from __future__ import annotations

import json
import argparse
import os
from pathlib import Path
from typing import Any, Dict, List

from conv_data_gen.generators.conversation.core_simulator import (
    ConversationSimulatorWithTools,
)
from conv_data_gen.generators.user.user_persona_generator import (
    UserPersonaGenerator,
    PersonaSample,
)
from concurrent.futures import ThreadPoolExecutor, as_completed


def _project_root() -> Path:
    return Path(__file__).resolve().parent.parent


def _default_assets_dir() -> Path:
    return _project_root() / "tests" / "assets"


def _default_output_dir() -> Path:
    out = _project_root() / "tests" / "output" / "user_randomizer"
    out.mkdir(parents=True, exist_ok=True)
    return out


def _load_proxy_json(path: Path) -> Dict[str, str]:
    obj = json.loads(path.read_text(encoding="utf-8"))
    if isinstance(obj, dict):
        return {str(k): str(v) for k, v in obj.items()}
    raise ValueError("Proxy JSON must be an object/dict")


def _read_text_file(path: Path) -> str:
    return path.read_text(encoding="utf-8")


def run_user_randomizer_test(
    n_variants: int,
    bot_prompt_path: Path,
    proxy_json_path: Path,
    out_dir: Path,
) -> Path:
    out_dir.mkdir(parents=True, exist_ok=True)
    proxy = _load_proxy_json(proxy_json_path)

    bot_text = _read_text_file(bot_prompt_path)

    # Single generator reused for sampling + formatting
    gen = UserPersonaGenerator(
        company_name="sarvam-finance",
        stakeholder="customer",
        use_case="loan",
        bot_prompt_text=bot_text,
    )

    mapping: List[Dict[str, Any]] = []

    def _work(i: int) -> Dict[str, Any]:
        # Sample attributes
        sample = gen._randomizer.sample_persona()

        # Compose persona text using reusable generator
        proxy_for_format = {str(k): str(v) for k, v in proxy.items()}
        persona = PersonaSample(
            knobs=sample.get("knobs", {}),
            knob_descriptions=sample.get("knob_descriptions", {}),
            language_style=sample.get("language_style", {}),
            language_descriptions=sample.get("language_descriptions", {}),
            demographics=sample.get("demographics", {}),
            demographic_descriptions=sample.get(
                "demographic_descriptions", {}
            ),
            interaction=sample.get("interaction", {}),
        )
        user_text = gen._compose_user_message_for_proxy(
            persona, proxy_for_format
        )

        # Persist persona
        # Run simulation
        sim = ConversationSimulatorWithTools(
            user_prompt_path=None,
            user_prompt_text=user_text,
            bot_prompt_path=str(bot_prompt_path),
            max_turns=10,
            output_dir=str(out_dir),
            base_filename=f"persona_{i}",
            conversation_direction="agent_to_user",
        )
        transcript = sim.generate()

        conv_base = (out_dir / f"persona_{i}").as_posix()
        # Prepend sampled knobs to the conversation text file
        try:
            conv_txt_path = out_dir / f"persona_{i}.txt"
            if conv_txt_path.exists():
                txt = conv_txt_path.read_text(encoding="utf-8")
                knobs_lines: List[str] = ["USER KNOBS (sampled):"]
                for k, v in sample.get("knobs", {}).items():
                    knobs_lines.append(f"- {k}: {v}")
                header = "\n".join(knobs_lines) + "\n\n"
                conv_txt_path.write_text(header + txt, encoding="utf-8")
        except Exception:
            pass

        # Enrich conversation JSON with user attributes
        conv_json_path = out_dir / f"persona_{i}.json"
        try:
            if conv_json_path.exists():
                meta = json.loads(conv_json_path.read_text(encoding="utf-8"))
                meta["user_attributes"] = {
                    "knobs": sample.get("knobs", {}),
                    "language_style": sample.get("language_style", {}),
                    "demographics": sample.get("demographics", {}),
                    "interaction": sample.get("interaction", {}),
                }
                conv_json_path.write_text(
                    json.dumps(meta, ensure_ascii=False, indent=2),
                    encoding="utf-8",
                )
        except Exception:
            pass
        return {
            "persona_index": i,
            "conversation_base": conv_base,
            "user_prompt_path": "",
            "bot_prompt_path": str(bot_prompt_path),
            "sampled_attributes": {
                "knobs": sample.get("knobs", {}),
                "language_style": sample.get("language_style", {}),
                "demographics": sample.get("demographics", {}),
                "interaction": sample.get("interaction", {}),
            },
            "total_turns": len(transcript),
        }

    cpu = os.cpu_count() or 2
    max_workers = max(1, min(16, cpu * 4))
    workers = min(max_workers, max(1, n_variants))

    futures = []
    with ThreadPoolExecutor(max_workers=workers) as ex:
        for i in range(1, n_variants + 1):
            futures.append(ex.submit(_work, i))
        for fut in as_completed(futures):
            try:
                res = fut.result()
                if isinstance(res, dict):
                    mapping.append(res)
            except Exception:
                # Skip failed runs but continue others
                pass

    # Stable order by persona index
    mapping.sort(key=lambda r: int(r.get("persona_index", 0)))

    # Write compact JSON map: conversation file base -> user attributes
    map_path = out_dir / "conversation_user_attributes_map.json"
    with open(map_path, "w", encoding="utf-8") as f:
        json.dump(mapping, f, ensure_ascii=False, indent=2)

    return map_path


def _parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(
        description=(
            "Generate conversations to test the effect of user attributes."
        )
    )
    p.add_argument("--num", type=int, default=10, help="Number of variants")
    p.add_argument(
        "--bot",
        type=Path,
        default=_default_assets_dir() / "bot.txt",
        help="Path to bot prompt text file",
    )
    p.add_argument(
        "--proxy",
        type=Path,
        default=_default_assets_dir() / "proxy.json",
        help="Path to base user proxy JSON file",
    )
    p.add_argument(
        "--out",
        type=Path,
        default=_default_output_dir(),
        help="Output directory for personas, conversations, and map",
    )
    return p.parse_args()


if __name__ == "__main__":
    args = _parse_args()
    path = run_user_randomizer_test(
        n_variants=max(1, int(args.num)),
        bot_prompt_path=args.bot,
        proxy_json_path=args.proxy,
        out_dir=args.out,
    )
    print(str(path))