# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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.
from typing import Union

import numpy as np
import torch


def gen_suffix(rank, use_smooth_quant, quant_per_channel):
    suffix = f"{rank}.bin"
    if use_smooth_quant:
        sq_prefix = "int8."
        if quant_per_channel:
            sq_prefix += "col."
        suffix = sq_prefix + suffix
    return suffix


def extract_layer_idx(name):
    ss = name.split('.')
    for s in ss:
        if s.isdigit():
            return s
    return None


def split(v: Union[np.ndarray, torch.Tensor],
          tp_size: int,
          tp_rank: int,
          dim=0):
    if tp_size == 1:
        return v
    assert len(v.shape) > 1 or dim == 0
    if isinstance(v, np.ndarray):
        return np.ascontiguousarray(
            np.split(v, tp_size, axis=dim)[tp_rank].copy())
    else:
        assert v.shape[dim] % tp_size == 0, \
            'Unable to split: shape={v.shape} (dim={dim}) tp_size={tp_size}.'
        split_size = v.shape[dim] // tp_size
        return v.split(split_size, dim=dim)[tp_rank].clone().detach()


def dup_kv_weight(v, num_head, tp_size):
    assert tp_size % num_head == 0
    reps = tp_size // num_head
    head_size = v.shape[0] // num_head
    v = v.reshape(num_head, head_size,
                  -1)[:, None, :, :].expand(num_head, reps, head_size,
                                            v.shape[1])
    return v.reshape(num_head * reps * head_size, -1).clone().detach()