from typing import Any
import logging

from sqlmodel import Field, SQLModel, MetaData, JSON, TEXT, Relationship, Column
from pydantic import computed_field
import numpy as np
import pandas as pd
import geopandas as gpd
import shapely
from sqlalchemy.sql import sqltypes
from geoalchemy2.types import Geometry

pandas_cast_map = {
    sqltypes.Integer: 'Int64',
    sqltypes.Float: 'float64',
}

logger = logging.getLogger('model_base_base')

class Model(SQLModel):
    """
    Base mixin class for models that can be converted to a Pandas dataframe with get_df
    """

    class Meta:
        filtered_columns_on_map: list[str] = []

    @classmethod
    def get_store_name(cls):
        return "{}.{}".format(cls.__table_args__['schema'], cls.__tablename__)

    @classmethod
    def get_table_name_prefix(cls):
        return "{}_{}".format(cls.__table_args__['schema'], cls.__tablename__)

    @classmethod
    async def get_df(cls, where=None,
                     with_related=None, recursive=True,
                     cast=True,
                     with_only_columns=None,
                     geom_as_ewkt=False,
                     **kwargs):
        """
        Return a Pandas dataframe of all records
        Optional arguments:
        * an SQLAlchemy where clause
        * with_related: automatically get data from related columns, following the foreign keys in the model definitions
        * cast: automatically transform various data in their best python types (eg. with date, time...)
        * with_only_columns: fetch only these columns (list of column names)
        * geom_as_ewkt: convert geometry columns to EWKB (handy for eg. using upsert_df)
        :return:
        """
        query = cls.query

        if with_related is not False:
            if with_related or getattr(cls, 'get_gdf_with_related', False):
                joins = get_join_with(cls, recursive)
                model_loader = cls.load(**joins)
                query = _get_query_with_table_names(model_loader)

        if where is not None:
            query.append_whereclause(where)

        if with_only_columns:
            query = query.with_only_columns([getattr(cls, colname) for colname in with_only_columns])

        ## Got idea from https://github.com/MagicStack/asyncpg/issues/173.
        async with query.bind.raw_pool.acquire() as conn:
            ## Convert hstore fields to dict
            await conn.set_builtin_type_codec('hstore', codec_name='pg_contrib.hstore')

            compiled = query.compile()
            stmt = await conn.prepare(compiled.string)
            columns = [a.name for a in stmt.get_attributes()]
            data = await stmt.fetch(*[compiled.params.get(param) for param in compiled.positiontup])
            df = pd.DataFrame(data, columns=columns)

            ## Convert primary key columns to Int64:
            ## allows NaN, fixing type convertion to float with merge
            for pk in [c.name for c in cls.__table__.primary_key.columns]:
                if pk in df.columns and df[pk].dtype=='int64':
                    df[pk] = df[pk].astype('Int64')

            if cast:
                ## Cast the type for known types (datetime, ...)
                for column_name in df.columns:
                    col = getattr(query.columns, column_name, None)
                    if col is None:
                        logger.debug(f'Cannot get column {column_name} in query for model {cls.__name__}')
                        continue
                    column_type = getattr(query.columns, column_name).type
                    ## XXX: Needs refinment, eg. nullable -> Int64 ...
                    if column_type.__class__ in pandas_cast_map:
                        df[column_name] = df[column_name].astype(pandas_cast_map[column_type.__class__])
                    elif isinstance(column_type, (sqltypes.Date, sqltypes.DateTime)):
                        ## Dates, times
                        df[column_name] = pd.to_datetime(df[column_name])
                    #elif isinstance(column_type, (sqltypes.Integer, sqltypes.Float)):
                    #    ## Numeric
                    #    df[column_name] = pd.to_numeric(df[column_name], errors='coerce')
                    ## XXX: keeping this note about that is about "char" SQL type, but the fix of #9694 makes it unnessary
                    #elif isinstance(column_type, sqltypes.CHAR) or (isinstance(column_type, sqltypes.String) and column_type.length == 1):
                    #    ## Workaround for bytes being used for string of length 1 (not sure - why???)
                    #    df[column_name] = df[column_name].str.decode('utf-8')

            ## Rename the columns, removing the schema_table prefix for the columns in that model
            prefix = cls.get_table_name_prefix()
            prefix_length = len(prefix) + 1
            rename_map = {colname: colname[prefix_length:] for colname in df.columns if colname.startswith(prefix)}
            df.rename(columns=rename_map, inplace=True)

            ## Eventually convert geometry columns to EWKB
            if geom_as_ewkt:
                geometry_columns = [col.name for col in cls.__table__.columns if isinstance(col.type, Geometry)]
                for column in geometry_columns:
                    df[column] = shapely.to_wkb(shapely.from_wkb(df.geom), hex=True, include_srid=True)

            return df