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import abc
import itertools
import dataclassabc

from polymatrix.polymatrix.mixins import PolyMatrixMixin
from polymatrix.polymatrix.abc import PolyMatrix
from polymatrix.expressionstate.abc import ExpressionState
from polymatrix.expression.mixins.expressionbasemixin import ExpressionBaseMixin


class BlockDiagExprMixin(ExpressionBaseMixin):
    """
    Create a block diagonal polymatrix from provided polymatrices

        [[x1]], [[x2], [x3]]  ->  [[x1, 0], [0, x2], [0, x3]].
    """

    @property
    @abc.abstractmethod
    def underlying(self) -> tuple[ExpressionBaseMixin, ...]: ...

    # overwrites the abstract method of `ExpressionBaseMixin`
    def apply(
        self,
        state: ExpressionState,
    ) -> tuple[ExpressionState, PolyMatrix]: # FIXME: incorrect return type
        all_underlying = []
        for expr in self.underlying:
            state, polymat = expr.apply(state=state)
            all_underlying.append(polymat)

        # NP: this is a very weird place to put a class
        @dataclassabc.dataclassabc(frozen=True)
        class BlockDiagPolyMatrix(PolyMatrixMixin):
            all_underlying: tuple[PolyMatrixMixin]
            underlying_row_col_range: tuple[tuple[int, int], ...]
            shape: tuple[int, int]

            # FIXME: typing problems
            def get_poly(self, row: int, col: int) -> dict[tuple[int, ...], float]:
                for polymatrix, ((row_start, col_start), (row_end, col_end)) in zip(
                    self.all_underlying, self.underlying_row_col_range
                ):
                    if row_start <= row < row_end:
                        if col_start <= col < col_end:
                            return polymatrix.get_poly(
                                row=row - row_start,
                                col=col - col_start,
                            )

                        else:
                            return None

                # NP: Do not raise generic expression, specialize error
                raise Exception(f"row {row} is out of bounds")

        underlying_row_col_range = tuple(
            itertools.pairwise(
                itertools.accumulate(
                    (expr.shape for expr in all_underlying),
                    lambda acc, v: tuple(v1 + v2 for v1, v2 in zip(acc, v)),
                    initial=(0, 0),
                )
            )
        )

        shape = underlying_row_col_range[-1][1]

        polymatrix = BlockDiagPolyMatrix(
            all_underlying=all_underlying,
            shape=shape,
            underlying_row_col_range=underlying_row_col_range,
        )

        return state, polymatrix