"""
*******************************************************
 * Copyright (C) 2017 MindsDB Inc. <copyright@mindsdb.com>
 *
 * This file is part of MindsDB Server.
 *
 * MindsDB Server can not be copied and/or distributed without the express
 * permission of MindsDB Inc
 *******************************************************
"""

import re
import copy
import dfsql
import pandas as pd
import datetime

from lightwood.api import dtype
from mindsdb_sql import parse_sql
from mindsdb_sql.planner import plan_query
from mindsdb_sql.parser.dialects.mindsdb.latest import Latest
from mindsdb_sql.parser.ast import (
    BinaryOperation,
    UnaryOperation,
    Identifier,
    Operation,
    Constant,
    OrderBy,
    Select,
    Union,
    Join,
    Star
)
from mindsdb_sql.planner.steps import (
    ApplyPredictorRowStep,
    FetchDataframeStep,
    ApplyPredictorStep,
    MapReduceStep,
    MultipleSteps,
    ProjectStep,
    FilterStep,
    UnionStep,
    JoinStep
)

from mindsdb.api.mysql.mysql_proxy.classes.com_operators import operator_map
from mindsdb.api.mysql.mysql_proxy.libs.constants.mysql import TYPES, ERR
from mindsdb.api.mysql.mysql_proxy.utilities import log
from mindsdb.interfaces.ai_table.ai_table import AITableStore
import mindsdb.interfaces.storage.db as db


superset_subquery = re.compile(r'from[\s\n]*(\(.*\))[\s\n]*as[\s\n]*virtual_table', flags=re.IGNORECASE | re.MULTILINE | re.S)


class NotImplementedError(Exception):
    pass


class SqlError(Exception):
    pass


def get_preditor_alias(step, mindsdb_database):
    predictor_name = '.'.join(step.predictor.parts)
    predictor_alias = '.'.join(step.predictor.alias.parts) if step.predictor.alias is not None else predictor_name
    return (mindsdb_database, predictor_name, predictor_alias)


def get_table_alias(table_obj, default_db_name):
    # (database, table, alias)
    if len(table_obj.parts) > 2:
        raise Exception(f'Table name must contain no more than 2 parts. Got name: {table_obj.parts}')
    elif len(table_obj.parts) == 1:
        name = (default_db_name, table_obj.parts[0])
    else:
        name = tuple(table_obj.parts)
    if table_obj.alias is not None:
        name = name + ('.'.join(table_obj.alias.parts),)
    else:
        name = name + (None,)
    return name


def get_all_tables(stmt):
    if isinstance(stmt, Union):
        left = get_all_tables(stmt.left)
        right = get_all_tables(stmt.right)
        return left + right

    if isinstance(stmt, Select):
        from_stmt = stmt.from_table
    elif isinstance(stmt, (Identifier, Join)):
        from_stmt = stmt
    else:
        raise Exception(f'Unknown type of identifier: {stmt}')

    result = []
    if isinstance(from_stmt, Identifier):
        result.append(from_stmt.parts[-1])
    elif isinstance(from_stmt, Join):
        result.extend(get_all_tables(from_stmt.left))
        result.extend(get_all_tables(from_stmt.right))
    return result


def markQueryVar(where):
    if isinstance(where, BinaryOperation):
        markQueryVar(where.args[0])
        markQueryVar(where.args[1])
    elif isinstance(where, UnaryOperation):
        markQueryVar(where.args[0])
    elif isinstance(where, Constant):
        if where.value == '$var':
            where.is_var = True


def replaceQueryVar(where, val):
    if isinstance(where, BinaryOperation):
        replaceQueryVar(where.args[0], val)
        replaceQueryVar(where.args[1], val)
    elif isinstance(where, UnaryOperation):
        replaceQueryVar(where.args[0], val)
    elif isinstance(where, Constant):
        if hasattr(where, 'is_var') and where.is_var is True:
            where.value = val


class SQLQuery():
    def __init__(self, sql, session):
        self.session = session
        self.integration = session.integration
        self.database = None if session.database == '' else session.database.lower()
        self.datahub = session.datahub
        self.ai_table = None
        self.outer_query = None
        self.row_id = 0

        self.mindsdb_database_name = 'mindsdb'

        # +++ workaround for subqueries in superset
        if 'as virtual_table' in sql.lower():
            subquery = re.findall(superset_subquery, sql)
            if isinstance(subquery, list) and len(subquery) == 1:
                subquery = subquery[0]
                self.outer_query = sql.replace(subquery, 'dataframe')
                sql = subquery.strip('()')
        # ---

        # 'offset x, y' - specific just for mysql, parser dont understand it
        sql = re.sub(r'\n?limit([\n\d\s]*),([\n\d\s]*)', ' limit \g<2> offset \g<1> ', sql, flags=re.IGNORECASE)

        self.raw = sql
        self.model_types = {}
        self._parse_query(sql)

    def fetch(self, datahub, view='list'):
        data = self.fetched_data

        if view == 'list':
            self.result = self._make_list_result_view(data)
        elif view == 'dict':
            self.result = self._make_dict_result_view(data)
        else:
            raise Exception('Only "list" and "dict" views supported atm')

        return {
            'success': True,
            'result': self.result
        }

    def _fetch_dataframe_step(self, step):
        dn = self.datahub.get(step.integration)
        query = step.query

        table_alias = get_table_alias(step.query.from_table, self.database)
        # TODO for information_schema we have 'database' = 'mindsdb'

        data, column_names = dn.select(
            query=query
        )

        self.columns_list = [
            table_alias + (column_name, column_name)
            for column_name in column_names
        ]

        for i, row in enumerate(data):
            row['__mindsdb_row_id'] = self.row_id + i
        self.row_id = self.row_id + len(data)

        data = [{table_alias: x} for x in data]
        return data

    def _multiple_steps(self, step):
        data = []
        for substep in step.steps:
            data.append(self._fetch_dataframe_step(substep))
        return data

    def _multiple_steps_reduce(self, step, values):
        if step.reduce != 'union':
            raise Exception(f'Unknown MultipleSteps type: {step.reduce}')

        result = []

        for substep in step.steps:
            if isinstance(substep, FetchDataframeStep) is False:
                raise Exception(f'Wrong step type for MultipleSteps: {step}')
            markQueryVar(substep.query.where)

        for v in values:
            for substep in step.steps:
                replaceQueryVar(substep.query.where, v)
            data = self._multiple_steps(step)
            for part in data:
                result.extend(part)

        return result

    def _parse_query(self, sql):
        # +++ FIXME https://github.com/mindsdb/mindsdb_sql/issues/53
        is_crutch = False
        if 'where 1 = 0' in sql.lower():
            sql = sql[:sql.lower().find('where 1 = 0')] + ' limit 0'
            is_crutch = True
        elif 'where 1=0' in sql.lower():
            sql = sql[:sql.lower().find('where 1=0')] + ' limit 0'
            is_crutch = True
        # ---

        # +++ https://github.com/mindsdb/mindsdb_sql/issues/64
        str_sql = sql.replace(' status ', ' `status` ')
        # ---
        mindsdb_sql_struct = parse_sql(str_sql, dialect='mindsdb')

        # is it query with only constants?
        if (
            mindsdb_sql_struct.from_table is None
            and mindsdb_sql_struct.where is None
            and set(isinstance(x, Constant) for x in mindsdb_sql_struct.targets) == set([True])
        ):
            table_name = (None, None, None)
            self.fetched_data = [{table_name: {}}]
            self.columns_list = []
            for column in mindsdb_sql_struct.targets:
                alias = '.'.join(column.alias.parts) if column.alias is not None else column.value
                self.fetched_data[0][table_name][alias] = column.value
                self.columns_list.append(table_name + (alias, alias))
            return

        # is it query to 'predictors'?
        if (
            isinstance(mindsdb_sql_struct.from_table, Identifier)
            and mindsdb_sql_struct.from_table.parts[-1].lower() == 'predictors'
            and (
                self.database == 'mindsdb'
                or mindsdb_sql_struct.from_table.parts[0].lower() == 'mindsdb'
            )
        ):
            dn = self.datahub.get(self.mindsdb_database_name)
            data, columns = dn.get_predictors(mindsdb_sql_struct)
            table_name = ('mindsdb', 'predictors', 'predictors')
            self.fetched_data = [
                {table_name: row}
                for row in data
            ]
            self.columns_list = [
                (table_name + (column_name, column_name))
                for column_name in columns
            ]
            return

        # is it query to 'commands'?
        if (
            isinstance(mindsdb_sql_struct.from_table, Identifier)
            and mindsdb_sql_struct.from_table.parts[-1].lower() == 'commands'
            and (
                self.database == 'mindsdb'
                or mindsdb_sql_struct.from_table.parts[0].lower() == 'mindsdb'
            )
        ):
            self.fetched_data = []
            self.columns_list = [('mindsdb', 'commands', 'commands', 'command', 'command')]
            return

        integrations_names = self.datahub.get_integrations_names()
        integrations_names.append('INFORMATION_SCHEMA')
        integrations_names.append('information_schema')
        integrations_names.append('datasource')

        all_tables = get_all_tables(mindsdb_sql_struct)

        predictor_metadata = {}
        predictors = db.session.query(db.Predictor).filter_by(company_id=self.session.company_id)
        for model_name in set(all_tables):
            for p in predictors:
                if p.name == model_name:
                    if isinstance(p.data, dict) and 'error' not in p.data:
                        ts_settings = p.learn_args.get('timeseries_settings', {})
                        if ts_settings.get('is_timeseries') is True:
                            window = ts_settings.get('window')
                            order_by = ts_settings.get('order_by')[0]
                            group_by = ts_settings.get('group_by')[0]
                            predictor_metadata[model_name] = {
                                'timeseries': True,
                                'window': window,
                                'order_by_column': order_by,
                                'group_by_column': group_by
                            }
                        else:
                            predictor_metadata[model_name] = {
                                'timeseries': False
                            }
                        self.model_types.update(p.data.get('dtypes', {}))

        # FIXME https://github.com/mindsdb/mindsdb_sql/issues/53
        if is_crutch is True:
            sql = sql[:sql.lower().find(' limit 0')] + " where when_data = '{}' limit 0"
            mindsdb_sql_struct = parse_sql(sql, dialect='mindsdb')

        plan = plan_query(
            mindsdb_sql_struct,
            integrations=integrations_names,
            predictor_namespace=self.mindsdb_database_name,
            predictor_metadata=predictor_metadata,
            default_namespace=self.database
        )
        steps_data = []

        for step in plan.steps:
            data = []
            if isinstance(step, FetchDataframeStep):
                data = self._fetch_dataframe_step(step)
            elif isinstance(step, UnionStep):
                left_data = steps_data[step.left.step_num]
                right_data = steps_data[step.right.step_num]
                data = left_data + right_data
            elif isinstance(step, MapReduceStep):
                if step.reduce != 'union':
                    raise Exception(f'Unknown MapReduceStep type: {step.reduce}')

                step_data = steps_data[step.values.step_num]
                values = []
                for row in step_data:
                    for row_data in row.values():
                        for name, value in row_data.items():
                            if name != '__mindsdb_row_id':
                                values.append(value)

                data = []
                substep = step.step
                if isinstance(substep, FetchDataframeStep):
                    query = substep.query
                    markQueryVar(query.where)
                    for value in values:
                        replaceQueryVar(query.where, value)
                        data.extend(self._fetch_dataframe_step(substep))
                elif isinstance(substep, MultipleSteps):
                    data = self._multiple_steps_reduce(substep, values)
                else:
                    raise Exception(f'Unknown step type: {step.step}')
            elif isinstance(step, ApplyPredictorRowStep):
                predictor = '.'.join(step.predictor.parts)
                dn = self.datahub.get(self.mindsdb_database_name)
                where_data = step.row_dict

                # +++ external datasource
                if 'external_datasource' in where_data:
                    external_datasource_sql = where_data['external_datasource']
                    if 'select ' not in external_datasource_sql.lower():
                        external_datasource_sql = f'select * from {external_datasource_sql}'
                    temp_session = copy.copy(self.session)
                    temp_session.database = 'datasource'
                    query = SQLQuery(external_datasource_sql, session=temp_session)
                    result = query.fetch(self.datahub, view='dict')
                    if result['success'] is False:
                        raise Exception(f"Something wrong with getting data from {where_data['external_datasource']}")
                    for row in result['result']:
                        row.update(where_data)
                    where_data = result['result']
                # ---

                data = dn.select(
                    table=predictor,
                    columns=None,
                    where_data=where_data,
                    integration_name=self.session.integration,
                    integration_type=self.session.integration_type,
                    # where={}
                )
                data = [{get_preditor_alias(step, self.database): x} for x in data]
            elif isinstance(step, ApplyPredictorStep):
                dn = self.datahub.get(self.mindsdb_database_name)
                predictor = '.'.join(step.predictor.parts)
                where_data = []
                for row in steps_data[step.dataframe.step_num]:
                    new_row = {}
                    for table_name in row:
                        keys_intersection = set(new_row) & set(row[table_name])
                        if len(keys_intersection) > 0:
                            raise Exception(
                                f'The predictor got two identical keys from different datasources: {keys_intersection}'
                            )
                        new_row.update(row[table_name])
                    where_data.append(new_row)

                is_timeseries = predictor_metadata[predictor]['timeseries']
                _mdb_make_predictions = None
                if is_timeseries:
                    if 'LATEST' in self.raw:
                        _mdb_make_predictions = False
                    else:
                        _mdb_make_predictions = True
                    for row in where_data:
                        if '__mdb_make_predictions' not in row:
                            row['__mdb_make_predictions'] = _mdb_make_predictions

                for row in where_data:
                    for key in row:
                        if isinstance(row[key], datetime.date):
                            row[key] = str(row[key])

                data = dn.select(
                    table=predictor,
                    columns=None,
                    where_data=where_data,
                    integration_name=self.session.integration,
                    integration_type=self.session.integration_type
                )
                data = [{get_preditor_alias(step, self.database): x} for x in data]
            elif isinstance(step, JoinStep):
                left_data = steps_data[step.left.step_num]
                right_data = steps_data[step.right.step_num]

                if len(left_data) == 0 and len(right_data) == 0:
                    return []
                if len(left_data) == 0:
                    return right_data
                if len(right_data) == 0:
                    return left_data

                left_keys = left_data[0].keys()
                right_keys = right_data[0].keys()

                if len(left_keys) != 1 or len(right_keys) != 1:
                    raise Exception('At this moment supported only one JOIN supported')
                if step.query.condition is not None:
                    raise Exception('At this moment supported only JOIN without conditions')

                left_key = list(left_keys)[0]
                right_key = list(right_keys)[0]

                data = []
                if step.query.join_type.upper() == 'LEFT JOIN':
                    for left_row in left_data:
                        left_row_data = left_row[left_key]
                        for right_row in right_data:
                            right_row_data = right_row[right_key]
                            if left_row_data['__mindsdb_row_id'] == right_row_data['__mindsdb_row_id']:
                                data.append({
                                    left_key: left_row_data,
                                    right_key: right_row_data
                                })
                                break
                        else:
                            data.append({
                                left_key: left_row_data,
                                right_key: {}
                            })
                elif step.query.join_type.upper() == 'JOIN':
                    right_used_ids = []
                    for left_row in left_data:
                        left_row_data = left_row[left_key]
                        for right_row in right_data:
                            right_row_data = right_row[right_key]
                            if left_row_data['__mindsdb_row_id'] == right_row_data['__mindsdb_row_id']:
                                data.append({
                                    left_key: left_row_data,
                                    right_key: right_row_data
                                })
                                right_used_ids.append(right_row_data['__mindsdb_row_id'])
                                break
                    for right_row in right_data:
                        right_row_data = right_row[right_key]
                        if right_row_data['__mindsdb_row_id'] not in right_used_ids:
                            data.append({
                                left_key: {},
                                right_key: right_row_data
                            })
                else:
                    raise Exception(f'Unknown JOIN type: {step.query.join_type}')
            elif isinstance(step, FilterStep):
                raise Exception('FilterStep not implemented')
            elif isinstance(step, ProjectStep):
                step_data = steps_data[step.dataframe.step_num]
                row = step_data[0]  # TODO if rowcount = 0
                tables_columns = {}
                for table_name in row:
                    tables_columns[table_name] = list(row[table_name].keys())

                columns_list = []
                for column_full_name in step.columns:
                    table_name = None
                    if isinstance(column_full_name, Star) is False:
                        column_name_parts = column_full_name.parts
                        column_alias = None if column_full_name.alias is None else '.'.join(column_full_name.alias.parts)
                        if len(column_name_parts) > 2:
                            raise Exception(f'Column name must contain no more than 2 parts. Got name: {".".join(column_full_name)}')
                        elif len(column_name_parts) == 1:
                            column_name = column_name_parts[0]

                            appropriate_table = None
                            for table_name, table_columns in tables_columns.items():
                                if column_name in table_columns:
                                    if appropriate_table is not None:
                                        raise Exception('Fount multiple appropriate tables for column {column_name}')
                                    else:
                                        appropriate_table = table_name
                            if appropriate_table is None:
                                # it is probably constaint
                                column_name = column_name.strip("'")
                                name_or_alias = column_alias or column_name
                                column_alias = name_or_alias
                                # appropriate_table = ''
                                for row in step_data:
                                    for table in row:
                                        row[table][name_or_alias] = column_name
                                appropriate_table = list(step_data[0].keys())[0]
                                # raise Exception(f'Can not find approproate table for column {column_name}')
                                columns_list.append(appropriate_table + (column_alias, column_alias))
                            else:
                                columns_list.append(appropriate_table + (column_name, column_alias))
                        elif len(column_name_parts) == 2:
                            table_name_or_alias = column_name_parts[0]
                            column_name = column_name_parts[1]

                            appropriate_table = None
                            for table_name, table_columns in tables_columns.items():
                                checkig_table_name_or_alias = table_name[2] or table_name[1]
                                if table_name_or_alias == checkig_table_name_or_alias:
                                    if column_name not in table_columns:
                                        raise Exception(f'Can not find column "{column_name}" in table "{table_name}"')
                                    appropriate_table = table_name
                                    break
                            if appropriate_table is None:
                                raise Exception(f'Can not find approproate table for column {column_name}')
                            columns_list.append(appropriate_table + (column_name, column_alias))
                        else:
                            raise Exception('Undefined column name')
                    else:
                        for table_name, table_columns in tables_columns.items():
                            for column_name in table_columns:
                                columns_list.append(table_name + (column_name, None))

                self.columns_list = columns_list
                data = step_data
            steps_data.append(data)

        if self.outer_query is not None:
            data = []
            # +++
            result = []
            for row in steps_data[-1]:
                data_row = {}
                for column_record in self.columns_list:
                    table_name = column_record[:3]
                    column_name = column_record[3]
                    data_row[column_record[4] or column_record[3]] = row[table_name][column_name]
                result.append(data_row)
            # ---
            data = self._make_list_result_view(result)
            df = pd.DataFrame(data)
            result = dfsql.sql_query(
                self.outer_query,
                ds_kwargs={'case_sensitive': False},
                reduce_output=False,
                dataframe=df
            )

            try:
                self.columns_list = [
                    ('', '', '', x, x) for x in result.columns
                ]
            except Exception:
                self.columns_list = [
                    ('', '', '', result.name, result.name)
                ]

            # +++ make list result view
            new_result = []
            for row in result.to_dict(orient='records'):
                data_row = []
                for column_record in self.columns_list:
                    column_name = column_record[4] or column_record[3]
                    data_row.append(row.get(column_name))
                new_result.append(data_row)
            result = new_result
            # ---

            self.fetched_data = result
        else:
            self.fetched_data = steps_data[-1]

        if hasattr(self, 'columns_list') is False:
            self.columns_list = []
            for row in self.fetched_data:
                for table_key in row:
                    for column_name in row[table_key]:
                        if (table_key + (column_name, column_name)) not in self.columns_list:
                            self.columns_list.append((table_key + (column_name, column_name)))

        self.columns_list = [x for x in self.columns_list if x[3] != '__mindsdb_row_id']

    def _apply_where_filter(self, row, where):
        if isinstance(where, Identifier):
            return row[where.value]
        elif isinstance(where, Constant):
            return where.value
        elif not isinstance(where, (UnaryOperation, BinaryOperation)):
            Exception(f'Unknown operation type: {where}')

        op_fn = operator_map.get(where.op)
        if op_fn is None:
            raise Exception(f'unknown operator {where.op}')

        args = [self._apply_where_filter(row, arg) for arg in where.args]
        result = op_fn(*args)
        return result

    def _make_list_result_view(self, data):
        if self.outer_query is not None:
            return data
        result = []
        for row in data:
            data_row = []
            for column_record in self.columns_list:
                table_name = column_record[:3]
                column_name = column_record[3]
                data_row.append(row[table_name][column_name])
            result.append(data_row)
        return result

    def _make_dict_result_view(self, data):
        result = []
        for row in data:
            data_row = {}
            for table_name in row:
                data_row.update(row[table_name])
            result.append(data_row)
        return result

    @property
    def columns(self):
        result = []
        for column_record in self.columns_list:
            try:
                field_type = self.model_types.get(column_record[3])
            except Exception:
                field_type = None

            column_type = TYPES.MYSQL_TYPE_VAR_STRING
            if field_type == dtype.date:
                column_type = TYPES.MYSQL_TYPE_DATE
            elif field_type == dtype.datetime:
                column_type = TYPES.MYSQL_TYPE_DATETIME

            result.append({
                'database': column_record[0] or self.database,
                #  TODO add 'original_table'
                'table_name': column_record[1],
                'name': column_record[3],
                'alias': column_record[4] or column_record[3],
                # NOTE all work with text-type, but if/when wanted change types to real,
                # it will need to check all types casts in BinaryResultsetRowPacket
                'type': column_type
            })
        return result
