# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
from typing import Dict, List


def accumulate_report_terms(
    info_accumulated: Dict[str, List[torch.Tensor]],
    report_terms: Dict[str, torch.Tensor],
) -> None:
    for k, v in report_terms.items():
        info_accumulated.setdefault(k, []).append(v)


def sum_or_zero(
    info_accumulated: Dict[str, List[torch.Tensor]], key: str, device: torch.device
) -> torch.Tensor:
    values = info_accumulated.get(key, [])
    if not values:
        return torch.tensor(0.0, device=device)
    return torch.sum(torch.stack(values))


def mean_or_zero(
    info_accumulated: Dict[str, List[torch.Tensor]], key: str, device: torch.device
) -> torch.Tensor:
    values = info_accumulated.get(key, [])
    if not values:
        return torch.tensor(0.0, device=device)
    return torch.mean(torch.stack(values))


def max_or_zero(
    info_accumulated: Dict[str, List[torch.Tensor]], key: str, device: torch.device
) -> torch.Tensor:
    values = info_accumulated.get(key, [])
    if not values:
        return torch.tensor(0.0, device=device)
    return torch.max(torch.stack(values))
