# Copyright (C) 2018-2026 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import numpy as np
import pytest

from common.tf_layer_test_class import CommonTFLayerTest


# Testing Cumsum operation
# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/Cumsum

class TestCumsum(CommonTFLayerTest):
    # input_shape - should be an array
    # axis - array which points on axis for the operation
    # exclusive - enables exclusive Cumsum
    # reverse - enables reverse order of Cumsum
    def create_cumsum_net(self, input_shape, axis, exclusive, reverse):
        import tensorflow as tf

        tf.compat.v1.reset_default_graph()

        # Create the graph and model
        with tf.compat.v1.Session() as sess:
            tf_input = tf.compat.v1.placeholder(tf.float32, input_shape, 'Input')

            tf_axis = tf.constant(axis, dtype=tf.int32)
            tf.raw_ops.Cumsum(x=tf_input, axis=tf_axis, exclusive=exclusive, reverse=reverse)

            tf.compat.v1.global_variables_initializer()
            tf_net = sess.graph_def

        ref_net = None

        return tf_net, ref_net

    test_data = [
        dict(input_shape=[2], axis=-1),
        dict(input_shape=[2, 3], axis=0),
        dict(input_shape=[2, 3], axis=1),
        dict(input_shape=[2, 3], axis=-2),
        dict(input_shape=[2, 3, 3, 4], axis=2),
        dict(input_shape=[2, 3, 3, 4], axis=-3),
    ]

    @pytest.mark.parametrize("params", test_data)
    @pytest.mark.parametrize("exclusive", [False, True, None])
    @pytest.mark.parametrize("reverse", [False, True, None])
    @pytest.mark.precommit
    @pytest.mark.precommit
    @pytest.mark.nightly
    def test_cumsum_basic(self, params, exclusive, reverse, ie_device, precision, ir_version, temp_dir):
        self._test(*self.create_cumsum_net(**params, exclusive=exclusive, reverse=reverse),
                   ie_device, precision, ir_version, temp_dir=temp_dir)


class TestComplexCumsum(CommonTFLayerTest):
    # input_shape - should be an array
    # axis - array which points on axis for the operation
    # exclusive - enables exclusive Cumsum
    # reverse - enables reverse order of Cumsum
    def _prepare_input(self, inputs_info):
        rng = np.random.default_rng()
        assert 'x_real:0' in inputs_info
        assert 'x_imag:0' in inputs_info
        x_shape = inputs_info['x_real:0']
        inputs_data = {}

        inputs_data['x_real:0'] = 4 * rng.random(x_shape).astype(np.float64) - 2
        inputs_data['x_imag:0'] = 4 * rng.random(x_shape).astype(np.float64) - 2

        return inputs_data

    def create_cumsum_net(self, input_shape, axis, exclusive, reverse):
        import tensorflow as tf

        tf.compat.v1.reset_default_graph()

        # Create the graph and model
        with tf.compat.v1.Session() as sess:
            x_real = tf.compat.v1.placeholder(tf.float32, input_shape, 'x_real')
            x_imag = tf.compat.v1.placeholder(tf.float32, input_shape, 'x_imag')
            complex_input = tf.complex(x_real, x_imag)
            tf_axis = tf.constant(axis, dtype=tf.int32)
            result = tf.raw_ops.Cumsum(x=complex_input, axis=tf_axis, exclusive=exclusive, reverse=reverse)
            real = tf.raw_ops.Real(input=result)
            img = tf.raw_ops.Imag(input=result)

            tf.compat.v1.global_variables_initializer()
            tf_net = sess.graph_def

        ref_net = None

        return tf_net, ref_net

    test_data = [
        dict(input_shape=[2], axis=-1),
        dict(input_shape=[2, 3], axis=0),
        dict(input_shape=[2, 3], axis=1),
        dict(input_shape=[2, 3], axis=-2),
        dict(input_shape=[2, 3, 3, 4], axis=2),
        dict(input_shape=[2, 3, 3, 4], axis=-3),
    ]

    @pytest.mark.parametrize("params", test_data)
    @pytest.mark.parametrize("exclusive", [False, True, None])
    @pytest.mark.parametrize("reverse", [False, True, None])
    @pytest.mark.precommit
    @pytest.mark.nightly
    def test_cumsum_basic(self, params, exclusive, reverse, ie_device, precision, ir_version, temp_dir):
        self._test(*self.create_cumsum_net(**params, exclusive=exclusive, reverse=reverse),
                   ie_device, precision, ir_version, temp_dir=temp_dir)