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# 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 omegaconf import OmegaConf
from typing import List
def load_and_merge_configs(config_paths: List[str]):
"""
Load and merge multiple OmegaConf configs in order.
Later configs override earlier ones.
Any missing keys in later configs are added to the schema as None.
Args:
config_paths (List[str]): List of paths to config files.
The first config acts as the base schema.
Returns:
OmegaConf.DictConfig: The merged configuration.
"""
if not config_paths:
raise ValueError("No config paths provided.")
# Start with the first config as schema
schema = OmegaConf.load(config_paths[0])
# Iteratively merge the rest
for path in config_paths[1:]:
cfg = OmegaConf.load(path)
# Add missing keys into schema
missing_keys = set(cfg.keys()) - set(schema.keys())
for key in missing_keys:
OmegaConf.update(schema, key, None, force_add=True)
# Merge current config into schema
schema = OmegaConf.merge(schema, cfg)
return schema
def seed_everything(seed: int):
import random, os
import numpy as np
import torch
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = True
dtype_map = {
'float32': torch.float32,
'float': torch.float32,
'float64': torch.float64,
'double': torch.float64,
'float16': torch.float16,
'half': torch.float16,
'bfloat16': torch.bfloat16,
'int32': torch.int32,
'int64': torch.int64,
'long': torch.int64,
}
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