"""
CSV header normalization for Signal.

Maps messy supplier CSV exports to canonical ShipmentRecord fields.
Tolerates header drift, alternative column names, and common date formats.
"""

import csv
import io
import re
from datetime import date, datetime
from typing import Optional

import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from core.coverage_calculator import ShipmentRecord

HEADER_MAP: dict[str, list[str]] = {
    "patient_id": [
        "patient_id", "patientid", "patient id", "pt_id", "pt id",
        "mrn", "account_number", "account number", "account_no",
        "patient_account", "acct_no", "acct no", "acct #", "acct#",
        "id", "patient", "member_id", "member id",
        "external patient ref", "external_patient_ref", "external ref",
    ],
    "device_type": [
        "device_type", "device type", "device", "devicetype",
        "product_type", "product type", "product", "item",
        "item_description", "item description", "hcpcs_description",
        "hcpcs description", "description", "product_name",
        "dme", "dme description", "dme_description", "dme desc",
        "equipment", "equipment description",
    ],
    "shipment_date": [
        "shipment_date", "shipment date", "ship_date", "ship date",
        "dispense_date", "dispense date", "dispensedate",
        "service_date", "service date",
        "order_date", "order date", "date_of_service", "dos",
        "fill_date", "fill date", "last_ship_date", "last ship date",
    ],
    "quantity": [
        "quantity", "qty", "units", "count", "qty_dispensed",
        "units_dispensed", "quantity_dispensed", "qty_shipped",
    ],
    "payer": [
        "payer", "insurance", "insurance_name", "insurance name",
        "plan", "plan_name", "plan name", "payer_name", "payer name",
        "primary_payer", "primary payer", "ins_name", "carrier",
    ],
    "component": [
        "component", "item_type", "component_type", "type", "supply_type",
    ],
    "csv_visit_date": [
        "visit_date", "visit date", "qualifying_visit_date", "qualifying visit date",
        "last_visit_date", "last visit date", "face_to_face_date", "f2f_date",
        "encounter_date", "encounter date", "physician_visit_date",
    ],
    "csv_swo_status": [
        "swo_status", "swo status", "swo", "standing_written_order",
        "standing written order", "order_status", "order status",
    ],
    "csv_pecos_verified": [
        "pecos_verified", "pecos verified", "pecos", "pecos_status",
        "enrollment_verified", "enrollment verified",
    ],
    "csv_pa_status": [
        "pa_status", "pa status", "prior_auth_status", "prior auth status",
        "prior_authorization_status", "auth_status", "pa", "authorization",
    ],
    "csv_diagnosis_on_file": [
        "diagnosis_on_file", "diagnosis on file", "diagnosis", "dx_on_file",
        "dx on file", "icd_on_file", "icd on file",
    ],
    "csv_transfer_from": [
        "transfer_from", "transfer from", "previous_supplier", "previous supplier",
        "prior_supplier", "prior supplier", "transfer_status", "transferred_from",
    ],
    "order_number": [
        "order_number", "order number", "order_no", "order no", "order#",
        "claim_number", "claim number", "brightree_order", "order_id",
        "rx_number", "rx number", "rx#", "dispense_number",
    ],
    "hcpcs": [
        "hcpcs", "hcpcs_code", "hcpcs code", "procedure_code", "procedure code",
        "billing_code", "billing code", "item_code", "item code", "hcpc",
    ],
}

DEVICE_MAP: dict[str, str] = {
    "dexcom g7": "dexcom_g7",
    "dexcom_g7": "dexcom_g7",
    "dexcomg7": "dexcom_g7",
    "g7": "dexcom_g7",
    "dexcom g6": "dexcom_g6",
    "dexcom_g6": "dexcom_g6",
    "dexcomg6": "dexcom_g6",
    "g6": "dexcom_g6",
    "freestyle libre 2": "freestyle_libre_2",
    "freestyle_libre_2": "freestyle_libre_2",
    "freestylelibre2": "freestyle_libre_2",
    "libre 2": "freestyle_libre_2",
    "libre2": "freestyle_libre_2",
    "fsl2": "freestyle_libre_2",
    "fs libre 2": "freestyle_libre_2",
    "freestyle libre 3": "freestyle_libre_3",
    "freestyle_libre_3": "freestyle_libre_3",
    "freestylelibre3": "freestyle_libre_3",
    "libre 3": "freestyle_libre_3",
    "libre3": "freestyle_libre_3",
    "fsl3": "freestyle_libre_3",
    "fs libre 3": "freestyle_libre_3",
    "omnipod 5": "omnipod_5",
    "omnipod_5": "omnipod_5",
    "omnipod5": "omnipod_5",
    "omnipod": "omnipod_5",
    "op5": "omnipod_5",
}

DATE_FORMATS = [
    "%Y-%m-%d",
    "%m/%d/%Y",
    "%m-%d-%Y",
    "%d/%m/%Y",
    "%m/%d/%y",
    "%Y%m%d",
    "%d-%b-%Y",
    "%b %d, %Y",
    "%B %d, %Y",
    "%m/%d/%Y %H:%M:%S",
    "%Y-%m-%dT%H:%M:%S",
]


def _normalize_key(s: str) -> str:
    return s.strip().lower().replace("-", " ").replace("_", " ")


def _map_header(raw: str) -> Optional[str]:
    key = _normalize_key(raw)
    for canonical, aliases in HEADER_MAP.items():
        if key in [_normalize_key(a) for a in aliases]:
            return canonical
    return None


def _map_header_with_confidence(raw: str) -> tuple[Optional[str], str]:
    """Return (canonical_field, confidence) where confidence is 'high' or 'inferred'."""
    key = _normalize_key(raw)
    for canonical, aliases in HEADER_MAP.items():
        if key == _normalize_key(canonical):
            return canonical, "high"
        if key in [_normalize_key(a) for a in aliases]:
            return canonical, "inferred"
    return None, "unmapped"


def _parse_date(value: str) -> Optional[date]:
    value = value.strip()
    for fmt in DATE_FORMATS:
        try:
            return datetime.strptime(value, fmt).date()
        except ValueError:
            continue
    return None


def _normalize_device(value: str) -> Optional[str]:
    key = _normalize_key(value)
    key_compact = re.sub(r"\s+", "", key)
    for alias, canonical in DEVICE_MAP.items():
        alias_compact = re.sub(r"\s+", "", alias)
        if key == alias or key_compact == alias_compact:
            return canonical
    return None


def normalize_csv(text: str) -> tuple[list[ShipmentRecord], list[str], dict]:
    """
    Parse raw CSV text and return (records, skipped_reasons, mapping_summary).
    Tolerates header drift and normalizes device/payer/date values.

    mapping_summary format:
        {
          "mapped": {canonical_field: {"raw_header": str, "confidence": "high"|"inferred"}},
          "unmapped_columns": [str],
          "required_missing": [str],
        }
    """
    reader = csv.DictReader(io.StringIO(text.strip()))
    if not reader.fieldnames:
        return [], ["No headers found in file"], {}

    column_map: dict[str, str] = {}
    mapping_detail: dict[str, dict] = {}
    unmapped_columns: list[str] = []

    for raw_header in reader.fieldnames:
        canonical, confidence = _map_header_with_confidence(raw_header)
        if canonical:
            column_map[raw_header] = canonical
            mapping_detail[canonical] = {"raw_header": raw_header, "confidence": confidence}
        else:
            unmapped_columns.append(raw_header)

    required_fields = {"patient_id", "device_type", "shipment_date"}
    required_missing = [f for f in required_fields if f not in mapping_detail]

    mapping_summary = {
        "mapped": mapping_detail,
        "unmapped_columns": unmapped_columns,
        "required_missing": required_missing,
    }

    records: list[ShipmentRecord] = []
    skipped: list[str] = []

    for i, row in enumerate(reader, start=2):  # noqa: B007
        mapped: dict[str, str] = {}
        for raw_h, canonical in column_map.items():
            mapped[canonical] = (row.get(raw_h) or "").strip()

        patient_id = mapped.get("patient_id", "").strip()
        if not patient_id:
            skipped.append(f"Row {i}: missing patient_id")
            continue

        raw_device = mapped.get("device_type", "")
        device_type = _normalize_device(raw_device)
        if not device_type:
            skipped.append(f"Row {i} ({patient_id}): unrecognized device '{raw_device}'")
            continue

        raw_date = mapped.get("shipment_date", "")
        shipment_date = _parse_date(raw_date)
        if not shipment_date:
            skipped.append(f"Row {i} ({patient_id}): unparseable date '{raw_date}'")
            continue

        raw_qty = mapped.get("quantity", "1")
        try:
            quantity = max(1, int(float(raw_qty)))
        except (ValueError, TypeError):
            quantity = 1

        # Raw payer string passes through untouched. coverage_calculator._normalize_payer
        # is the single normalization point; the raw value survives for display.
        payer = mapped.get("payer", "").strip()
        component = (mapped.get("component", "sensor") or "sensor").lower().strip()
        if component not in ("sensor", "transmitter", "pod"):
            component = "sensor"

        # Optional doc fields — only populated if the CSV contains these columns
        csv_visit_date: Optional[date] = None
        raw_visit = mapped.get("csv_visit_date", "")
        if raw_visit:
            csv_visit_date = _parse_date(raw_visit)  # None if unparseable (non-error)

        csv_swo_status = mapped.get("csv_swo_status") or None
        csv_pecos_verified = mapped.get("csv_pecos_verified") or None
        csv_pa_status = mapped.get("csv_pa_status") or None
        csv_diagnosis_on_file = mapped.get("csv_diagnosis_on_file") or None
        transfer_raw = mapped.get("csv_transfer_from", "").strip()
        csv_transfer_from = transfer_raw if transfer_raw else None
        order_number = mapped.get("order_number") or None
        hcpcs = mapped.get("hcpcs") or None

        records.append(ShipmentRecord(
            patient_id=patient_id,
            device_type=device_type,
            shipment_date=shipment_date,
            quantity=quantity,
            payer=payer,
            component=component,
            csv_visit_date=csv_visit_date,
            csv_swo_status=csv_swo_status,
            csv_pecos_verified=csv_pecos_verified,
            csv_pa_status=csv_pa_status,
            csv_diagnosis_on_file=csv_diagnosis_on_file,
            csv_transfer_from=csv_transfer_from,
            order_number=order_number,
            hcpcs=hcpcs,
        ))

    return records, skipped, mapping_summary