from typing import TYPE_CHECKING, List, Tuple

import numpy as np

from pandas._typing import ArrayLike

if TYPE_CHECKING:
    from pandas.core.internals.blocks import Block  # noqa:F401
    from pandas.core.internals.managers import BlockManager  # noqa:F401


def operate_blockwise(
    left: "BlockManager", right: "BlockManager", array_op
) -> "BlockManager":
    # At this point we have already checked the parent DataFrames for
    #  assert rframe._indexed_same(lframe)

    res_blks: List["Block"] = []
    for n, blk in enumerate(left.blocks):
        locs = blk.mgr_locs
        blk_vals = blk.values

        left_ea = not isinstance(blk_vals, np.ndarray)

        rblks = right._slice_take_blocks_ax0(locs.indexer, only_slice=True)

        # Assertions are disabled for performance, but should hold:
        # if left_ea:
        #    assert len(locs) == 1, locs
        #    assert len(rblks) == 1, rblks
        #    assert rblks[0].shape[0] == 1, rblks[0].shape

        for k, rblk in enumerate(rblks):
            right_ea = not isinstance(rblk.values, np.ndarray)

            lvals, rvals = _get_same_shape_values(blk, rblk, left_ea, right_ea)

            res_values = array_op(lvals, rvals)
            if left_ea and not right_ea and hasattr(res_values, "reshape"):
                res_values = res_values.reshape(1, -1)
            nbs = rblk._split_op_result(res_values)

            # Assertions are disabled for performance, but should hold:
            # if right_ea or left_ea:
            #    assert len(nbs) == 1
            # else:
            #    assert res_values.shape == lvals.shape, (res_values.shape, lvals.shape)

            _reset_block_mgr_locs(nbs, locs)

            res_blks.extend(nbs)

    # Assertions are disabled for performance, but should hold:
    #  slocs = {y for nb in res_blks for y in nb.mgr_locs.as_array}
    #  nlocs = sum(len(nb.mgr_locs.as_array) for nb in res_blks)
    #  assert nlocs == len(left.items), (nlocs, len(left.items))
    #  assert len(slocs) == nlocs, (len(slocs), nlocs)
    #  assert slocs == set(range(nlocs)), slocs

    new_mgr = type(right)(res_blks, axes=right.axes, do_integrity_check=False)
    return new_mgr


def _reset_block_mgr_locs(nbs: List["Block"], locs):
    """
    Reset mgr_locs to correspond to our original DataFrame.
    """
    for nb in nbs:
        nblocs = locs.as_array[nb.mgr_locs.indexer]
        nb.mgr_locs = nblocs
        # Assertions are disabled for performance, but should hold:
        #  assert len(nblocs) == nb.shape[0], (len(nblocs), nb.shape)
        #  assert all(x in locs.as_array for x in nb.mgr_locs.as_array)


def _get_same_shape_values(
    lblk: "Block", rblk: "Block", left_ea: bool, right_ea: bool
) -> Tuple[ArrayLike, ArrayLike]:
    """
    Slice lblk.values to align with rblk.  Squeeze if we have EAs.
    """
    lvals = lblk.values
    rvals = rblk.values

    # Require that the indexing into lvals be slice-like
    assert rblk.mgr_locs.is_slice_like, rblk.mgr_locs

    # TODO(EA2D): with 2D EAs pnly this first clause would be needed
    if not (left_ea or right_ea):
        lvals = lvals[rblk.mgr_locs.indexer, :]
        assert lvals.shape == rvals.shape, (lvals.shape, rvals.shape)
    elif left_ea and right_ea:
        assert lvals.shape == rvals.shape, (lvals.shape, rvals.shape)
    elif right_ea:
        # lvals are 2D, rvals are 1D
        lvals = lvals[rblk.mgr_locs.indexer, :]
        assert lvals.shape[0] == 1, lvals.shape
        lvals = lvals[0, :]
    else:
        # lvals are 1D, rvals are 2D
        assert rvals.shape[0] == 1, rvals.shape
        rvals = rvals[0, :]

    return lvals, rvals