"""Shared pytest fixtures for TrialPath test suite."""

from __future__ import annotations

import os
from unittest.mock import AsyncMock, MagicMock, patch

import pytest

try:
    from dotenv import load_dotenv

    load_dotenv()
except ImportError:
    pass

from app.services.mock_data import (
    MOCK_ELIGIBILITY_LEDGERS,
    MOCK_PATIENT_PROFILE,
    MOCK_TRIAL_CANDIDATES,
)
from trialpath.models import (
    EligibilityLedger,
    PatientProfile,
    SearchAnchors,
    TrialCandidate,
)

# ---------------------------------------------------------------------------
# Sample data fixtures
# ---------------------------------------------------------------------------


@pytest.fixture()
def sample_profile() -> PatientProfile:
    """Return the shared mock patient profile."""
    return MOCK_PATIENT_PROFILE


@pytest.fixture()
def sample_trials() -> list[TrialCandidate]:
    """Return the shared mock trial candidates."""
    return list(MOCK_TRIAL_CANDIDATES)


@pytest.fixture()
def sample_ledgers() -> list[EligibilityLedger]:
    """Return the shared mock eligibility ledgers."""
    return list(MOCK_ELIGIBILITY_LEDGERS)


@pytest.fixture()
def sample_anchors(sample_profile: PatientProfile) -> SearchAnchors:
    """Build SearchAnchors from the mock profile."""
    assert sample_profile.diagnosis is not None
    assert sample_profile.performance_status is not None
    return SearchAnchors(
        condition=sample_profile.diagnosis.primary_condition,
        subtype=sample_profile.diagnosis.histology,
        biomarkers=[b.name for b in sample_profile.biomarkers],
        stage=sample_profile.diagnosis.stage,
        age=sample_profile.demographics.age,
        performance_status_max=sample_profile.performance_status.value,
    )


# ---------------------------------------------------------------------------
# Service mock fixtures
# ---------------------------------------------------------------------------


@pytest.fixture()
def mock_medgemma():
    """Patch MedGemmaExtractor with a mock that returns sample profile data."""
    with patch("trialpath.services.medgemma_extractor.MedGemmaExtractor") as cls:
        instance = MagicMock()
        instance.extract = AsyncMock(return_value=MOCK_PATIENT_PROFILE)
        instance.evaluate_medical_criterion = AsyncMock(
            return_value={
                "decision": "met",
                "confidence": 0.9,
                "reasoning": "Criterion satisfied based on profile data.",
            }
        )
        cls.return_value = instance
        yield instance


@pytest.fixture()
def mock_gemini():
    """Patch GeminiPlanner with a mock that returns structured outputs."""
    with patch("trialpath.services.gemini_planner.GeminiPlanner") as cls:
        instance = MagicMock()
        instance.generate_search_anchors = AsyncMock(
            return_value=SearchAnchors(
                condition="Non-Small Cell Lung Cancer",
                biomarkers=["EGFR"],
                stage="IIIB",
            )
        )
        instance.evaluate_eligibility = AsyncMock(
            return_value={
                "overall_assessment": "uncertain",
                "criteria": [],
            }
        )
        instance.refine_search = AsyncMock(
            return_value=SearchAnchors(
                condition="NSCLC",
                biomarkers=["EGFR"],
                stage="IIIB",
            )
        )
        instance.relax_search = AsyncMock(
            return_value=SearchAnchors(
                condition="Lung Cancer",
            )
        )
        instance.slice_criteria = AsyncMock(
            return_value=[
                {"text": "Age >= 18", "type": "structural"},
                {"text": "EGFR mutation positive", "type": "medical"},
            ]
        )
        instance.evaluate_structural_criterion = AsyncMock(
            return_value={
                "decision": "met",
                "confidence": 0.95,
                "reasoning": "Patient is 62, meets age requirement.",
            }
        )
        instance.aggregate_assessments = AsyncMock(return_value=MOCK_ELIGIBILITY_LEDGERS[0])
        instance.analyze_gaps = AsyncMock(
            return_value=[
                {
                    "description": "Brain MRI status unknown",
                    "recommended_action": "Order brain MRI",
                    "clinical_importance": "high",
                }
            ]
        )
        cls.return_value = instance
        yield instance


@pytest.fixture()
def mock_mcp():
    """Patch ClinicalTrialsMCPClient with a mock."""
    with patch("trialpath.services.mcp_client.ClinicalTrialsMCPClient") as cls:
        instance = AsyncMock()
        instance.search_studies.return_value = {
            "studies": [t.model_dump() for t in MOCK_TRIAL_CANDIDATES]
        }
        instance.get_study.return_value = MOCK_TRIAL_CANDIDATES[0].model_dump()
        cls.return_value = instance
        yield instance


# ---------------------------------------------------------------------------
# Live service fixtures (require real API keys / running servers)
# ---------------------------------------------------------------------------


@pytest.fixture(scope="session")
def live_env():
    """Ensure env vars are loaded; skip the entire session block if missing."""
    if not os.environ.get("GEMINI_API_KEY"):
        pytest.skip("GEMINI_API_KEY not set — skipping live tests")


@pytest.fixture(scope="session")
def live_gemini(live_env):
    """Return a real GeminiPlanner wired to the Gemini API."""
    from trialpath.services.gemini_planner import GeminiPlanner

    return GeminiPlanner()


@pytest.fixture(scope="session")
def live_mcp_client(live_env):
    """Return a real ClinicalTrialsMCPClient."""
    from trialpath.services.mcp_client import ClinicalTrialsMCPClient

    return ClinicalTrialsMCPClient()


@pytest.fixture(scope="session")
def live_medgemma(live_env):
    """Return a real MedGemmaExtractor (skip if no HF_TOKEN)."""
    if not os.environ.get("HF_TOKEN"):
        pytest.skip("HF_TOKEN not set — skipping MedGemma live tests")

    from trialpath.services.medgemma_extractor import MedGemmaExtractor

    return MedGemmaExtractor()