"""
Reference pipeline orchestrator.
Wire into your LLM runner. Supports packs built from free-form scenarios.
"""

from pathlib import Path
from typing import Tuple, Dict, Any
import json

from ..validators import schema_validator, unit_validator, math_validator, policy_validator
from ..graders.rule_grader import grade
from .io_utils import load_json

# -------------------------
# REPLACE with your actual LLM runner
def call_model(system_prompt: str, user_prompt: str, temperature: float = 0.2, top_p: float = 0.9) -> str:
    raise NotImplementedError("call_model must be implemented in your environment.")
# -------------------------

def build_user_prompt(template_text: str, context: str, data_inputs: str, constraints: str) -> str:
    return (
        template_text
        .replace("{CONTEXT}", context)
        .replace("{DATA_INPUTS}", data_inputs)
        .replace("{CONSTRAINTS}", constraints)
    )

def run_clarityops(pack_dir: str) -> Tuple[Dict[str, Any], Any]:
    pack = Path(pack_dir)
    root = pack.parents[1]

    system_prompt = (root / "prompts" / "system_two_phase.txt").read_text(encoding="utf-8")
    user_template = (root / "prompts" / "user_template.txt").read_text(encoding="utf-8")

    inputs = load_json(pack / "inputs.json")
    constraints = load_json(pack / "constraints.json")
    schema_cfg = load_json(pack / "schema.json")

    # Optional — only required if you want grading
    rubric_path = pack / "rubric.json"
    expected_path = pack / "expected.json"
    rubric = load_json(rubric_path) if rubric_path.exists() else {"set_equals": [], "must_contain": [], "numeric_equals": []}
    expected = load_json(expected_path) if expected_path.exists() else {"note": "No expected gold provided."}

    # Build Phase 1 user prompt
    context_block = inputs.get("context", "No context provided.")
    data_block = json.dumps(inputs.get("data_inputs", {}), ensure_ascii=False, indent=2)
    constraints_block = json.dumps(constraints, ensure_ascii=False, indent=2)

    # ---- Phase 1: Clarification Questions
    user_prompt_phase1 = build_user_prompt(user_template, context_block, data_block, constraints_block)
    user_prompt_phase1 += "\n\n[INSTRUCTION TO MODEL] Produce **Phase 1** only. Do not produce Phase 2 yet."
    clarif_raw = call_model(system_prompt, user_prompt_phase1)

    # ---- Collect answers
    clarif_answers_path = pack / "clarifications.json"
    if clarif_answers_path.exists():
        clarif_answers = load_json(clarif_answers_path)
        # If the file is still the placeholder, raise to force operator to fill it
        if clarif_answers.get("_note"):
            raise RuntimeError(f"Clarification answers required. Edit and remove _note in: {clarif_answers_path}")
    else:
        raise RuntimeError(f"Clarification answers file missing: {clarif_answers_path}")

    # Merge clarifications into inputs for Phase 2
    merged_inputs = inputs.copy()
    merged_inputs["clarifications"] = clarif_answers

    # ---- Phase 2: Structured Analysis
    user_prompt_phase2 = build_user_prompt(
        user_template,
        context_block,
        json.dumps(merged_inputs, ensure_ascii=False, indent=2),
        constraints_block
    )
    user_prompt_phase2 += "\n\n[INSTRUCTION TO MODEL] Produce **Phase 2** only (final structured analysis), using clarified inputs."
    final_raw = call_model(system_prompt, user_prompt_phase2)

    # Parse final output JSON
    try:
        output = json.loads(final_raw)
    except Exception as e:
        raise ValueError(f"Failed to parse model output as JSON. Raw:\n{final_raw}") from e

    # Validators (hard guardrails)
    schema_validator.assert_valid(output, str(root / "schemas" / "analysis_output.schema.json"))
    unit_validator.assert_valid(output, str(root / "core" / "policy_global.json"))
    math_validator.assert_valid(output)
    policy_validator.assert_valid(output, str(pack / "constraints.json"))

    # Optional grading
    grader_result = grade(output, str(rubric_path)) if rubric_path.exists() else {"score": 0, "max_score": 0, "notes": ["No rubric."]}
    output["_grader"] = grader_result
    output["_clarifications_summary"] = clarif_raw

    return output, clarif_raw