# SPDX-License-Identifier: Apache-2.0


import numpy as np  # type: ignore

import onnx
from ..base import Base
from . import expect
from typing import Tuple, Text


def einsum_reference_implementation(Eqn: Text, Operands: Tuple[np.ndarray, ...]) -> np.ndarray:
    Z = np.einsum(Eqn, *Operands)
    return Z


class Einsum(Base):

    @staticmethod
    def export_einsum_transpose() -> None:
        Eqn = 'ij->ji'
        node = onnx.helper.make_node(
            'Einsum',
            inputs=['x'],
            outputs=['y'],
            equation=Eqn
        )

        X = np.random.randn(3, 4)
        Y = einsum_reference_implementation(Eqn, (X,))

        expect(node, inputs=[X], outputs=[Y], name='test_einsum_transpose')

    @staticmethod
    def export_einsum_sum() -> None:
        Eqn = 'ij->i'
        node = onnx.helper.make_node(
            'Einsum',
            inputs=['x'],
            outputs=['y'],
            equation=Eqn
        )

        X = np.random.randn(3, 4)
        Z = einsum_reference_implementation(Eqn, (X,))

        expect(node, inputs=[X], outputs=[Z], name='test_einsum_sum')

    @staticmethod
    def export_einsum_batch_diagonal() -> None:
        Eqn = '...ii ->...i'
        node = onnx.helper.make_node(
            'Einsum',
            inputs=['x'],
            outputs=['y'],
            equation=Eqn
        )

        X = np.random.randn(3, 5, 5)
        Z = einsum_reference_implementation(Eqn, (X,))

        expect(node, inputs=[X], outputs=[Z], name='test_einsum_batch_diagonal')

    @staticmethod
    def export_einsum_inner_prod() -> None:
        Eqn = 'i,i'
        node = onnx.helper.make_node(
            'Einsum',
            inputs=['x', 'y'],
            outputs=['z'],
            equation=Eqn
        )

        X = np.random.randn(5)
        Y = np.random.randn(5)
        Z = einsum_reference_implementation(Eqn, (X, Y))

        expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_inner_prod')

    @staticmethod
    def export_einsum_batch_matmul() -> None:
        Eqn = 'bij, bjk -> bik'
        node = onnx.helper.make_node(
            'Einsum',
            inputs=['x', 'y'],
            outputs=['z'],
            equation=Eqn
        )

        X = np.random.randn(5, 2, 3)
        Y = np.random.randn(5, 3, 4)
        Z = einsum_reference_implementation(Eqn, (X, Y))

        expect(node, inputs=[X, Y], outputs=[Z], name='test_einsum_batch_matmul')
