# SPDX-FileCopyrightText: Copyright (c) 2025 The Self-Forcing Authors. 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
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import numpy as np
import random
import torch


def set_seed(seed: int, deterministic: bool = False):
    """
    Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`.

    Args:
        seed (`int`):
            The seed to set.
        deterministic (`bool`, *optional*, defaults to `False`):
            Whether to use deterministic algorithms where available. Can slow down training.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    if deterministic:
        torch.use_deterministic_algorithms(True)


def merge_dict_list(dict_list):
    if len(dict_list) == 1:
        return dict_list[0]

    merged_dict = {}
    for k, v in dict_list[0].items():
        if isinstance(v, torch.Tensor):
            if v.ndim == 0:
                merged_dict[k] = torch.stack([d[k] for d in dict_list], dim=0)
            else:
                merged_dict[k] = torch.cat([d[k] for d in dict_list], dim=0)
        else:
            # for non-tensor values, we just copy the value from the first item
            merged_dict[k] = v
    return merged_dict