# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

from __future__ import annotations

import enum
from typing import (
    Any,
    Dict,
    Iterable,
    Optional,
    Tuple,
)

import sys
if sys.version_info >= (3, 8):
    from typing import TypedDict
else:
    from typing_extensions import TypedDict

import pyarrow as pa
import pyarrow.compute as pc
from pyarrow.interchange.buffer import _PyArrowBuffer


class DtypeKind(enum.IntEnum):
    """
    Integer enum for data types.

    Attributes
    ----------
    INT : int
        Matches to signed integer data type.
    UINT : int
        Matches to unsigned integer data type.
    FLOAT : int
        Matches to floating point data type.
    BOOL : int
        Matches to boolean data type.
    STRING : int
        Matches to string data type (UTF-8 encoded).
    DATETIME : int
        Matches to datetime data type.
    CATEGORICAL : int
        Matches to categorical data type.
    """

    INT = 0
    UINT = 1
    FLOAT = 2
    BOOL = 20
    STRING = 21  # UTF-8
    DATETIME = 22
    CATEGORICAL = 23


Dtype = Tuple[DtypeKind, int, str, str]  # see Column.dtype


_PYARROW_KINDS = {
    pa.int8(): (DtypeKind.INT, "c"),
    pa.int16(): (DtypeKind.INT, "s"),
    pa.int32(): (DtypeKind.INT, "i"),
    pa.int64(): (DtypeKind.INT, "l"),
    pa.uint8(): (DtypeKind.UINT, "C"),
    pa.uint16(): (DtypeKind.UINT, "S"),
    pa.uint32(): (DtypeKind.UINT, "I"),
    pa.uint64(): (DtypeKind.UINT, "L"),
    pa.float16(): (DtypeKind.FLOAT, "e"),
    pa.float32(): (DtypeKind.FLOAT, "f"),
    pa.float64(): (DtypeKind.FLOAT, "g"),
    pa.bool_(): (DtypeKind.BOOL, "b"),
    pa.string(): (DtypeKind.STRING, "u"),
    pa.large_string(): (DtypeKind.STRING, "U"),
}


class ColumnNullType(enum.IntEnum):
    """
    Integer enum for null type representation.

    Attributes
    ----------
    NON_NULLABLE : int
        Non-nullable column.
    USE_NAN : int
        Use explicit float NaN value.
    USE_SENTINEL : int
        Sentinel value besides NaN.
    USE_BITMASK : int
        The bit is set/unset representing a null on a certain position.
    USE_BYTEMASK : int
        The byte is set/unset representing a null on a certain position.
    """

    NON_NULLABLE = 0
    USE_NAN = 1
    USE_SENTINEL = 2
    USE_BITMASK = 3
    USE_BYTEMASK = 4


class ColumnBuffers(TypedDict):
    # first element is a buffer containing the column data;
    # second element is the data buffer's associated dtype
    data: Tuple[_PyArrowBuffer, Dtype]

    # first element is a buffer containing mask values indicating missing data;
    # second element is the mask value buffer's associated dtype.
    # None if the null representation is not a bit or byte mask
    validity: Optional[Tuple[_PyArrowBuffer, Dtype]]

    # first element is a buffer containing the offset values for
    # variable-size binary data (e.g., variable-length strings);
    # second element is the offsets buffer's associated dtype.
    # None if the data buffer does not have an associated offsets buffer
    offsets: Optional[Tuple[_PyArrowBuffer, Dtype]]


class CategoricalDescription(TypedDict):
    # whether the ordering of dictionary indices is semantically meaningful
    is_ordered: bool
    # whether a dictionary-style mapping of categorical values to other objects
    # exists
    is_dictionary: bool
    # Python-level only (e.g. ``{int: str}``).
    # None if not a dictionary-style categorical.
    categories: Optional[_PyArrowColumn]


class Endianness:
    """Enum indicating the byte-order of a data-type."""

    LITTLE = "<"
    BIG = ">"
    NATIVE = "="
    NA = "|"


class NoBufferPresent(Exception):
    """Exception to signal that there is no requested buffer."""


class _PyArrowColumn:
    """
    A column object, with only the methods and properties required by the
    interchange protocol defined.

    A column can contain one or more chunks. Each chunk can contain up to three
    buffers - a data buffer, a mask buffer (depending on null representation),
    and an offsets buffer (if variable-size binary; e.g., variable-length
    strings).

    TBD: Arrow has a separate "null" dtype, and has no separate mask concept.
         Instead, it seems to use "children" for both columns with a bit mask,
         and for nested dtypes. Unclear whether this is elegant or confusing.
         This design requires checking the null representation explicitly.

         The Arrow design requires checking:
         1. the ARROW_FLAG_NULLABLE (for sentinel values)
         2. if a column has two children, combined with one of those children
            having a null dtype.

         Making the mask concept explicit seems useful. One null dtype would
         not be enough to cover both bit and byte masks, so that would mean
         even more checking if we did it the Arrow way.

    TBD: there's also the "chunk" concept here, which is implicit in Arrow as
         multiple buffers per array (= column here). Semantically it may make
         sense to have both: chunks were meant for example for lazy evaluation
         of data which doesn't fit in memory, while multiple buffers per column
         could also come from doing a selection operation on a single
         contiguous buffer.

         Given these concepts, one would expect chunks to be all of the same
         size (say a 10,000 row dataframe could have 10 chunks of 1,000 rows),
         while multiple buffers could have data-dependent lengths. Not an issue
         in pandas if one column is backed by a single NumPy array, but in
         Arrow it seems possible.
         Are multiple chunks *and* multiple buffers per column necessary for
         the purposes of this interchange protocol, or must producers either
         reuse the chunk concept for this or copy the data?

    Note: this Column object can only be produced by ``__dataframe__``, so
          doesn't need its own version or ``__column__`` protocol.
    """

    def __init__(
        self, column: pa.Array | pa.ChunkedArray, allow_copy: bool = True
    ) -> None:
        """
        Handles PyArrow Arrays and ChunkedArrays.
        """
        # Store the column as a private attribute
        if isinstance(column, pa.ChunkedArray):
            if column.num_chunks == 1:
                column = column.chunk(0)
            else:
                if not allow_copy:
                    raise RuntimeError(
                        "Chunks will be combined and a copy is required which "
                        "is forbidden by allow_copy=False"
                    )
                column = column.combine_chunks()

        self._allow_copy = allow_copy

        if pa.types.is_boolean(column.type):
            if not allow_copy:
                raise RuntimeError(
                    "Boolean column will be casted to uint8 and a copy "
                    "is required which is forbidden by allow_copy=False"
                )
            self._dtype = self._dtype_from_arrowdtype(column.type, 8)
            self._col = pc.cast(column, pa.uint8())
        else:
            self._col = column
            dtype = self._col.type
            try:
                bit_width = dtype.bit_width
            except ValueError:
                # in case of a variable-length strings, considered as array
                # of bytes (8 bits)
                bit_width = 8
            self._dtype = self._dtype_from_arrowdtype(dtype, bit_width)

    def size(self) -> int:
        """
        Size of the column, in elements.

        Corresponds to DataFrame.num_rows() if column is a single chunk;
        equal to size of this current chunk otherwise.

        Is a method rather than a property because it may cause a (potentially
        expensive) computation for some dataframe implementations.
        """
        return len(self._col)

    @property
    def offset(self) -> int:
        """
        Offset of first element.

        May be > 0 if using chunks; for example for a column with N chunks of
        equal size M (only the last chunk may be shorter),
        ``offset = n * M``, ``n = 0 .. N-1``.
        """
        return self._col.offset

    @property
    def dtype(self) -> Tuple[DtypeKind, int, str, str]:
        """
        Dtype description as a tuple ``(kind, bit-width, format string,
        endianness)``.

        Bit-width : the number of bits as an integer
        Format string : data type description format string in Apache Arrow C
                        Data Interface format.
        Endianness : current only native endianness (``=``) is supported

        Notes:
            - Kind specifiers are aligned with DLPack where possible (hence the
              jump to 20, leave enough room for future extension)
            - Masks must be specified as boolean with either bit width 1 (for
              bit masks) or 8 (for byte masks).
            - Dtype width in bits was preferred over bytes
            - Endianness isn't too useful, but included now in case in the
              future we need to support non-native endianness
            - Went with Apache Arrow format strings over NumPy format strings
              because they're more complete from a dataframe perspective
            - Format strings are mostly useful for datetime specification, and
              for categoricals.
            - For categoricals, the format string describes the type of the
              categorical in the data buffer. In case of a separate encoding of
              the categorical (e.g. an integer to string mapping), this can
              be derived from ``self.describe_categorical``.
            - Data types not included: complex, Arrow-style null, binary,
              decimal, and nested (list, struct, map, union) dtypes.
        """
        return self._dtype

    def _dtype_from_arrowdtype(
        self, dtype: pa.DataType, bit_width: int
    ) -> Tuple[DtypeKind, int, str, str]:
        """
        See `self.dtype` for details.
        """
        # Note: 'c' (complex) not handled yet (not in array spec v1).
        #       'b', 'B' (bytes), 'S', 'a', (old-style string) 'V' (void)
        #       not handled datetime and timedelta both map to datetime
        #       (is timedelta handled?)

        if pa.types.is_timestamp(dtype):
            kind = DtypeKind.DATETIME
            ts = dtype.unit[0]
            tz = dtype.tz if dtype.tz else ""
            f_string = "ts{ts}:{tz}".format(ts=ts, tz=tz)
            return kind, bit_width, f_string, Endianness.NATIVE
        elif pa.types.is_dictionary(dtype):
            kind = DtypeKind.CATEGORICAL
            arr = self._col
            indices_dtype = arr.indices.type
            _, f_string = _PYARROW_KINDS.get(indices_dtype)
            return kind, bit_width, f_string, Endianness.NATIVE
        else:
            kind, f_string = _PYARROW_KINDS.get(dtype, (None, None))
            if kind is None:
                raise ValueError(
                    f"Data type {dtype} not supported by interchange protocol")

            return kind, bit_width, f_string, Endianness.NATIVE

    @property
    def describe_categorical(self) -> CategoricalDescription:
        """
        If the dtype is categorical, there are two options:
        - There are only values in the data buffer.
        - There is a separate non-categorical Column encoding categorical
          values.

        Raises TypeError if the dtype is not categorical

        Returns the dictionary with description on how to interpret the
        data buffer:
            - "is_ordered" : bool, whether the ordering of dictionary indices
                             is semantically meaningful.
            - "is_dictionary" : bool, whether a mapping of
                                categorical values to other objects exists
            - "categories" : Column representing the (implicit) mapping of
                             indices to category values (e.g. an array of
                             cat1, cat2, ...). None if not a dictionary-style