"""
.. codeauthor:: Tsuyoshi Hombashi <tsuyoshi.hombashi@gmail.com>
"""

import copy
import re
from collections import OrderedDict, namedtuple
from collections.abc import Iterator, Sequence
from typing import TYPE_CHECKING, Any, Optional, Union

import dataproperty as dp
import typepy
from dataproperty import DataPropertyMatrix
from dataproperty.typing import TypeHint
from typepy import Nan

from ._constant import PatternMatch
from ._converter import to_value_matrix
from ._logger import logger  # type: ignore


if TYPE_CHECKING:
    import pandas


class TableData:
    """
    Class to represent a table data structure.

    :param table_name: Name of the table.
    :param  headers: Table header names.
    :param rows: Data of the table.
    """

    def __init__(
        self,
        table_name: Optional[str],
        headers: Sequence[str],
        rows: Sequence,
        dp_extractor: Optional[dp.DataPropertyExtractor] = None,
        type_hints: Optional[Sequence[Union[str, TypeHint]]] = None,
        max_workers: Optional[int] = None,
        max_precision: Optional[int] = None,
    ) -> None:
        self.__table_name = table_name
        self.__value_matrix: list[list[Any]] = []
        self.__value_dp_matrix: Optional[DataPropertyMatrix] = None

        if rows:
            self.__rows = rows
        else:
            self.__rows = []

        if dp_extractor:
            self.__dp_extractor = copy.deepcopy(dp_extractor)
        else:
            self.__dp_extractor = dp.DataPropertyExtractor(max_precision=max_precision)

        if type_hints:
            self.__dp_extractor.column_type_hints = type_hints

        self.__dp_extractor.strip_str_header = '"'

        if max_workers:
            self.__dp_extractor.max_workers = max_workers

        if not headers:
            self.__dp_extractor.headers = []
        else:
            self.__dp_extractor.headers = headers

    def __repr__(self) -> str:
        element_list = [f"table_name={self.table_name}"]

        try:
            element_list.append("headers=[{}]".format(", ".join(self.headers)))
        except TypeError:
            element_list.append("headers=None")

        element_list.extend([f"cols={self.num_columns}", f"rows={self.num_rows}"])

        return ", ".join(element_list)

    def __eq__(self, other: Any) -> bool:
        if not isinstance(other, TableData):
            return False

        return self.equals(other, cmp_by_dp=False)

    def __ne__(self, other: Any) -> bool:
        if not isinstance(other, TableData):
            return True

        return not self.equals(other, cmp_by_dp=False)

    @property
    def table_name(self) -> Optional[str]:
        """str: Name of the table."""

        return self.__table_name

    @table_name.setter
    def table_name(self, value: Optional[str]) -> None:
        self.__table_name = value

    @property
    def headers(self) -> Sequence[str]:
        """Sequence[str]: Table header names."""

        return self.__dp_extractor.headers

    @property
    def rows(self) -> Sequence:
        """Sequence: Original rows of tabular data."""

        return self.__rows

    @property
    def value_matrix(self) -> DataPropertyMatrix:
        """DataPropertyMatrix: Converted rows of tabular data."""

        if self.__value_matrix:
            return self.__value_matrix

        self.__value_matrix = [
            [value_dp.data for value_dp in value_dp_list] for value_dp_list in self.value_dp_matrix
        ]

        return self.__value_matrix

    @property
    def has_value_dp_matrix(self) -> bool:
        return self.__value_dp_matrix is not None

    @property
    def max_workers(self) -> int:
        return self.__dp_extractor.max_workers

    @max_workers.setter
    def max_workers(self, value: Optional[int]) -> None:
        self.__dp_extractor.max_workers = value

    @property
    def num_rows(self) -> Optional[int]:
        """Optional[int]:
        Number of rows in the tabular data.
        |None| if the ``rows`` is neither list nor tuple.
        """

        try:
            return len(self.rows)
        except TypeError:
            return None

    @property
    def num_columns(self) -> Optional[int]:
        if typepy.is_not_empty_sequence(self.headers):
            return len(self.headers)

        try:
            return len(self.rows[0])
        except TypeError:
            return None
        except IndexError:
            return 0

    @property
    def value_dp_matrix(self) -> DataPropertyMatrix:
        """DataPropertyMatrix: DataProperty for table data."""

        if self.__value_dp_matrix is None:
            self.__value_dp_matrix = self.__dp_extractor.to_dp_matrix(
                to_value_matrix(self.headers, self.rows)
            )

        return self.__value_dp_matrix

    @property
    def header_dp_list(self) -> list[dp.DataProperty]:
        return self.__dp_extractor.to_header_dp_list()

    @property
    def column_dp_list(self) -> list[dp.ColumnDataProperty]:
        return self.__dp_extractor.to_column_dp_list(self.value_dp_matrix)

    @property
    def dp_extractor(self) -> dp.DataPropertyExtractor:
        return self.__dp_extractor

    def is_empty_header(self) -> bool:
        """bool: |True| if the data :py:attr:`.headers` is empty."""

        return typepy.is_empty_sequence(self.headers)

    def is_empty_rows(self) -> bool:
        """
        :return: |True| if the tabular data has no rows.
        :rtype: bool
        """

        return self.num_rows == 0

    def is_empty(self) -> bool:
        """
        :return:
            |True| if the data :py:attr:`.headers` or
            :py:attr:`.value_matrix` is empty.
        :rtype: bool
        """

        return any([self.is_empty_header(), self.is_empty_rows()])

    def equals(self, other: "TableData", cmp_by_dp: bool = True) -> bool:
        if cmp_by_dp:
            return self.__equals_dp(other)

        return self.__equals_raw(other)

    def __equals_base(self, other: "TableData") -> bool:
        compare_item_list = [self.table_name == other.table_name]

        if self.num_rows is not None:
            compare_item_list.append(self.num_rows == other.num_rows)

        return all(compare_item_list)

    def __equals_raw(self, other: "TableData") -> bool:
        if not self.__equals_base(other):
            return False

        if self.headers != other.headers:
            return False

        for lhs_row, rhs_row in zip(self.rows, other.rows):
            if len(lhs_row) != len(rhs_row):
                return False

            if not all(
                [
                    lhs == rhs
                    for lhs, rhs in zip(lhs_row, rhs_row)
                    if not Nan(lhs).is_type() and not Nan(rhs).is_type()
                ]
            ):
                return False

        return True

    def __equals_dp(self, other: "TableData") -> bool:
        if not self.__equals_base(other):
            return False

        if self.header_dp_list != other.header_dp_list:
            return False

        if self.value_dp_matrix is None or other.value_dp_matrix is None:
            return False

        for lhs_list, rhs_list in zip(self.value_dp_matrix, other.value_dp_matrix):
            if len(lhs_list) != len(rhs_list):
                return False

            if any([lhs != rhs for lhs, rhs in zip(lhs_list, rhs_list)]):
                return False

        return True

    def in_tabledata_list(self, other: Sequence["TableData"], cmp_by_dp: bool = True) -> bool:
        for table_data in other:
            if self.equals(table_data, cmp_by_dp=cmp_by_dp):
                return True

        return False

    def validate_rows(self) -> None:
        """
        :raises ValueError:
        """

        invalid_row_idx_list = []

        for row_idx, row in enumerate(self.rows):
            if isinstance(row, (list, tuple)) and len(self.headers) != len(row):
                invalid_row_idx_list.append(row_idx)

            if isinstance(row, dict):
                if not all([header in row for header in self.headers]):
                    invalid_row_idx_list.append(row_idx)

        if not invalid_row_idx_list:
            return

        for invalid_row_idx in invalid_row_idx_list:
            logger.debug(f"invalid row (line={invalid_row_idx}): {self.rows[invalid_row_idx]}")

        raise ValueError(
            "table header length and row length are mismatch:\n"
            + f"  header(len={len(self.headers)}): {self.headers}\n"
            + "  # of miss match rows: {} ouf of {}\n".format(
                len(invalid_row_idx_list), self.num_rows
            )
        )

    def as_dict(self, default_key: str = "table") -> dict[str, list["OrderedDict[str, Any]"]]:
        """
        Args:
            default_key:
                Key of a returning dictionary when the ``table_name`` is empty.

        Returns:
            dict: Table data as a |dict| instance.

        Sample Code:
            .. code:: python

                from tabledata import TableData

                TableData(
                    "sample",
                    ["a", "b"],
                    [[1, 2], [3.3, 4.4]]
                ).as_dict()

        Output:
            .. code:: json

                {'sample': [OrderedDict([('a', 1), ('b', 2)]), OrderedDict([('a', 3.3), ('b', 4.4)])]}
        """  # noqa

        dict_body = []
        for row in self.value_matrix:
            if not row:
                continue

            values = [
                (header, value) for header, value in zip(self.headers, row) if value is not None
            ]

            if not values:
                continue

            dict_body.append(OrderedDict(values))

        table_name = self.table_name
        if not table_name:
            table_name = default_key

        return {table_name: dict_body}

    def as_tuple(self) -> Iterator[tuple]:
        """
        :return: Rows of the tuple.
        :rtype: list of |namedtuple|

        :Sample Code:
            .. code:: python

                from tabledata import TableData

                records = TableData(
                    "sample",
                    ["a", "b"],
                    [[1, 2], [3.3, 4.4]]
                ).as_tuple()
                for record in records:
                    print(record)

        :Output:
            .. code-block:: none

                Row(a=1, b=2)
                Row(a=Decimal('3.3'), b=Decimal('4.4'))
        """

        Row = namedtuple("Row", self.headers)  # type: ignore

        for value_dp_list in self.value_dp_matrix:
            if typepy.is_empty_sequence(value_dp_list):
                continue

            row = Row(*(value_dp.data for value_dp in value_dp_list))

            yield row

    def as_dataframe(self) -> "pandas.DataFrame":
        """
        :return: Table data as a ``pandas.DataFrame`` instance.
        :rtype: pandas.DataFrame

        :Sample Code:
            .. code-block:: python

                from tabledata import TableData

                TableData(
                    "sample",
                    ["a", "b"],
                    [[1, 2], [3.3, 4.4]]
                ).as_dataframe()

        :Output:
            .. code-block:: none

                     a    b
                0    1    2
                1  3.3  4.4

        :Dependency Packages:
            - `pandas <https://pandas.pydata.org/>`__
        """

        try:
            from pandas import DataFrame
        except ImportError:
            raise RuntimeError("required 'pandas' package to execute as_dataframe method")

        dataframe = DataFrame(self.value_matrix)
        if not self.is_empty_header():
            dataframe.columns = self.headers

        return dataframe

    def transpose(self) -> "TableData":
        return TableData(
            self.table_name,
            self.headers,
            [row for row in zip(*self.rows)],
            max_workers=self.max_workers,
        )

    def filter_column(
        self,
        patterns: Optional[str] = None,
        is_invert_match: bool = False,
        is_re_match: bool = False,
        pattern_match: PatternMatch = PatternMatch.OR,
    ) -> "TableData":
        logger.debug(
            "filter_column: patterns={}, is_invert_match={}, "
            "is_re_match={}, pattern_match={}".format(
                patterns, is_invert_match, is_re_match, pattern_match
            )
        )

        if not patterns:
            return self

        match_header_list = []
        match_column_matrix = []

        if pattern_match == PatternMatch.OR:
            match_method = any
        elif pattern_match == PatternMatch.AND:
            match_method = all
        else:
            raise ValueError(f"unknown matching: {pattern_match}")

        for header, column in zip(self.headers, zip(*self.rows)):
            is_match_list = []
            for pattern in patterns:
                is_match = self.__is_match(header, pattern, is_re_match)

                is_match_list.append(
                    any([is_match and not is_invert_match, not is_match and is_invert_match])
                )

            if match_method(is_match_list):
                match_header_list.append(header)
                match_column_matrix.append(column)

        logger.debug(
            "filter_column: table={}, match_header_list={}".format(
                self.table_name, match_header_list
            )
        )

        return TableData(
            self.table_name,
            match_header_list,
            list(zip(*match_column_matrix)),
            max_workers=self.max_workers,
        )

    @staticmethod
    def from_dataframe(
        dataframe: "pandas.DataFrame",
        table_name: str = "",
        type_hints: Optional[Sequence[TypeHint]] = None,
        max_workers: Optional[int] = None,
    ) -> "TableData":
        """
        Initialize TableData instance from a pandas.DataFrame instance.

        :param pandas.DataFrame dataframe:
        :param str table_name: Table name to create.
        """

        return TableData(
            table_name,
            list(dataframe.columns.values),
            dataframe.values.tolist(),
            type_hints=type_hints,
            max_workers=max_workers,
        )

    @staticmethod
    def __is_match(header: str, pattern: str, is_re_match: bool) -> bool:
        if is_re_match:
            return re.search(pattern, header) is not None

        return header == pattern