<reponame>psbsgic/rabbitai
""" Rabbitai wrapper around pyarrow.Table."""

import datetime
import json
import logging
from typing import Any, Dict, List, Optional, Tuple, Type

import numpy as np
import pandas as pd
import pyarrow as pa

from rabbitai import db_engine_specs
from rabbitai.typing import DbapiDescription, DbapiResult
from rabbitai.utils import core as utils

logger = logging.getLogger(__name__)


def dedup(l: List[str], suffix: str = "__", case_sensitive: bool = True) -> List[str]:
    """De-duplicates a list of string by suffixing a counter

    Always returns the same number of entries as provided, and always returns
    unique values. Case sensitive comparison by default.

    >>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
    foo,bar,bar__1,bar__2,Bar
    >>> print(
        ','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False))
    )
    foo,bar,bar__1,bar__2,Bar__3
    """
    new_l: List[str] = []
    seen: Dict[str, int] = {}
    for item in l:
        s_fixed_case = item if case_sensitive else item.lower()
        if s_fixed_case in seen:
            seen[s_fixed_case] += 1
            item += suffix + str(seen[s_fixed_case])
        else:
            seen[s_fixed_case] = 0
        new_l.append(item)
    return new_l


def stringify(obj: Any) -> str:
    return json.dumps(obj, default=utils.json_iso_dttm_ser)


def stringify_values(array: np.ndarray) -> np.ndarray:
    vstringify = np.vectorize(stringify)
    return vstringify(array)


def destringify(obj: str) -> Any:
    return json.loads(obj)


class RabbitaiResultSet:
    def __init__(
        self,
        data: DbapiResult,
        cursor_description: DbapiDescription,
        db_engine_spec: Type[db_engine_specs.BaseEngineSpec],
    ):
        self.db_engine_spec = db_engine_spec
        data = data or []
        column_names: List[str] = []
        pa_data: List[pa.Array] = []
        deduped_cursor_desc: List[Tuple[Any, ...]] = []
        numpy_dtype: List[Tuple[str, ...]] = []
        stringified_arr: np.ndarray

        if cursor_description:
            # get deduped list of column names
            column_names = dedup([col[0] for col in cursor_description])

            # fix cursor descriptor with the deduped names
            deduped_cursor_desc = [
                tuple([column_name, *list(description)[1:]])
                for column_name, description in zip(column_names, cursor_description)
            ]

            # generate numpy structured array dtype
            numpy_dtype = [(column_name, "object") for column_name in column_names]

        # only do expensive recasting if datatype is not standard list of tuples
        if data and (not isinstance(data, list) or not isinstance(data[0], tuple)):
            data = [tuple(row) for row in data]
        array = np.array(data, dtype=numpy_dtype)
        if array.size > 0:
            for column in column_names:
                try:
                    pa_data.append(pa.array(array[column].tolist()))
                except (
                    pa.lib.ArrowInvalid,
                    pa.lib.ArrowTypeError,
                    pa.lib.ArrowNotImplementedError,
                    TypeError,  # this is super hackey,
                    # https://issues.apache.org/jira/browse/ARROW-7855
                ):
                    # attempt serialization of values as strings
                    stringified_arr = stringify_values(array[column])
                    pa_data.append(pa.array(stringified_arr.tolist()))

        if pa_data:  # pylint: disable=too-many-nested-blocks
            for i, column in enumerate(column_names):
                if pa.types.is_nested(pa_data[i].type):
                    # TODO: revisit nested column serialization once nested types
                    #  are added as a natively supported column type in Rabbitai
                    #  (rabbitai.utils.core.GenericDataType).
                    stringified_arr = stringify_values(array[column])
                    pa_data[i] = pa.array(stringified_arr.tolist())

                elif pa.types.is_temporal(pa_data[i].type):
                    # workaround for bug converting
                    # `psycopg2.tz.FixedOffsetTimezone` tzinfo values.
                    # related: https://issues.apache.org/jira/browse/ARROW-5248
                    sample = self.first_nonempty(array[column])
                    if sample and isinstance(sample, datetime.datetime):
                        try:
                            if sample.tzinfo:
                                tz = sample.tzinfo
                                series = pd.Series(
                                    array[column], dtype="datetime64[ns]"
                                )
                                series = pd.to_datetime(series).dt.tz_localize(tz)
                                pa_data[i] = pa.Array.from_pandas(
                                    series, type=pa.timestamp("ns", tz=tz)
                                )
                        except Exception as ex:  # pylint: disable=broad-except
                            logger.exception(ex)

        self.table = pa.Table.from_arrays(pa_data, names=column_names)
        self._type_dict: Dict[str, Any] = {}
        try:
            # The driver may not be passing a cursor.description
            self._type_dict = {
                col: db_engine_spec.get_datatype(deduped_cursor_desc[i][1])
                for i, col in enumerate(column_names)
                if deduped_cursor_desc
            }
        except Exception as ex:
            logger.exception(ex)

    @staticmethod
    def convert_pa_dtype(pa_dtype: pa.DataType) -> Optional[str]:
        if pa.types.is_boolean(pa_dtype):
            return "BOOL"
        if pa.types.is_integer(pa_dtype):
            return "INT"
        if pa.types.is_floating(pa_dtype):
            return "FLOAT"
        if pa.types.is_string(pa_dtype):
            return "STRING"
        if pa.types.is_temporal(pa_dtype):
            return "DATETIME"
        return None

    @staticmethod
    def convert_table_to_df(table: pa.Table) -> pd.DataFrame:
        return table.to_pandas(integer_object_nulls=True)

    @staticmethod
    def first_nonempty(items: List[Any]) -> Any:
        return next((i for i in items if i), None)

    def is_temporal(self, db_type_str: Optional[str]) -> bool:
        column_spec = self.db_engine_spec.get_column_spec(db_type_str)
        if column_spec is None:
            return False
        return column_spec.is_dttm

    def data_type(self, col_name: str, pa_dtype: pa.DataType) -> Optional[str]:
        """Given a pyarrow data type, Returns a generic database type"""
        set_type = self._type_dict.get(col_name)
        if set_type:
            return set_type

        mapped_type = self.convert_pa_dtype(pa_dtype)
        if mapped_type:
            return mapped_type

        return None

    def to_pandas_df(self) -> pd.DataFrame:
        return self.convert_table_to_df(self.table)

    @property
    def pa_table(self) -> pa.Table:
        return self.table

    @property
    def size(self) -> int:
        return self.table.num_rows

    @property
    def columns(self) -> List[Dict[str, Any]]:
        if not self.table.column_names:
            return []

        columns = []
        for col in self.table.schema:
            db_type_str = self.data_type(col.name, col.type)
            column = {
                "name": col.name,
                "type": db_type_str,
                "is_date": self.is_temporal(db_type_str),
            }
            columns.append(column)

        return columns

""" Rabbitai wrapper around pyarrow.Table."""

import datetime
import json
import logging
from typing import Any, Dict, List, Optional, Tuple, Type

import numpy as np
import pandas as pd
import pyarrow as pa

from rabbitai import db_engine_specs
from rabbitai.typing import DbapiDescription, DbapiResult
from rabbitai.utils import core as utils

logger = logging.getLogger(__name__)


def dedup(l: List[str], suffix: str = "__", case_sensitive: bool = True) -> List[str]:
    """De-duplicates a list of string by suffixing a counter

    Always returns the same number of entries as provided, and always returns
    unique values. Case sensitive comparison by default.

    >>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
    foo,bar,bar__1,bar__2,Bar
    >>> print(
        ','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False))
    )
    foo,bar,bar__1,bar__2,Bar__3
    """
    new_l: List[str] = []
    seen: Dict[str, int] = {}
    for item in l:
        s_fixed_case = item if case_sensitive else item.lower()
        if s_fixed_case in seen:
            seen[s_fixed_case] += 1
            item += suffix + str(seen[s_fixed_case])
        else:
            seen[s_fixed_case] = 0
        new_l.append(item)
    return new_l


def stringify(obj: Any) -> str:
    return json.dumps(obj, default=utils.json_iso_dttm_ser)


def stringify_values(array: np.ndarray) -> np.ndarray:
    vstringify = np.vectorize(stringify)
    return vstringify(array)


def destringify(obj: str) -> Any:
    return json.loads(obj)


class RabbitaiResultSet:
    def __init__(
        self,
        data: DbapiResult,
        cursor_description: DbapiDescription,
        db_engine_spec: Type[db_engine_specs.BaseEngineSpec],
    ):
        self.db_engine_spec = db_engine_spec
        data = data or []
        column_names: List[str] = []
        pa_data: List[pa.Array] = []
        deduped_cursor_desc: List[Tuple[Any, ...]] = []
        numpy_dtype: List[Tuple[str, ...]] = []
        stringified_arr: np.ndarray

        if cursor_description:
            # get deduped list of column names
            column_names = dedup([col[0] for col in cursor_description])

            # fix cursor descriptor with the deduped names
            deduped_cursor_desc = [
                tuple([column_name, *list(description)[1:]])
                for column_name, description in zip(column_names, cursor_description)
            ]

            # generate numpy structured array dtype
            numpy_dtype = [(column_name, "object") for column_name in column_names]

        # only do expensive recasting if datatype is not standard list of tuples
        if data and (not isinstance(data, list) or not isinstance(data[0], tuple)):
            data = [tuple(row) for row in data]
        array = np.array(data, dtype=numpy_dtype)
        if array.size > 0:
            for column in column_names:
                try:
                    pa_data.append(pa.array(array[column].tolist()))
                except (
                    pa.lib.ArrowInvalid,
                    pa.lib.ArrowTypeError,
                    pa.lib.ArrowNotImplementedError,
                    TypeError,  # this is super hackey,
                    # https://issues.apache.org/jira/browse/ARROW-7855
                ):
                    # attempt serialization of values as strings
                    stringified_arr = stringify_values(array[column])
                    pa_data.append(pa.array(stringified_arr.tolist()))

        if pa_data:  # pylint: disable=too-many-nested-blocks
            for i, column in enumerate(column_names):
                if pa.types.is_nested(pa_data[i].type):
                    # TODO: revisit nested column serialization once nested types
                    #  are added as a natively supported column type in Rabbitai
                    #  (rabbitai.utils.core.GenericDataType).
                    stringified_arr = stringify_values(array[column])
                    pa_data[i] = pa.array(stringified_arr.tolist())

                elif pa.types.is_temporal(pa_data[i].type):
                    # workaround for bug converting
                    # `psycopg2.tz.FixedOffsetTimezone` tzinfo values.
                    # related: https://issues.apache.org/jira/browse/ARROW-5248
                    sample = self.first_nonempty(array[column])
                    if sample and isinstance(sample, datetime.datetime):
                        try:
                            if sample.tzinfo:
                                tz = sample.tzinfo
                                series = pd.Series(
                                    array[column], dtype="datetime64[ns]"
                                )
                                series = pd.to_datetime(series).dt.tz_localize(tz)
                                pa_data[i] = pa.Array.from_pandas(
                                    series, type=pa.timestamp("ns", tz=tz)
                                )
                        except Exception as ex:  # pylint: disable=broad-except
                            logger.exception(ex)

        self.table = pa.Table.from_arrays(pa_data, names=column_names)
        self._type_dict: Dict[str, Any] = {}
        try:
            # The driver may not be passing a cursor.description
            self._type_dict = {
                col: db_engine_spec.get_datatype(deduped_cursor_desc[i][1])
                for i, col in enumerate(column_names)
                if deduped_cursor_desc
            }
        except Exception as ex:
            logger.exception(ex)

    @staticmethod
    def convert_pa_dtype(pa_dtype: pa.DataType) -> Optional[str]:
        if pa.types.is_boolean(pa_dtype):
            return "BOOL"
        if pa.types.is_integer(pa_dtype):
            return "INT"
        if pa.types.is_floating(pa_dtype):
            return "FLOAT"
        if pa.types.is_string(pa_dtype):
            return "STRING"
        if pa.types.is_temporal(pa_dtype):
            return "DATETIME"
        return None

    @staticmethod
    def convert_table_to_df(table: pa.Table) -> pd.DataFrame:
        return table.to_pandas(integer_object_nulls=True)

    @staticmethod
    def first_nonempty(items: List[Any]) -> Any:
        return next((i for i in items if i), None)

    def is_temporal(self, db_type_str: Optional[str]) -> bool:
        column_spec = self.db_engine_spec.get_column_spec(db_type_str)
        if column_spec is None:
            return False
        return column_spec.is_dttm

    def data_type(self, col_name: str, pa_dtype: pa.DataType) -> Optional[str]:
        """Given a pyarrow data type, Returns a generic database type"""
        set_type = self._type_dict.get(col_name)
        if set_type:
            return set_type

        mapped_type = self.convert_pa_dtype(pa_dtype)
        if mapped_type:
            return mapped_type

        return None

    def to_pandas_df(self) -> pd.DataFrame:
        return self.convert_table_to_df(self.table)

    @property
    def pa_table(self) -> pa.Table:
        return self.table

    @property
    def size(self) -> int:
        return self.table.num_rows

    @property
    def columns(self) -> List[Dict[str, Any]]:
        if not self.table.column_names:
            return []

        columns = []
        for col in self.table.schema:
            db_type_str = self.data_type(col.name, col.type)
            column = {
                "name": col.name,
                "type": db_type_str,
                "is_date": self.is_temporal(db_type_str),
            }
            columns.append(column)

        return columns
Rabbitai wrapper around pyarrow.Table. De-duplicates a list of string by suffixing a counter      Always returns the same number of entries as provided, and always returns     unique values. Case sensitive comparison by default.      >>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))     foo,bar,bar__1,bar__2,Bar     >>> print(         ','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False))     )     foo,bar,bar__1,bar__2,Bar__3 # get deduped list of column names # fix cursor descriptor with the deduped names # generate numpy structured array dtype # only do expensive recasting if datatype is not standard list of tuples # this is super hackey, # https://issues.apache.org/jira/browse/ARROW-7855 # attempt serialization of values as strings # pylint: disable=too-many-nested-blocks # TODO: revisit nested column serialization once nested types #  are added as a natively supported column type in Rabbitai #  (rabbitai.utils.core.GenericDataType). # workaround for bug converting # `psycopg2.tz.FixedOffsetTimezone` tzinfo values. # related: https://issues.apache.org/jira/browse/ARROW-5248 # pylint: disable=broad-except # The driver may not be passing a cursor.description Given a pyarrow data type, Returns a generic database type