# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0

import numpy as np

import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect


class DFT(Base):
    @staticmethod
    def export() -> None:
        node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=1)
        x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
        y = np.fft.fft(x, axis=0)

        x = x.reshape(1, 10, 10, 1)
        y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
        expect(node, inputs=[x], outputs=[y], name="test_dft")

        node = onnx.helper.make_node("DFT", inputs=["x"], outputs=["y"], axis=2)
        x = np.arange(0, 100).reshape(10, 10).astype(np.float32)
        y = np.fft.fft(x, axis=1)

        x = x.reshape(1, 10, 10, 1)
        y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
        expect(node, inputs=[x], outputs=[y], name="test_dft_axis")

        node = onnx.helper.make_node(
            "DFT", inputs=["x"], outputs=["y"], inverse=1, axis=1
        )
        x = np.arange(0, 100, dtype=np.complex64).reshape(
            10,
            10,
        )
        y = np.fft.ifft(x, axis=0)

        x = np.stack((x.real, x.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
        y = np.stack((y.real, y.imag), axis=2).astype(np.float32).reshape(1, 10, 10, 2)
        expect(node, inputs=[x], outputs=[y], name="test_dft_inverse")
