from __future__ import annotations

import json
import logging
from pathlib import Path

from jsonschema import Draft202012Validator

from gcmd_classifier.articles import validate_article_records
from gcmd_classifier.config import ModelSettings
from gcmd_classifier.llm import FakeModelClient
from gcmd_classifier.logging_config import sanitize_log_details
from gcmd_classifier.models import (
    ArticleClassificationOutcome,
    ArticleProcessingStatus,
    ArticleRecord,
)
from gcmd_classifier.persistence import ArticleResultCache, JsonResultStore
from gcmd_classifier.pipeline import classify_article, run_batch
from gcmd_classifier.vocabulary import load_vocabulary

FIXTURE_PATH = Path("tests/fixtures/gcmd_hierarchy_small.json")
FULL_ARTICLES_PATH = Path("data/articles.json")
FULL_HIERARCHY_PATH = Path("data/gcmd_hierarchy.json")
RUN_SUMMARY_SCHEMA_PATH = Path("schemas/run_summary.schema.json")


def _index():
    return load_vocabulary(FIXTURE_PATH)


def _article(abstract: str = "Atmospheric carbon dioxide profiles are discussed.") -> ArticleRecord:
    return ArticleRecord(
        DOI="10.example/pipeline",
        Title="Atmospheric carbon dioxide observations",
        Year=2025,
        Abstract=abstract,
    )


def _decision(
    candidate_id: str,
    *,
    support_type: str = "explicit",
    confidence: float = 0.8,
) -> dict:
    return {
        "candidate_id": candidate_id,
        "confidence": confidence,
        "evidence": f"Evidence for {candidate_id}.",
        "support_type": support_type,
        "reason": f"Reason for {candidate_id}.",
    }


def _no_topic(reason: str = "No supported Topic.") -> dict:
    return {"selected": [], "no_selection_reason": reason}


def _select_topic(candidate_id: str = "topic_0001") -> dict:
    return {"selected": [_decision(candidate_id)]}


def _select_term(candidate_id: str = "term_0001") -> dict:
    return {"selected": [_decision(candidate_id)], "stop_at_parent": False}


def _select_variable(*candidate_ids: str) -> dict:
    return {
        "selected": [_decision(candidate_id) for candidate_id in candidate_ids],
        "stop_at_parent": False,
    }


def _stop(reason: str) -> dict:
    return {"selected": [], "stop_at_parent": True, "stop_reason": reason}


def test_single_article_deep_variable_classification_is_accepted() -> None:
    client = FakeModelClient(
        [
            _select_topic(),
            _select_term(),
            _select_variable("variable_0001"),
            _select_variable("variable_0001"),
            _select_variable("variable_0001"),
        ]
    )

    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
    )

    assert result.processing_status is ArticleProcessingStatus.COMPLETED
    assert result.classification_outcome is ArticleClassificationOutcome.CLASSIFIED
    assert [record.UUID for record in result.classifications] == ["vl3-carbon-dioxide-profiles"]
    assert result.classifications[0].deterministic_validation.valid is True
    assert result.processing_metadata.model_calls == 5


def test_article_stops_at_topic_and_becomes_topic_classification() -> None:
    client = FakeModelClient([_select_topic(), _stop("Topic is the deepest supported level.")])

    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
    )

    assert result.classifications[0].UUID == "topic-atmosphere"
    assert result.classifications[0].level == "Topic"
    assert result.classifications[0].reason_for_stopping == "Topic is the deepest supported level."


def test_review_risk_flagging_preserves_accepted_topic_classification() -> None:
    client = FakeModelClient(
        [
            {"selected": [_decision("topic_0001", support_type="inferred", confidence=0.9)]},
            _stop("Topic is the deepest supported level."),
        ]
    )

    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
    )

    classification = result.classifications[0]
    assert classification.UUID == "topic-atmosphere"
    assert classification.level == "Topic"
    assert classification.final_status == "accepted"
    assert classification.review_required is True
    assert classification.warnings[-1].code == "REVIEW_RECOMMENDED_WEAK_SUPPORT"


def test_article_stops_at_term_and_becomes_term_classification() -> None:
    client = FakeModelClient(
        [_select_topic(), _select_term(), _stop("Term is the deepest supported level.")]
    )

    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
    )

    assert result.classifications[0].UUID == "term-atmospheric-chemistry"
    assert result.classifications[0].level == "Term"
    assert result.classifications[0].reason_for_stopping == "Term is the deepest supported level."


def test_no_topic_selection_is_completed_not_classified() -> None:
    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic("Not Earth science.")]),
        settings=ModelSettings(),
    )

    assert result.processing_status is ArticleProcessingStatus.COMPLETED
    assert result.classification_outcome is ArticleClassificationOutcome.NOT_CLASSIFIED
    assert result.classifications == ()
    assert result.no_classification_reason == "Not Earth science."


def test_invalid_model_candidate_produces_structured_error() -> None:
    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=FakeModelClient([_select_topic("missing_topic")]),
        settings=ModelSettings(),
    )

    assert result.processing_status is ArticleProcessingStatus.FAILED
    assert result.classifications == ()
    assert result.errors[0].stage == "topic_routing"
    assert result.errors[0].code == "UnknownCandidateIDError"


def test_partial_branch_failure_preserves_successful_sibling_outcome() -> None:
    client = FakeModelClient(
        [
            _select_topic(),
            _select_term(),
            _select_variable("variable_0001"),
            _select_variable("variable_0001", "variable_0002"),
            _select_variable("missing_variable"),
        ]
    )

    result = classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
    )

    assert result.processing_status is ArticleProcessingStatus.PARTIAL
    assert result.classification_outcome is ArticleClassificationOutcome.CLASSIFIED
    assert [record.UUID for record in result.classifications] == ["vl2-methane"]
    assert result.errors
    assert result.errors[0].stage == "variable_descent"


def test_empty_abstract_remains_valid_and_can_be_processed() -> None:
    result = classify_article(
        article=_article(abstract=""),
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic("Title only is insufficient.")]),
        settings=ModelSettings(),
    )

    assert result.Abstract == ""
    assert result.processing_metadata.abstract_available is False
    assert result.processing_status is ArticleProcessingStatus.COMPLETED


def test_batch_processes_multiple_valid_articles_and_preserves_order(tmp_path: Path) -> None:
    load_result = validate_article_records(
        [
            {"DOI": "10.example/one", "Title": "One", "Year": 2025, "Abstract": ""},
            {"DOI": "10.example/two", "Title": "Two", "Year": 2025, "Abstract": ""},
        ]
    )
    client = FakeModelClient([_no_topic("No Topic one."), _no_topic("No Topic two.")])

    batch = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
        store=JsonResultStore(tmp_path / "results"),
    )

    assert [result.DOI for result in batch.results] == ["10.example/one", "10.example/two"]
    assert batch.summary.processed_articles == 2
    assert batch.summary.articles_completed == 2
    assert batch.summary.articles_not_classified == 2


def test_batch_continues_after_one_article_failure(tmp_path: Path) -> None:
    load_result = validate_article_records(
        [
            {"DOI": "10.example/one", "Title": "One", "Year": 2025, "Abstract": ""},
            {"DOI": "10.example/two", "Title": "Two", "Year": 2025, "Abstract": ""},
            {"DOI": "10.example/three", "Title": "Three", "Year": 2025, "Abstract": ""},
        ]
    )
    client = FakeModelClient([_no_topic(), _select_topic("missing_topic"), _no_topic()])

    batch = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=client,
        settings=ModelSettings(),
        store=JsonResultStore(tmp_path / "results"),
    )

    assert [result.DOI for result in batch.results] == [
        "10.example/one",
        "10.example/two",
        "10.example/three",
    ]
    assert batch.summary.articles_failed == 1
    assert batch.summary.articles_completed == 2
    assert batch.results[1].errors[0].code == "UnknownCandidateIDError"


def test_batch_reports_invalid_source_records_without_inventing_doi(tmp_path: Path) -> None:
    load_result = validate_article_records(
        [
            {"DOI": "10.example/valid", "Title": "Valid", "Year": 2025, "Abstract": ""},
            {"DOI": "", "Title": "Invalid", "Year": 2025, "Abstract": ""},
        ]
    )

    batch = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic()]),
        settings=ModelSettings(),
        store=JsonResultStore(tmp_path / "results"),
    )

    assert batch.summary.articles_received == 2
    assert batch.summary.valid_article_records == 1
    assert batch.summary.invalid_source_records == 1
    assert batch.summary.errors[0].DOI == ""
    assert [result.DOI for result in batch.results] == ["10.example/valid"]


def test_batch_cache_hit_is_completed_not_skipped_and_force_reprocess_bypasses_cache(
    tmp_path: Path,
) -> None:
    load_result = validate_article_records(
        [{"DOI": "10.example/cache", "Title": "Cache", "Year": 2025, "Abstract": ""}]
    )
    cache = ArticleResultCache(tmp_path / "cache")
    store = JsonResultStore(tmp_path / "results")

    first = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic("First run.")]),
        settings=ModelSettings(),
        store=store,
        cache=cache,
    )
    second = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=FakeModelClient([]),
        settings=ModelSettings(),
        store=store,
        cache=cache,
    )
    forced = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic("Forced run.")]),
        settings=ModelSettings(),
        store=store,
        cache=cache,
        force_reprocess=True,
    )

    assert first.summary.cache_misses == 1
    assert second.summary.cache_hits == 1
    assert second.results[0].processing_metadata.cache_used is True
    assert second.results[0].processing_status is ArticleProcessingStatus.COMPLETED
    assert forced.summary.cache_hits == 0
    assert forced.summary.cache_misses == 1
    assert forced.results[0].no_classification_reason == "Forced run."


def test_batch_output_summary_validates_against_schema(tmp_path: Path) -> None:
    load_result = validate_article_records(
        [{"DOI": "10.example/schema", "Title": "Schema", "Year": 2025, "Abstract": ""}]
    )
    batch = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic()]),
        settings=ModelSettings(),
        store=JsonResultStore(tmp_path / "results"),
    )

    schema = json.loads(RUN_SUMMARY_SCHEMA_PATH.read_text())
    Draft202012Validator(schema).validate(batch.summary.model_dump(mode="json"))


def test_structured_diagnostics_do_not_include_secrets() -> None:
    details = sanitize_log_details(
        {
            "DOI": "10.example/redacted",
            "api_key": "raw-secret-value",
            "nested": {"token": "raw-secret-value"},
        }
    )

    assert details["api_key"] == "[REDACTED]"
    assert details["nested"]["token"] == "[REDACTED]"
    assert "raw-secret-value" not in json.dumps(details)


def test_model_retry_counts_and_cache_flags_appear_in_metadata(tmp_path: Path) -> None:
    load_result = validate_article_records(
        [{"DOI": "10.example/meta", "Title": "Meta", "Year": 2025, "Abstract": ""}]
    )
    batch = run_batch(
        article_load_result=load_result,
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic()]),
        settings=ModelSettings(max_retries=3),
        store=JsonResultStore(tmp_path / "results"),
    )

    metadata = batch.results[0].processing_metadata
    assert metadata.model_parameters["max_retries"] == 3
    assert metadata.cache_used is False
    assert metadata.model_calls == 1


def test_full_data_smoke_reports_current_invalid_article_without_modifying_sources(
    tmp_path: Path,
) -> None:
    hierarchy_before = FULL_HIERARCHY_PATH.read_bytes()
    articles_before = FULL_ARTICLES_PATH.read_bytes()
    raw_articles = json.loads(articles_before)
    load_result = validate_article_records(raw_articles)
    subset_load_result = load_result.model_copy(update={"articles": load_result.articles[:1]})

    batch = run_batch(
        article_load_result=subset_load_result,
        vocabulary=load_vocabulary(FULL_HIERARCHY_PATH),
        model_client=FakeModelClient([_no_topic()]),
        settings=ModelSettings(),
        store=JsonResultStore(tmp_path / "results"),
    )

    assert isinstance(raw_articles, list)
    assert load_result.source_count == len(raw_articles)
    assert len(load_result.articles) == len(raw_articles) - len(load_result.errors)
    assert len({article.DOI for article in load_result.articles}) == len(load_result.articles)
    if load_result.errors:
        assert all(error.code and error.message for error in load_result.errors)
        assert all(
            error.index is None or 0 <= error.index < len(raw_articles)
            for error in load_result.errors
        )
    assert batch.summary.articles_received == len(raw_articles)
    assert batch.summary.invalid_source_records == len(load_result.errors)
    assert batch.summary.processed_articles == 1
    assert FULL_HIERARCHY_PATH.read_bytes() == hierarchy_before
    assert FULL_ARTICLES_PATH.read_bytes() == articles_before


def test_logging_records_processing_events_without_secrets(caplog) -> None:
    logger = logging.getLogger("gcmd_classifier.tests.pipeline")
    caplog.set_level(logging.INFO, logger=logger.name)

    classify_article(
        article=_article(),
        vocabulary=_index(),
        model_client=FakeModelClient([_no_topic()]),
        settings=ModelSettings(),
        logger=logger,
        relevant_config={"api_key": "should-not-log"},
    )

    assert any(record.__dict__.get("event") == "article_started" for record in caplog.records)
    serialized = "\n".join(str(record.__dict__) for record in caplog.records)
    assert "should-not-log" not in serialized