"""Phase-separated E02 interactive localization experiment."""

from __future__ import annotations

from dataclasses import asdict
from hashlib import sha256
import json
from pathlib import Path
import time
from typing import Any, Sequence

from .confirmatory_retrieval import retrieve_treatment, subprocess_git
from .context_packing import (
    EVIDENCE_TOKEN_BUDGET,
    candidates_from_records,
    load_ranking_records,
    pack_role_summaries,
    pack_skeletons,
    pack_snippets,
    pack_specialized_channels,
    pack_whole_files,
    ranking_path,
)
from .fusion import unique_files
from .llm_localization import LocalizationError, parse_selection
from .lm_studio import LMStudioClient, LMStudioError
from .lm_studio_embeddings import LMStudioEmbeddingClient
from .pilot import research_code_revision, retrieval_metrics
from .repository import GitSnapshot, chunk_snapshot
from .retrieval import BM25FuzzyRetriever, DenseRetriever, ExactRetriever, SQLiteEmbeddingCache
from .specs import (
    HarnessSpec,
    TaskSpec,
    load_embeddings,
    load_experiments,
    load_harnesses,
    load_models,
    load_task_split,
    load_tasks,
)
from .syntax_index import SymbolGraph, SyntaxRetriever, parse_snapshot
from .telemetry import EventWriter, RunIdentity, run_directory
from .tokenization import QwenTokenCounter
from .vector_backends import FaissFlatRetriever


LOCALIZE_SYSTEM = """You are performing blinded bug localization in a large Go repository.
Use only the issue and evidence supplied by the harness. Select source files likely to
require changes. Return exactly one JSON object with keys files and reasoning. files must
contain 1-10 distinct evidence paths; reasoning must be below 180 words. Do not write a patch
and do not use Markdown."""

QUERY_SYSTEM = """You are reformulating a code-search query after inspecting initial evidence.
Return exactly {"query":"..."} with one concise query that would retrieve missing implementation
locations. Do not include reasoning, Markdown, or any other key."""


class InteractiveExperimentError(RuntimeError):
    """Raised when an E02 phase cannot preserve its frozen protocol."""


def score_selection_response(
    response: dict[str, Any], allowed_paths: set[str]
) -> tuple[dict[str, Any], str | None]:
    """Parse a constrained selection or score an interface violation as no selection.

    Invalid model output is an experimental outcome, not an infrastructure failure.  The
    unmodified response remains in the run artifacts and the violation is explicitly logged.
    """

    try:
        return parse_selection(response, allowed_paths), None
    except LocalizationError as exc:
        return {"files": [], "reasoning": ""}, str(exc)


def exclusive_loaded(native_models: Sequence[dict[str, Any]]) -> tuple[str, ...]:
    return tuple(
        str(record.get("key")) for record in native_models if record.get("loaded_instances")
    )


def selected_tasks(root: Path, experiment: Any, task_filter: set[str] | None) -> list[TaskSpec]:
    catalog = load_tasks(root)
    split = load_task_split(root / "tasks" / "splits" / f"{experiment.task_split}.txt")
    return [catalog[item] for item in split if task_filter is None or item in task_filter]


def initial_context(
    root: Path,
    task: TaskSpec,
    harness: HarnessSpec,
    snapshot: GitSnapshot,
    tokenizer: QwenTokenCounter,
) -> tuple[str, tuple[str, ...], int]:
    if harness.interface == "specialized":
        return pack_specialized_channels(
            root, task.task_id, snapshot, task.base_commit, tokenizer
        )
    records = load_ranking_records(ranking_path(root, "E01", "H008", task.task_id))
    candidates = candidates_from_records(snapshot, task.base_commit, records)
    paths = [candidate.path for candidate in candidates]
    if harness.packing == "ranked_snippets":
        return pack_snippets(tokenizer, candidates)
    if harness.packing == "skeletons":
        return pack_skeletons(tokenizer, snapshot, task.base_commit, paths)
    if harness.packing == "whole_files":
        return pack_whole_files(tokenizer, snapshot, task.base_commit, paths)
    if harness.packing == "role_summaries":
        return pack_role_summaries(tokenizer, snapshot, task.base_commit, paths)
    raise InteractiveExperimentError(f"unsupported packing policy {harness.packing}")


def localization_prompt(task: TaskSpec, context: str, phase: str = "initial") -> str:
    return f"ISSUE:\n{task.statement}\n\n{phase.upper()} EVIDENCE:\n{context}"


def parse_query(response: dict[str, Any]) -> str:
    try:
        content = response["choices"][0]["message"]["content"]
        stripped = content.strip()
        if stripped.startswith("```"):
            stripped = stripped.removeprefix("```json").removeprefix("```")
            stripped = stripped.removesuffix("```").strip()
        try:
            value = json.loads(stripped)
        except json.JSONDecodeError:
            start, end = stripped.find("{"), stripped.rfind("}")
            value = json.loads(stripped[start : end + 1])
        query = value["query"]
    except (KeyError, IndexError, TypeError, json.JSONDecodeError) as exc:
        raise InteractiveExperimentError("query-reformulation response is not valid JSON") from exc
    if set(value) != {"query"} or not isinstance(query, str) or not query.strip():
        raise InteractiveExperimentError("query response must contain exactly one non-empty query")
    return query.strip()[:1000]


def identity_for(root: Path, experiment: Any, task: TaskSpec, harness: HarnessSpec, revision: str) -> RunIdentity:
    model = load_models(root)[experiment.model_ids[0]]
    return RunIdentity(
        experiment_id=experiment.experiment_id,
        task_id=task.task_id,
        harness_id=harness.harness_id,
        harness_hash=harness.config_hash,
        model_id=model.model_id,
        model_key=model.expected_inference_key,
        model_config_hash=model.config_hash,
        context_budget=experiment.context_budgets[0],
        seed=experiment.seeds[0],
        repetition=0,
        repository_sha=task.base_commit,
        code_revision=revision,
    )


def run_one_shot_and_queries(
    root: Path,
    repository: Path,
    phase: str,
    task_filter: set[str] | None = None,
    harness_filter: set[str] | None = None,
) -> dict[str, Any]:
    revision = research_code_revision(root)
    experiment = load_experiments(root)["E02"]
    harness_catalog = load_harnesses(root)
    model = load_models(root)[experiment.model_ids[0]]
    tasks = selected_tasks(root, experiment, task_filter)
    wanted = (
        {"H008", "H011", "H013", "H014", "H015"}
        if phase == "one_shot"
        else {"H010", "H012"}
    )
    if harness_filter is not None:
        wanted &= harness_filter
    harnesses = [harness_catalog[item] for item in experiment.harness_ids if item in wanted]
    if not tasks or not harnesses:
        raise InteractiveExperimentError("filters selected no E02 model cells")
    client = LMStudioClient(model, timeout_seconds=experiment.timeout_seconds)
    discovery, resolved = client.resolve()
    loaded = exclusive_loaded(discovery.native_models)
    if loaded != (model.expected_inference_key,):
        raise InteractiveExperimentError(f"E02 model phase requires exclusive Qwen residency: {loaded}")
    tokenizer = QwenTokenCounter()
    snapshot = GitSnapshot(repository)
    rows: list[dict[str, Any]] = []
    for task in tasks:
        for harness in harnesses:
            context, allowed_paths, context_tokens = initial_context(
                root, task, harness, snapshot, tokenizer
            )
            prompt = localization_prompt(task, context)
            if phase == "query":
                stage = root / "results" / "staging" / "E02" / revision / harness.harness_id / task.task_id
                stage.mkdir(parents=True, exist_ok=True)
                output = stage / "query_stage.json"
                if output.exists():
                    rows.append(json.loads(output.read_text(encoding="utf-8")))
                    continue
                started = time.monotonic()
                response = client.chat_completions(
                    resolved.inference_key,
                    [
                        {"role": "system", "content": QUERY_SYSTEM},
                        {"role": "user", "content": prompt},
                    ],
                    max_tokens=1536,
                )
                elapsed = time.monotonic() - started
                query_violation: str | None = None
                try:
                    query = parse_query(response)
                    query_source = "model"
                except InteractiveExperimentError as exc:
                    query = task.statement
                    query_source = "issue_fallback"
                    query_violation = str(exc)
                value = {
                    "task_id": task.task_id,
                    "harness_id": harness.harness_id,
                    "query": query,
                    "query_source": query_source,
                    "protocol_violation": query_violation,
                    "allowed_initial_paths": allowed_paths,
                    "context_tokens": context_tokens,
                    "prompt_sha256": sha256(prompt.encode()).hexdigest(),
                    "elapsed_seconds": elapsed,
                    "usage": response.get("usage", {}),
                    "response": response,
                    "prompt": prompt,
                }
                output.write_text(json.dumps(value, indent=2) + "\n", encoding="utf-8")
                rows.append({key: value[key] for key in value if key not in {"response", "prompt"}})
                continue

            identity = identity_for(root, experiment, task, harness, revision)
            directory = run_directory(root / "results", identity)
            if directory.exists():
                final = json.loads((directory / "final_metrics.json").read_text(encoding="utf-8"))
                rows.append(final)
                continue
            started = time.monotonic()
            response = client.chat_completions(
                resolved.inference_key,
                [
                    {"role": "system", "content": LOCALIZE_SYSTEM},
                    {"role": "user", "content": prompt},
                ],
                max_tokens=model.max_tokens,
            )
            elapsed = time.monotonic() - started
            selection, protocol_violation = score_selection_response(response, set(allowed_paths))
            metrics = retrieval_metrics(selection["files"], task.gold_files)
            final = {
                "run_id": identity.run_id,
                "experiment_id": "E02",
                "task_id": task.task_id,
                "harness_id": harness.harness_id,
                "selected_files": selection["files"],
                "reasoning": selection["reasoning"],
                "metrics": metrics,
                "context_tokens": context_tokens,
                "prompt_sha256": sha256(prompt.encode()).hexdigest(),
                "elapsed_seconds": elapsed,
                "usage": response.get("usage", {}),
                "model_calls": 1,
                "protocol_violation": protocol_violation,
            }
            with EventWriter(root / "results", identity, asdict(harness), resolved.to_dict()) as writer:
                writer.emit("run_started", {"confirmatory": True, "phase": phase})
                writer.emit("model_call", {"elapsed_seconds": elapsed, "usage": response.get("usage", {})})
                writer.write_artifact("prompt.txt", prompt)
                writer.write_artifact("model_response.json", json.dumps(response, indent=2) + "\n")
                writer.write_artifact(
                    "selection.json",
                    json.dumps({**selection, "protocol_violation": protocol_violation}, indent=2) + "\n",
                )
                writer.write_artifact("final_metrics.json", json.dumps(final, indent=2) + "\n")
                writer.emit(
                    "run_finished",
                    {
                        "status": "completed_with_protocol_violation" if protocol_violation else "completed",
                        "metrics": metrics,
                        "protocol_violation": protocol_violation,
                    },
                )
            rows.append(final)
    return {"phase": phase, "code_revision": revision, "run_count": len(rows), "rows": rows}


def run_refined_retrieval(
    root: Path,
    repository: Path,
    task_filter: set[str] | None = None,
    harness_filter: set[str] | None = None,
) -> dict[str, Any]:
    revision = research_code_revision(root)
    experiment = load_experiments(root)["E02"]
    harness_catalog = load_harnesses(root)
    embedding = load_embeddings(root)[experiment.embedding_id]
    tasks = selected_tasks(root, experiment, task_filter)
    wanted = {"H010", "H012"} if harness_filter is None else {"H010", "H012"} & harness_filter
    harnesses = [harness_catalog[item] for item in experiment.harness_ids if item in wanted]
    client = LMStudioEmbeddingClient(embedding, timeout_seconds=120.0)
    runtime = client.resolve()
    loaded = client.loaded_model_keys()
    if tuple(loaded) != (embedding.model_key,):
        raise InteractiveExperimentError(f"refined retrieval requires exclusive embedding residency: {loaded}")
    snapshot = GitSnapshot(repository)
    cache_path = root / "indexes" / "embeddings" / f"{embedding.config_hash}.sqlite3"
    rows: list[dict[str, Any]] = []
    with SQLiteEmbeddingCache(cache_path, embedding) as cache:
        for task in tasks:
            chunks = chunk_snapshot(snapshot, task.base_commit, embedding.chunk_lines, embedding.chunk_overlap_lines, embedding.chunk_char_limit)
            symbols = parse_snapshot(snapshot, task.base_commit)
            exact = ExactRetriever(chunks)
            lexical = BM25FuzzyRetriever(chunks)
            syntax = SyntaxRetriever(symbols)
            dense_base, _ = DenseRetriever.build(chunks, embedding, client, cache)
            dense = FaissFlatRetriever(dense_base)
            graph = SymbolGraph(symbols)
            for harness in harnesses:
                stage = root / "results" / "staging" / "E02" / revision / harness.harness_id / task.task_id
                query_stage = stage / "query_stage.json"
                if not query_stage.exists():
                    raise InteractiveExperimentError(f"missing query stage: {query_stage}")
                output = stage / "refined_ranking.json"
                if output.exists():
                    rows.append({"task_id": task.task_id, "harness_id": harness.harness_id, "status": "resumed"})
                    continue
                query = json.loads(query_stage.read_text(encoding="utf-8"))["query"]
                ranking = retrieve_treatment(harness, query, exact, lexical, syntax, dense, graph, 200)
                payload = [
                    {
                        "rank": rank,
                        "path": item.path,
                        "line_start": item.line_start,
                        "line_end": item.line_end,
                        "score": item.score,
                        "source": item.source,
                        "symbol": item.symbol,
                    }
                    for rank, item in enumerate(ranking, start=1)
                ]
                output.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
                rows.append({"task_id": task.task_id, "harness_id": harness.harness_id, "query": query, "candidate_count": len(payload)})
    return {"phase": "refined_retrieval", "embedding_runtime": runtime, "run_count": len(rows), "rows": rows}


def run_iterative_final(
    root: Path,
    repository: Path,
    task_filter: set[str] | None = None,
    harness_filter: set[str] | None = None,
) -> dict[str, Any]:
    revision = research_code_revision(root)
    experiment = load_experiments(root)["E02"]
    model = load_models(root)[experiment.model_ids[0]]
    harness_catalog = load_harnesses(root)
    tasks = selected_tasks(root, experiment, task_filter)
    wanted = {"H010", "H012"} if harness_filter is None else {"H010", "H012"} & harness_filter
    harnesses = [harness_catalog[item] for item in experiment.harness_ids if item in wanted]
    client = LMStudioClient(model, timeout_seconds=experiment.timeout_seconds)
    discovery, resolved = client.resolve()
    loaded = exclusive_loaded(discovery.native_models)
    if loaded != (model.expected_inference_key,):
        raise InteractiveExperimentError(f"iterative final phase requires exclusive Qwen residency: {loaded}")
    tokenizer = QwenTokenCounter()
    snapshot = GitSnapshot(repository)
    rows: list[dict[str, Any]] = []
    for task in tasks:
        for harness in harnesses:
            identity = identity_for(root, experiment, task, harness, revision)
            directory = run_directory(root / "results", identity)
            if directory.exists():
                rows.append(json.loads((directory / "final_metrics.json").read_text(encoding="utf-8")))
                continue
            stage = root / "results" / "staging" / "E02" / revision / harness.harness_id / task.task_id
            query_value = json.loads((stage / "query_stage.json").read_text(encoding="utf-8"))
            refined_records = load_ranking_records(stage / "refined_ranking.json")
            refined_candidates = candidates_from_records(snapshot, task.base_commit, refined_records)
            refined_text, refined_paths, refined_tokens = pack_snippets(
                tokenizer, refined_candidates, EVIDENCE_TOKEN_BUDGET // 2
            )
            initial_text, initial_paths, initial_tokens = initial_context(root, task, harness, snapshot, tokenizer)
            # Repack initial evidence to half the budget for a fair two-round total.
            if harness.interface == "specialized":
                initial_text, initial_paths, initial_tokens = pack_specialized_channels(
                    root, task.task_id, snapshot, task.base_commit, tokenizer, EVIDENCE_TOKEN_BUDGET // 2
                )
            else:
                initial_records = load_ranking_records(ranking_path(root, "E01", "H008", task.task_id))
                initial_candidates = candidates_from_records(snapshot, task.base_commit, initial_records)
                initial_text, initial_paths, initial_tokens = pack_snippets(
                    tokenizer, initial_candidates, EVIDENCE_TOKEN_BUDGET // 2
                )
            context = (
                f"FOLLOW-UP QUERY:\n{query_value['query']}\n\nINITIAL EVIDENCE:\n{initial_text}"
                f"\n\nREFINED EVIDENCE:\n{refined_text}"
            )
            prompt = localization_prompt(task, context, phase="iterative")
            allowed = tuple(dict.fromkeys((*initial_paths, *refined_paths)))
            started = time.monotonic()
            response = client.chat_completions(
                resolved.inference_key,
                [{"role": "system", "content": LOCALIZE_SYSTEM}, {"role": "user", "content": prompt}],
                max_tokens=model.max_tokens,
            )
            elapsed = time.monotonic() - started
            selection, protocol_violation = score_selection_response(response, set(allowed))
            metrics = retrieval_metrics(selection["files"], task.gold_files)
            first_usage = query_value.get("usage", {})
            second_usage = response.get("usage", {})
            final = {
                "run_id": identity.run_id,
                "experiment_id": "E02",
                "task_id": task.task_id,
                "harness_id": harness.harness_id,
                "selected_files": selection["files"],
                "reasoning": selection["reasoning"],
                "query": query_value["query"],
                "metrics": metrics,
                "context_tokens": initial_tokens + refined_tokens,
                "prompt_sha256": sha256(prompt.encode()).hexdigest(),
                "elapsed_seconds": query_value["elapsed_seconds"] + elapsed,
                "usage": {"first": first_usage, "second": second_usage},
                "model_calls": 2,
                "protocol_violation": protocol_violation,
            }
            with EventWriter(root / "results", identity, asdict(harness), resolved.to_dict()) as writer:
                writer.emit("run_started", {"confirmatory": True, "phase": "iterative"})
                writer.emit("model_call", {"stage": "query", "elapsed_seconds": query_value["elapsed_seconds"], "usage": first_usage})
                writer.emit("model_call", {"stage": "final", "elapsed_seconds": elapsed, "usage": second_usage})
                writer.write_artifact("prompt_query.txt", query_value["prompt"])
                writer.write_artifact("prompt_final.txt", prompt)
                writer.write_artifact("response_query.json", json.dumps(query_value["response"], indent=2) + "\n")
                writer.write_artifact("response_final.json", json.dumps(response, indent=2) + "\n")
                writer.write_artifact(
                    "selection.json",
                    json.dumps({**selection, "protocol_violation": protocol_violation}, indent=2) + "\n",
                )
                writer.write_artifact("final_metrics.json", json.dumps(final, indent=2) + "\n")
                writer.emit(
                    "run_finished",
                    {
                        "status": "completed_with_protocol_violation" if protocol_violation else "completed",
                        "metrics": metrics,
                        "protocol_violation": protocol_violation,
                    },
                )
            rows.append(final)
    return {"phase": "iterative_final", "code_revision": revision, "run_count": len(rows), "rows": rows}