"""
MIPROv2-style prompt optimizer (modular, DSPy-inspired).

This module provides a modular implementation of the MIPROv2 pseudocode from DSPy,
adapted to a provider-agnostic "program" interface. The goal is to keep the
bootstrapping and search process pluggable so it can be swapped for alternatives.

Key ideas
- Program adapter: unify how we set instructions/demos and run predictions.
- Demo bootstrapping: gather high-confidence examples (by metric) as candidates.
- Instruction proposals: generated by a prompt model from contextual summaries.
- Search (placeholder): random/Bayesian-like search over (instructions × demos).

Notes
- The implementation is intentionally lightweight and dependency-free.
- "BayesOpt" here is a placeholder randomized proposer that uses history; you
  can plug in a real optimizer later.
"""

from __future__ import annotations

import random
from collections.abc import Callable, Sequence
from dataclasses import dataclass, replace
from typing import Any, Protocol

# ---------------------------
# Program adapter and protocols
# ---------------------------


class PredictProgram(Protocol):
    """Minimal protocol a program must satisfy for MIPRO.

    You can adapt your own pipeline to this by implementing these methods or
    by wrapping it with `ProgramAdapter` below.
    """

    def deepcopy(self) -> PredictProgram: ...

    def run(self, x: Any, *, model: Any | None = None) -> Any: ...

    def with_instructions(self, instructions: dict[str, str]) -> PredictProgram: ...

    def with_demos(self, demos: list[tuple[Any, Any]]) -> PredictProgram: ...

    @property
    def predictors(self) -> list[str]: ...


@dataclass
class ProgramAdapter:
    """Adapter that turns a set of callables/state into a `PredictProgram`.

    - run_fn: Callable[[x, model], y]
    - state: arbitrary dict; supports `instructions` and `demos` keys
    - predictors: list of predictor identifiers (e.g., names of prompt blocks)
    - set_instructions: Callable to update instructions (per predictor)
    - set_demos: Callable to update demos (global or per predictor)
    """

    run_fn: Callable[[Any, Any | None], Any]
    state: dict[str, Any]
    _predictors: list[str]
    set_instructions: Callable[[dict[str, str], dict[str, Any]], dict[str, Any]]
    set_demos: Callable[[list[tuple[Any, Any]], dict[str, Any]], dict[str, Any]]

    def deepcopy(self) -> ProgramAdapter:
        return replace(self, state={**self.state})

    def run(self, x: Any, *, model: Any | None = None) -> Any:
        return self.run_fn(x, model)

    def with_instructions(self, instructions: dict[str, str]) -> ProgramAdapter:
        new_state = self.set_instructions(instructions, {**self.state})
        return replace(self, state=new_state)

    def with_demos(self, demos: list[tuple[Any, Any]]) -> ProgramAdapter:
        new_state = self.set_demos(demos, {**self.state})
        return replace(self, state=new_state)

    @property
    def predictors(self) -> list[str]:
        return list(self._predictors)


# ---------------------------
# Utility helpers
# ---------------------------


def summarize_dataset(trainset: Sequence[tuple[Any, Any]], max_items: int = 50) -> str:
    n = len(trainset)
    ex = ", ".join(
        repr(trainset[i][0])[:40] for i in range(0, min(max_items, n), max(1, n // max_items or 1))
    )
    return f"Dataset size: {n}. Example inputs: {ex}"


def summarize_program(prog: PredictProgram) -> str:
    return f"Program predictors: {prog.predictors}"


def random_tip(rng: random.Random) -> str:
    tips = [
        "Be concise.",
        "Focus on the task definition.",
        "Use the provided examples as guidance.",
        "Avoid unnecessary verbosity.",
    ]
    return rng.choice(tips)


def choose(items: Sequence[Any], rng: random.Random | None = None) -> Any:
    r = rng or random
    return r.choice(items)


# ---------------------------
# Evaluator
# ---------------------------


@dataclass
class EvalResult:
    score: float
    subscores: list[float]


def evaluate_program(
    program: PredictProgram, dataset: Sequence[tuple[Any, Any]], metric: Callable[[Any, Any], float]
) -> EvalResult:
    subs = []
    for x, y in dataset:
        yhat = program.run(x)
        subs.append(metric(yhat, y))
    return EvalResult(score=float(sum(subs)) / max(1, len(subs)), subscores=subs)


# ---------------------------
# MIPROv2 compile
# ---------------------------


def mipro_v2_compile(
    student: PredictProgram,
    trainset: Sequence[tuple[Any, Any]],
    valset: Sequence[tuple[Any, Any]],
    metric: Callable[[Any, Any], float],
    *,
    prompt_model: Any,
    task_model: Any,
    max_bootstrapped_demos: int = 8,
    max_labeled_demos: int = 4,
    num_candidates: int = 8,
    num_trials: int = 20,
    minibatch: bool = True,
    minibatch_size: int = 16,
    minibatch_full_eval_steps: int = 5,
    seed: int = 0,
    auto: str = "light",
    program_aware: bool = True,
    data_aware: bool = True,
    tip_aware: bool = True,
    fewshot_aware: bool = True,
) -> tuple[PredictProgram, list[dict[str, Any]]]:
    """MIPROv2-style optimizer.

    Arguments mirror the DSPy pseudocode but remain provider-agnostic. The
    `prompt_model` must expose `generate_instructions(ctx, k)`; the `student`
    program must implement the `PredictProgram` protocol.
    """

    rng = random.Random(seed)
    program = student.deepcopy()

    # Step 1: bootstrap few-shot example candidates
    demo_candidates: list[dict[str, Any]] = []
    for _ in range(num_candidates):
        boot: list[tuple[Any, Any]] = []
        # collect bootstrapped, self-consistent demos
        while len(boot) < max_bootstrapped_demos:
            x, y = rng.choice(trainset)
            yhat = program.run(x, model=task_model)
            if metric(yhat, y) == 1:  # perfect match
                boot.append((x, y))
        labeled = rng.sample(list(trainset), k=min(max_labeled_demos, len(trainset)))
        demo_candidates.append({"boot": boot, "labeled": labeled})

    # Step 2: propose instruction candidates per predictor
    instr_candidates: dict[str, list[str]] = {}
    for pred in program.predictors or ["predictor"]:
        ctx: dict[str, Any] = {}
        if data_aware:
            ctx["dataset_summary"] = summarize_dataset(trainset)
        if program_aware:
            ctx["program_summary"] = summarize_program(program)
        if fewshot_aware and demo_candidates:
            ctx["examples"] = choose(demo_candidates, rng)
        if tip_aware:
            ctx["tip"] = random_tip(rng)
        cand = prompt_model.generate_instructions(ctx, k=num_candidates)
        instr_candidates[pred] = list(cand)

    # Step 3: Bayesian-optimization-like search (random proposer placeholder)
    history: list[tuple[dict[str, Any], float]] = []
    records: list[dict[str, Any]] = []
    best_score = -1.0
    best_cfg: dict[str, Any] | None = None

    def propose(history_: list[tuple[dict[str, Any], float]]) -> dict[str, Any]:
        # Placeholder: randomly sample from the cartesian product
        instructions = {pred: choose(instr_candidates[pred], rng) for pred in instr_candidates}
        demos = choose(demo_candidates, rng) if demo_candidates else None
        return {"instructions": instructions, "demo_set": demos}

    for t in range(1, num_trials + 1):
        theta = propose(history)
        program_t = program.with_instructions(theta["instructions"])
        if theta.get("demo_set") is not None:
            # Combine bootstrapped + labeled demos
            ds = theta["demo_set"]
            demo_set = list(ds.get("boot", [])) + list(ds.get("labeled", []))
            program_t = program_t.with_demos(demo_set)

        batch = (
            valset
            if not minibatch
            else random.sample(list(valset), k=min(minibatch_size, len(valset)))
        )
        batch_res = evaluate_program(program_t, batch, metric)
        s_t = batch_res.score
        history.append((theta, s_t))
        records.append(
            {
                "trial": t,
                "evaluation": "batch" if minibatch else "full",
                "score": s_t,
                "intervention": {
                    "instructions": theta.get("instructions"),
                    "demo_set": theta.get("demo_set"),
                },
            }
        )

        if (not minibatch) or (t % max(1, minibatch_full_eval_steps) == 0):
            full_res = evaluate_program(program_t, valset, metric)
            s_full = full_res.score
            if s_full > best_score:
                best_score = s_full
                best_cfg = theta
            records.append(
                {
                    "trial": t,
                    "evaluation": "full",
                    "score": s_full,
                    "intervention": {
                        "instructions": theta.get("instructions"),
                        "demo_set": theta.get("demo_set"),
                    },
                }
            )

    if best_cfg is None:
        return program, records

    best_program = program.with_instructions(best_cfg["instructions"])
    if best_cfg.get("demo_set") is not None:
        ds = best_cfg["demo_set"]
        demo_set = list(ds.get("boot", [])) + list(ds.get("labeled", []))
        best_program = best_program.with_demos(demo_set)
    return best_program, records


__all__ = [
    "PredictProgram",
    "ProgramAdapter",
    "evaluate_program",
    "mipro_v2_compile",
]


class ExampleTwoStepDag:
    pass


"""
A -> B
"""
