"""Build the prompt dataset for GRPO training.

Responsibility: enumerate the curriculum of physical systems, simulate each
one a configurable number of times, and emit a :class:`datasets.Dataset`
whose rows contain everything the training loop needs:

- ``prompt``: the chat-format string passed to the model
- ``system_id``, ``state_variables``, ``parameters``, ``initial_conditions``,
  ``timestamps``, ``observed``: the system context the scorer needs
- ``previous_total``: 0.0 at turn-0 (we train on first-turn prompts; the
  iterative refinement skill emerges from the model's general ability to
  read history at inference time)

Multi-turn prompts can be added later by extending this builder; the
hackathon scope deliberately keeps it to turn-0 prompts.
"""

from __future__ import annotations

from collections.abc import Iterable

import numpy as np
from datasets import Dataset
from pydantic import BaseModel, ConfigDict

from physix.models import DEFAULT_MAX_TURNS, PhysiXObservation
from physix.systems import (
    SYSTEM_REGISTRY,
    SUPPORTED_SYSTEMS,
    SystemTier,
    get_system,
    list_systems_by_tier,
)
from physix.systems.base import PhysicalSystem, TrajectoryData
from physix.training.prompt import build_prompt


class DatasetSpec(BaseModel):
    """Configuration for :func:`build_training_dataset`."""

    model_config = ConfigDict(frozen=True)

    system_ids: tuple[str, ...] = SUPPORTED_SYSTEMS
    instances_per_system: int = 32
    seed: int = 0


class EvalDatasetSpec(BaseModel):
    """Held-out evaluation set, drawn separately so seeds do not overlap."""

    model_config = ConfigDict(frozen=True)

    train_tiers: tuple[SystemTier, ...] = (SystemTier.TIER_1, SystemTier.TIER_2)
    held_out_tiers: tuple[SystemTier, ...] = (SystemTier.TIER_3,)
    instances_per_system: int = 8
    seed: int = 1_000_000  # large to avoid overlap with training seeds


def build_training_dataset(spec: DatasetSpec | None = None) -> Dataset:
    """Build the GRPO training dataset.

    Each row contains one (system, instance) prompt at turn 0.
    """
    spec = spec or DatasetSpec()
    _validate_system_ids(spec.system_ids)
    rng = np.random.default_rng(spec.seed)

    rows: list[dict[str, object]] = []
    for system_id in spec.system_ids:
        for _ in range(spec.instances_per_system):
            rows.append(_build_row(system_id, rng))
    return Dataset.from_list(rows)


def _validate_system_ids(system_ids: tuple[str, ...]) -> None:
    """Fail fast if the spec references an unregistered system."""
    if not system_ids:
        raise ValueError(
            "DatasetSpec.system_ids must be non-empty. "
            f"Available: {sorted(SYSTEM_REGISTRY)!r}."
        )
    unknown = [sid for sid in system_ids if sid not in SYSTEM_REGISTRY]
    if unknown:
        raise ValueError(
            f"Unknown system_ids in DatasetSpec: {unknown!r}. "
            f"Registered: {sorted(SYSTEM_REGISTRY)!r}."
        )


def build_eval_dataset(spec: EvalDatasetSpec | None = None) -> Dataset:
    """Build a held-out evaluation dataset spanning held-out tiers too."""
    spec = spec or EvalDatasetSpec()
    rng = np.random.default_rng(spec.seed)

    rows: list[dict[str, object]] = []
    for system_id in _list_systems(spec.train_tiers + spec.held_out_tiers):
        for _ in range(spec.instances_per_system):
            row = _build_row(system_id, rng)
            row["is_held_out"] = system_id in _list_systems(spec.held_out_tiers)
            rows.append(row)
    return Dataset.from_list(rows)


def _list_systems(tiers: Iterable[SystemTier]) -> list[str]:
    out: list[str] = []
    for tier in tiers:
        out.extend(list_systems_by_tier(tier))
    return out


def _build_row(system_id: str, rng: np.random.Generator) -> dict[str, object]:
    """Generate one (prompt + system context) row for a given system."""
    system = get_system(system_id)
    trajectory = system.simulate(rng)

    obs = _build_observation(system, trajectory)
    prompt = build_prompt(obs)

    return {
        "prompt": prompt,  # chat list of {"role", "content"} dicts
        "system_id": system_id,
        "state_variables": list(system.state_variables),
        "parameters": dict(system.parameters),
        "initial_conditions": dict(system.initial_conditions),
        "timestamps": trajectory.timestamps.tolist(),
        "observed": {var: trajectory.states[var].tolist() for var in system.state_variables},
        "previous_r_match": 0.0,
    }


def _build_observation(
    system: PhysicalSystem,
    trajectory: TrajectoryData,
) -> PhysiXObservation:
    """Construct a turn-0 :class:`PhysiXObservation` for a fresh system.

    We bypass :class:`PhysiXEnvironment` here because its lifecycle (history,
    convergence flag, episode budget) is irrelevant for dataset construction.
    """
    return PhysiXObservation(
        done=False,
        reward=None,
        trajectory=trajectory.to_observation_samples(),
        state_variables=list(system.state_variables),
        hint=system.hint(system.parameters),
        history=[],
        mismatch_summary="",
        turn=0,
        turn_remaining=DEFAULT_MAX_TURNS,
        system_id=system.system_id,
        stats=trajectory.stats(),
        reward_breakdown={},
    )