# Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations

import numpy as np

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


def apply_adam(
    r, t, x, g, v, h, norm_coefficient, norm_coefficient_post, alpha, beta, epsilon
):
    # Add gradient of regularization term.
    g_regularized = norm_coefficient * x + g
    # Update momentum.
    v_new = alpha * v + (1 - alpha) * g_regularized
    # Update second-order momentum.
    h_new = beta * h + (1 - beta) * (g_regularized * g_regularized)
    # Compute element-wise square root.
    h_sqrt = np.sqrt(h_new) + epsilon
    # Adjust learning rate.
    r_adjusted = None
    if t > 0:
        # Consider bias correction on momentums.
        r_adjusted = r * np.sqrt(1 - beta**t) / (1 - alpha**t)
    else:
        # No bias correction on momentums.
        r_adjusted = r
    # Apply Adam update rule.
    x_new = x - r_adjusted * (v_new / h_sqrt)
    # It's possible to apply regularization in the end.
    x_final = (1 - norm_coefficient_post) * x_new
    return x_final, v_new, h_new


class Adam(Base):
    @staticmethod
    def export_adam() -> None:
        # Define operator attributes.
        norm_coefficient = 0.001
        alpha = 0.95
        beta = 0.1
        epsilon = 1e-7

        # Create operator.
        node = onnx.helper.make_node(
            "Adam",
            inputs=["R", "T", "X", "G", "V", "H"],
            outputs=["X_new", "V_new", "H_new"],
            norm_coefficient=norm_coefficient,
            alpha=alpha,
            beta=beta,
            epsilon=epsilon,
            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
        )

        # Define operator inputs.
        r = np.array(0.1, dtype=np.float32)  # scalar
        t = np.array(0, dtype=np.int64)  # scalar
        x = np.array([1.2, 2.8], dtype=np.float32)
        g = np.array([-0.94, -2.5], dtype=np.float32)
        v = np.array([1.7, 3.6], dtype=np.float32)
        h = np.array([0.1, 0.1], dtype=np.float32)

        # Compute expected outputs of Adam.
        x_new, v_new, h_new = apply_adam(
            r, t, x, g, v, h, norm_coefficient, 0.0, alpha, beta, epsilon
        )

        # Check results.
        expect(
            node,
            inputs=[r, t, x, g, v, h],
            outputs=[x_new, v_new, h_new],
            name="test_adam",
            opset_imports=[
                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
            ],
        )

    @staticmethod
    def export_adam_multiple() -> None:
        # Define operator attributes.
        norm_coefficient = 0.001
        alpha = 0.95
        beta = 0.85
        epsilon = 1e-2

        node = onnx.helper.make_node(
            "Adam",
            inputs=["R", "T", "X1", "X2", "G1", "G2", "V1", "V2", "H1", "H2"],
            outputs=["X1_new", "X2_new", "V1_new", "V2_new", "H1_new", "H2_new"],
            norm_coefficient=norm_coefficient,
            alpha=alpha,
            beta=beta,
            domain=AI_ONNX_PREVIEW_TRAINING_DOMAIN,
        )

        # Define operator inputs.
        r = np.array(0.1, dtype=np.float32)  # scalar
        t = np.array(0, dtype=np.int64)  # scalar

        x1 = np.array([1.0], dtype=np.float32)
        g1 = np.array([-1.0], dtype=np.float32)
        v1 = np.array([2.0], dtype=np.float32)
        h1 = np.array([0.5], dtype=np.float32)

        x2 = np.array([1.0, 2.0], dtype=np.float32)
        g2 = np.array([-1.0, -3.0], dtype=np.float32)
        v2 = np.array([4.0, 1.0], dtype=np.float32)
        h2 = np.array([1.0, 10.0], dtype=np.float32)

        # Compute expected outputs of Adam.
        x1_new, v1_new, h1_new = apply_adam(
            r, t, x1, g1, v1, h1, norm_coefficient, 0.0, alpha, beta, epsilon
        )
        x2_new, v2_new, h2_new = apply_adam(
            r, t, x2, g2, v2, h2, norm_coefficient, 0.0, alpha, beta, epsilon
        )

        # Check results.
        expect(
            node,
            inputs=[r, t, x1, x2, g1, g2, v1, v2, h1, h2],
            outputs=[x1_new, x2_new, v1_new, v2_new, h1_new, h2_new],
            name="test_adam_multiple",
            opset_imports=[
                onnx.helper.make_opsetid(AI_ONNX_PREVIEW_TRAINING_DOMAIN, 1)
            ],
        )
