"""IO contracts for the market indicator endpoints (Issue #99).

POST /api/v1/market/ddu-indicator — mirrors the ARN/НСПД request body so the
frontend can reuse one UI pattern for primary (наша ДДУ) vs secondary (ARN).
Field names are camelCase (ARN-compatible) via validation aliases; snake_case
also accepted (populate_by_name).
"""

from __future__ import annotations

from typing import Any

from pydantic import BaseModel, ConfigDict, Field


class DduIndicatorRequest(BaseModel):
    """ARN-shaped request body. Unsupported options are reported in `notes`
    of the response rather than rejected (MVP supports Q / methods 1,2 / subj 66).
    """

    model_config = ConfigDict(populate_by_name=True)

    # ARN indicator types M/Q/H/Y. MVP supports only 'Q'.
    indicators: list[str] | None = Field(default=None)
    # 1 = basis, 2 = previous, (3 = year-ago — unsupported in MVP).
    calculation_method: int = Field(default=2, ge=1, le=3, alias="calculationMethod")
    # ARN-style period bounds, e.g. '2025-Q2' .. '2026-Q1'. None = unbounded.
    period_from: str | None = Field(default=None, alias="periodFrom", max_length=10)
    period_to: str | None = Field(default=None, alias="periodTo", max_length=10)
    # ARN federal district ids (e.g. [6] = Уральский). Informational in MVP.
    federal_district: list[int] | None = Field(default=None, alias="federalDistrict")
    # ARN subject codes. Only '66' (Свердловская обл.) is backed by data.
    federal_subject: list[str] | None = Field(default=None, alias="federalSubject")
    # ARN cadastral quarters. NOT supported (primary-market ДДУ too sparse per
    # cad_quarter) — accepted for shape-compat, reported as unsupported.
    cadastral_quarter: list[str] | None = Field(default=None, alias="cadastralQuarter")
    # ARN area-range ids 1..6 (0 = all-area headline). None → all present.
    area_ranges: list[int] | None = Field(default=None, alias="areaRanges")


class DduIndicatorResponse(BaseModel):
    """Free-form ARN-mirror response (table + graph + meta + notes)."""

    meta: dict[str, Any]
    table: list[dict[str, Any]]
    graph: list[dict[str, Any]]
    notes: list[str]