from pydantic import BaseModel, conlist, validator
from typing import Optional

TYPICAL_MEASURE_TYPES = [
    "wall_insulation",
    "roof_insulation",
    "ventilation",
    "floor_insulation",
    "windows",
    "fireplace",
    "heating",
    "hot_water",
    "low_energy_lighting",
    "secondary_heating",
    "solar_pv"
]

SPECIFIC_MEASURES = [
    # Specific measures
    # Walls
    "internal_wall_insulation",
    "external_wall_insulation",
    "cavity_wall_insulation",
    # Roof
    "loft_insulation",
    "flat_roof_insulation",
    "room_roof_insulation",
    # Floor
    "suspended_floor_insulation",
    "solid_floor_insulation",
    # Heating
    "boiler_upgrade",
    "high_heat_retention_storage_heater",
    "air_source_heat_pump",
    "secondary_heating",
    # Solar
    "solar_pv",
    # Windows Glazing
    "double_glazing",
    "secondary_glazing",
    # Mechanical ventilation
    "ventilation",
    # Other
    "low_energy_lighting",
    "fireplace",
    "hot_water",
]

NON_INVASIVE_SPECIFIC_MEASURES = [
    # Specific measures that will typically come from an energy assessment
    "trickle_vents",
    "draught_proofing",
    "mixed_glazing",  # This covers partial double glazing and secondary glazing
    "cavity_extract_and_refill",
]

# This allows us to extend high level categories for measures such as "wall_insulation" to the specific measures
# such as "external_wall_insulation", "internal_wall_insulation", "cavity_wall_insulation"
MEASURE_MAP = {
    "wall_insulation": [
        "internal_wall_insulation", "external_wall_insulation", "cavity_wall_insulation",
    ],
    "roof_insulation": ["loft_insulation", "flat_roof_insulation", "room_roof_insulation"],
    "floor_insulation": ["suspended_floor_insulation", "solid_floor_insulation"],
    "heating": ["boiler_upgrade", "high_heat_retention_storage_heater", "air_source_heat_pump"],
    "windows": ["double_glazing", "secondary_glazing"],
    "heating_controls": ["roomstat_programmer_trvs", "time_temperature_zone_control"]
}


class PlanTriggerRequest(BaseModel):
    budget: Optional[float] = None
    goal: str
    housing_type: str
    goal_value: str
    portfolio_id: int
    trigger_file_path: str
    already_installed_file_path: Optional[str] = None
    patches_file_path: Optional[str] = None
    non_invasive_recommendations_file_path: Optional[str] = None
    exclusions: Optional[conlist(str, min_items=1)] = None
    inclusions: Optional[conlist(str, min_items=1)] = None

    scenario_name: Optional[str] = ""
    # If true, will allow us to create multiple plans for the same portfolio, whereas if this is false, if this property
    # exists in the portfolio, it will be ignored
    multi_plan: Optional[bool] = False

    # if False, allows optimisation to be switched off
    optimise: Optional[bool] = True

    _allowed_goals = {"Increasing EPC"}

    _allowed_housing_types = {"Social", "Private"}

    # Validator to ensure exclusions are within the pre-defined possibilities
    @validator('exclusions', each_item=True)
    def check_exclusions(cls, v):
        if v not in TYPICAL_MEASURE_TYPES + SPECIFIC_MEASURES + NON_INVASIVE_SPECIFIC_MEASURES:
            raise ValueError(f"{v} is not an allowed exclusion")
        return v

    @validator('inclusions', each_item=True)
    def check_inclusions(cls, v):
        if v not in TYPICAL_MEASURE_TYPES + SPECIFIC_MEASURES + NON_INVASIVE_SPECIFIC_MEASURES:
            raise ValueError(f"{v} is not an allowed inclusion")
        return v

    # Validator to ensure that the goal is within the pre-defined possibilities
    @validator('goal')
    def check_goal(cls, v):
        if v not in cls._allowed_goals:
            raise ValueError(f"{v} is not a valid goal")
        return v

    # Validator to ensure that the housing type is within the pre-defined possibilities
    @validator('housing_type')
    def check_housing_type(cls, v):
        if v not in cls._allowed_housing_types:
            raise ValueError(f"{v} is not a valid housing type")
        return v


class MdsRequest(PlanTriggerRequest):
    # When creating the mds report, we allow an optional list of measures to select from. If this is passed, it will
    # cause the service to select the optimal package from the list of measures
    measures: Optional[conlist(str, min_items=1)] = None