"""Restore canonical Postgres integration-test fixtures.

The experimental ingest pipeline replaced stable chunk IDs (``chk-ret-*``,
``chk-test-*``) with random UUIDs and broke retrieval/integration tests.

This script puts the database back to the known-good fixture state defined in:
  - tests/integration/test_retrieval_api_postgres.py
  - tests/integration/test_memory_api_postgres.py

Usage:
    python3 scripts/restore_postgres_fixtures.py
"""

from __future__ import annotations

import json
import os
import sys
from datetime import UTC, datetime
from pathlib import Path

REPO_ROOT = Path(__file__).resolve().parent.parent
_env_file = REPO_ROOT / ".env"
if _env_file.exists():
    for raw_line in _env_file.read_text().splitlines():
        line = raw_line.strip()
        if not line or line.startswith("#") or "=" not in line:
            continue
        key, value = line.split("=", 1)
        os.environ.setdefault(key.strip(), value.strip())

import psycopg  # noqa: E402

DSN = os.getenv(
    "AKS_DATABASE_URL",
    "postgresql+psycopg://mobcoderid-296@localhost/ai_knowledge_spine",
).replace("postgresql+psycopg://", "postgresql://", 1)

NOW = datetime.now(UTC)

# Stable fixture chunks — do not change IDs without updating integration tests.
FIXTURE_CHUNKS = [
    {
        "chunk_id": "chk-ret-lbl-1",
        "source_id": "LBL-NSCLC-RET-EMA-2026",
        "version_id": "ver-ret-lbl-1",
        "text": "The approved dose is 80 mg once daily in first-line metastatic NSCLC within EU label boundaries.",
        "claim_type": "DOSE",
        "section_path": "2 POSOLOGY",
        "page_start": 2,
        "page_end": 2,
        "token_count": 15,
        "audience_fit": ["HCP", "Internal"],
    },
    {
        "chunk_id": "chk-ret-csr-1",
        "source_id": "DOC-CSR-NSCLC-RET-2026",
        "version_id": "ver-ret-csr-1",
        "text": "Progression-free survival improved in EGFR-positive NSCLC for DRUG-A.",
        "claim_type": "EFFICACY",
        "section_path": "RESULTS",
        "page_start": 5,
        "page_end": 5,
        "token_count": 10,
        "audience_fit": ["HCP", "Internal"],
    },
    {
        "chunk_id": "chk-ret-sop-1",
        "source_id": "SOP-MED-NSCLC-RET-2026",
        "version_id": "ver-ret-sop-1",
        "text": "Internal responders should preserve dose boundaries and medical citation discipline.",
        "claim_type": "DOSE",
        "section_path": "DOSING GUIDANCE",
        "page_start": 1,
        "page_end": 1,
        "token_count": 10,
        "audience_fit": ["Internal"],
    },
    {
        "chunk_id": "chk-test-lbl-1",
        "source_id": "LBL-NSCLC-TEST-EMA-2026",
        "version_id": "ver-test-lbl-1",
        "text": "The recommended dose is 80 mg once daily in first-line metastatic NSCLC within approved EU label boundaries.",
        "claim_type": "DOSE",
        "section_path": "2 POSOLOGY",
        "page_start": 2,
        "page_end": 2,
        "token_count": 16,
        "audience_fit": ["HCP", "Internal"],
    },
    {
        "chunk_id": "chk-test-csr-1",
        "source_id": "DOC-CSR-NSCLC-TEST-2026",
        "version_id": "ver-test-csr-1",
        "text": "DRUG-A improved progression-free survival in EGFR-positive NSCLC in the study population.",
        "claim_type": "EFFICACY",
        "section_path": "RESULTS",
        "page_start": 5,
        "page_end": 5,
        "token_count": 12,
        "audience_fit": ["HCP", "Internal"],
    },
]

FIXTURE_LINKS = [
    ("clm-ret-lbl-1", "chk-ret-lbl-1", "LBL-NSCLC-RET-EMA-2026"),
    ("clm-ret-csr-1", "chk-ret-csr-1", "DOC-CSR-NSCLC-RET-2026"),
    ("clm-test-lbl-1", "chk-test-lbl-1", "LBL-NSCLC-TEST-EMA-2026"),
    ("clm-test-csr-1", "chk-test-csr-1", "DOC-CSR-NSCLC-TEST-2026"),
]

PROTECTED_SOURCE_IDS = {row["source_id"] for row in FIXTURE_CHUNKS}


def restore() -> None:
    conn = psycopg.connect(DSN)
    try:
        with conn:
            with conn.cursor() as cur:
                cur.execute(
                    "DELETE FROM chunks WHERE source_id = ANY(%s)",
                    (list(PROTECTED_SOURCE_IDS),),
                )
                deleted_chunks = cur.rowcount
                for row in FIXTURE_CHUNKS:
                    cur.execute(
                        """
                        INSERT INTO chunks (
                            chunk_id, source_id, version_id, text, claim_type,
                            section_path, page_start, page_end, token_count,
                            audience_fit, geography_fit, therapy_area,
                            embedding_id, created_at
                        ) VALUES (
                            %(chunk_id)s, %(source_id)s, %(version_id)s, %(text)s, %(claim_type)s,
                            %(section_path)s, %(page_start)s, %(page_end)s, %(token_count)s,
                            %(audience_fit)s::json, 'EU / EMA', 'NSCLC',
                            NULL, %(created_at)s
                        )
                        """,
                        {**row, "audience_fit": json.dumps(row["audience_fit"]), "created_at": NOW},
                    )

                cur.execute(
                    "DELETE FROM claim_evidence_links WHERE claim_id = ANY(%s)",
                    ([link[0] for link in FIXTURE_LINKS],),
                )
                for claim_id, chunk_id, source_id in FIXTURE_LINKS:
                    cur.execute(
                        """
                        INSERT INTO claim_evidence_links (
                            claim_id, chunk_id, source_id, support_type,
                            extraction_confidence, is_primary_support
                        ) VALUES (%s, %s, %s, 'PRIMARY', 0.99, TRUE)
                        ON CONFLICT DO NOTHING
                        """,
                        (claim_id, chunk_id, source_id),
                    )

                print(f"Restored {len(FIXTURE_CHUNKS)} fixture chunks (removed {deleted_chunks} prior rows).")
                print(f"Restored {len(FIXTURE_LINKS)} claim_evidence_links.")
                cur.execute("SELECT COUNT(*) FROM chunks")
                print(f"chunks total: {cur.fetchone()[0]}")
                cur.execute(
                    """
                    DELETE FROM chunk_embeddings ce
                    WHERE NOT EXISTS (SELECT 1 FROM chunks c WHERE c.chunk_id = ce.chunk_id)
                    """
                )
                orphans_removed = cur.rowcount or 0
                if orphans_removed:
                    print(f"Removed {orphans_removed} orphan chunk_embeddings.")
    finally:
        conn.close()


if __name__ == "__main__":
    restore()
    sys.exit(0)