit_count,
            sample_index=sample_index,
        )
        prompt_ids = self._chat_prompt_ids(sample.messages)
        response_ids, response = self._load_or_generate(sample_index, sample)
        full_ids = torch.cat(
            [prompt_ids, torch.tensor([response_ids], device=self.args.device)], dim=1
        )
        self._capture(full_ids)
        self.args.sample_index = sample_index
        self.sample = sample
        self.full_ids = full_ids[0]
        self.prompt_length = prompt_ids.shape[1]
        self.response_text = response
        self.current_position = max(0, self.prompt_length - 1)
        print(
            f"Loaded held-out sample {sample_index} (dataset row {sample.original_index})\n"
            f"prompt_tokens={self.prompt_length} response_tokens={len(response_ids)} "
            f"total_tokens={len(self.full_ids)}"
        )
        self.print_result()

    def problem_text(self) -> str:
        assert self.sample is not None
        parts = []
        for message in self.sample.messages:
            role = str(message.get("role", "unknown")).upper()
            content = str(message.get("content", ""))
            parts.append(f"[{role}]\n{content}")
        return "\n\n".join(parts)

    def print_problem(self) -> None:
        print("=== Problem ===")
        print(self.problem_text())

    def print_output(self) -> None:
        print("=== Model output ===")
        print(self.response_text)

    def print_result(self) -> None:
        assert self.sample is not None
        predicted = response_answer(self.response_text)
        normalized_prediction = normalize_answer(predicted)
        normalized_gold = normalize_answer(self.sample.label)
        correct = normalized_prediction == normalized_gold
        print("=== Answers ===")
        print(f"Extracted:    {predicted!r}")
        print(f"Ground truth: {self.sample.label!r}")
        print(f"Correct:      {correct}")

    def print_info(self) -> None:
        assert self.sample is not None
        print(
            f"=== Sample ===\nheld-out index: {self.args.sample_index}\n"
            f"dataset row:   {self.sample.original_index}"
        )
        self.print_problem()
        self.print_output()
        self.print_result()

    def _check_position(self, position: int) -> int:
        assert self.full_ids is not None
        if position < 0:
            position += len(self.full_ids)
        if not 0 <= position < len(self.full_ids) - 1:
            raise ValueError(f"position must be in [0, {len(self.full_ids) - 2}]")
        return position

    @torch.inference_mode()
    def logits(self, layer: int, position: int, *, use_jacobian: bool = True) -> torch.Tensor:
        residual = self.activations[layer][position]
        if use_jacobian:
            residual = residual @ self.jacobians[layer].T
        residual = self.adapter.decoder.norm(residual)
        return self.model.lm_head(residual).float()

    def token_label(self, token_id: int) -> str:
        return repr(self.tokenizer.decode([int(token_id)]))

    def print_tokens(self, start: int | None = None, end: int | None = None) -> None:
        assert self.full_ids is not None
        if start is None:
            start = self.prompt_length
        if end is None:
            end = min(len(self.full_ids), start + 80)
        start = max(0, start)
        end = min(len(self.full_ids), end)
        for position in range(start, end):
            marker = ">" if position == self.current_position else " "
            region = "R" if position >= self.prompt_length else "P"
            print(f"{marker} {position:5d} {region} {self.token_label(self.full_ids[position])}")

    def inspect(self, position: int, top_k: int = 5) -> None:
        position = self._check_position(position)
        self.current_position = position
        assert self.full_ids is not None
        print(
            f"position={position} current={self.token_label(self.full_ids[position])} "
            f"predicts={self.token_label(self.full_ids[position + 1])}"
        )
        for layer in self.layer_numbers:
            logits = self.logits(layer, position)
            values, ids = logits.topk(top_k)
            decoded = " ".join(
                f"{self.token_label(token_id)}({value:.2f})"
                for token_id, value in zip(ids.tolist(), values.tolist(), strict=True)
            )
            print(f"L{layer:02d} J: {decoded}")

    def compare(self, position: int, layer: int, top_k: int = 10) -> None:
        position = self._check_position(position)
        if layer not in self.layer_numbers:
            raise ValueError(f"layer must be one of {self.layer_numbers}")
        for name, enabled in (("J-lens", True), ("logit", False)):
            values, ids = self.logits(layer, position, use_jacobian=enabled).topk(top_k)
            decoded = " ".join(
                f"{self.token_label(token_id)}({value:.2f})"
                for token_id, value in zip(ids.tolist(), values.tolist(), strict=True)
            )
            print(f"{name:7s}: {decoded}")

    @torch.inference_mode()
    def save_interval(
        self,
        start: int,
        end: int,
        layer: int | None = None,
        top_k: int = 5,
        output_path: str | None = None,
    ) -> Path:
        """Export original tokens and their J-lens mappings for ``[start, end)``."""
        assert self.full_ids is not None and self.sample is not None
        if start < 0 or end <= start or end > len(self.full_ids) - 1:
            raise ValueError(
                f"require 0 <= START < END <= {len(self.full_ids) - 1}; "
                "END is exclusive"
            )
        if top_k < 1:
            raise ValueError("TOP_K must be positive")
        layers = self.layer_numbers if layer is None else [layer]
        if any(item not in self.layer_numbers for item in layers):
            raise ValueError(f"layer must be one of {self.layer_numbers}, or 'all'")

        layer_label = "all" if layer is None else str(layer)
        if output_path is None:
            output = Path(self.args.export_dir) / (
                f"sample-{self.args.sample_index}-tokens-{start}-{end}-layer-{layer_label}.txt"
            )
        else:
            output = Path(output_path)
        output.parent.mkdir(parents=True, exist_ok=True)

        lines = [
            "J-lens token interval export",
            f"held_out_sample: {self.args.sample_index}",
            f"dataset_row: {self.sample.original_index}",
            f"ground_truth: {self.sample.label!r}",
            f"token_interval: [{start}, {end})",
            f"layers: {layer_label}",
            f"top_k: {top_k}",
            "convention: activation at position t predicts token at t+1",
            "",
        ]
        for position in range(start, end):
            current_id = int(self.full_ids[position])
            next_id = int(self.full_ids[position + 1])
            region = "response" if position >= self.prompt_length else "prompt"
            lines.extend(
                [
                    f"POSITION {position} ({region})",
                    f"  original: id={current_id} token={self.token_label(current_id)}",
                    f"  predicts: id={next_id} token={self.token_label(next_id)}",
                ]
            )
            for layer_number in layers:
                values, ids = self.logits(layer_number, position).topk(top_k)
                mapped = " | ".join(
                    f"rank={rank} id={token_id} token={self.token_label(token_id)} logit={value:.4f}"
                    for rank, (token_id, value) in enumerate(
                        zip(ids.tolist(), values.tolist(), strict=True), start=1
                    )
                )
                lines.append(f"  L{layer_number:02d}: {mapped}")
            lines.append("")
        output.write_text("\n".join(lines), encoding="utf-8")
        print(f"saved {end - start} positions × {len(layers)} layers to {output}")
        return output

    def _token_ids(self, text: str) -> list[int]:
        return self.tokenizer.encode(text, add_special_tokens=False)

    def trace(self, text: str, position: int | None = None) -> None:
        position = self.current_position if position is None else self._check_position(position)
        ids = self._token_ids(text)
        if not ids:
            raise ValueError("text tokenized to no tokens")
        print(f"trace text={text!r} ids={ids} at position={position}")
        for layer in self.layer_numbers:
            logits = self.logits(layer, position)
            details = []
            for token_id in ids:
                rank = 1 + int((logits > logits[token_id]).sum().item())
                details.append(f"{self.token_label(token_id)} rank={rank} logit={logits[token_id]:.2f}")
            print(f"L{layer:02d}: {'; '.join(details)}")

    def answer_trace(self) -> None:
        assert self.sample is not None and self.full_ids is not None
        full_list = self.full_ids.tolist()
        candidates = [self.sample.label, " " + self.sample.label]
        match = None
        matched_ids = None
        for candidate in candidates:
            ids = self._token_ids(candidate)
            found = find_last_subsequence(full_list[self.prompt_length:], ids)
            if found is not None:
                match = self.prompt_length + found
                matched_ids = ids
                break
        if match is None or matched_ids is None:
            print(f"Gold answer {self.sample.label!r} was not found as a token sequence in the response")
            return
        activation_position = match - 1
        print(
            f"gold answer starts at token position {match}; inspecting position "
            f"{activation_position}, which predicts its first token"
        )
        self.trace(self.tokenizer.decode([matched_ids[0]]), activation_position)

    def repl(self) -> None:
        print("Type 'help' for commands.")
        while True:
            try:
                raw = input("jlens> ").strip()
                if not raw:
                    continue
                parts = shlex.split(raw)
                command, values = parts[0].lower(), parts[1:]
                if command in {"quit", "exit", "q"}:
                    return
                if command == "help":
                    print(
                        "tokens [start] [end]\ninspect POSITION [TOP_K]\n"
                        "compare POSITION LAYER [TOP_K]\ntrace TEXT [POSITION]\n"
                        "save START END [LAYER|all] [TOP_K] [FILE]\n"
                        "problem\noutput\nresult\ninfo\nanswer\n"
                        "sample INDEX\nnext\nquit"
                    )
                elif command == "tokens":
                    self.print_tokens(*(int(value) for value in values))
                elif command == "inspect":
                    self.inspect(int(values[0]), int(values[1]) if len(values) > 1 else 5)
                elif command == "compare":
                    self.compare(int(values[0]), int(values[1]), int(values[2]) if len(values) > 2 else 10)
                elif command == "trace":
                    self.trace(values[0], int(values[1]) if len(values) > 1 else None)
                elif command == "save":
                    if len(values) < 2:
                        raise ValueError(
                            "usage: save START END [LAYER|all] [TOP_K] [FILE]"
                        )
                    chosen_layer = None
                    if len(values) > 2 and values[2].lower() != "all":
                        chosen_layer = int(values[2])
                    chosen_top_k = int(values[3]) if len(values) > 3 else 5
                    chosen_file = values[4] if len(values) > 4 else None
                    self.save_interval(
                        int(values[0]), int(values[1]), chosen_layer,
                        chosen_top_k, chosen_file,
                    )
                elif command == "answer":
                    self.answer_trace()
                elif command == "problem":
                    self.print_problem()
                elif command == "output":
                    self.print_output()
                elif command == "result":
                    self.print_result()
                elif command == "sample":
                    self.load_sample(int(values[0]))
                elif command == "next":
                    self.load_sample(self.args.sample_index + 1)
                elif command == "info":
                    self.print_info()
                else:
                    print(f"unknown command: {command}")
            except (IndexError, ValueError) as exc:
                print(f"error: {exc}")
            except KeyboardInterrupt:
                print("\nUse 'quit' to exit.")


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--model", required=True)
    parser.add_argument("--data", required=True)
    parser.add_argument("--lens", required=True)
    parser.add_argument("--fit-count", type=int, required=True)
    parser.add_argument("--seed", type=int, default=17)
    parser.add_argument("--sample-index", type=int, default=0)
    parser.add_argument("--device", default="cuda:0")
    parser.add_argument("--max-new-tokens", type=int, default=2048)
    parser.add_argument("--cache-dir", default="outputs/explorer-cache")
    parser.add_argument("--export-dir", default="outputs/jlens-exports")
    parser.add_argument("--regenerate", action="store_true")
    return parser


def main() -> None:
    Explorer(build_parser().parse_args()).repl()


if __name__ == "__main__":
    main()