# Copyright 2021 Samsung Electronics Co., Ltd.
#
# 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
import torch.nn.functional as F

from typing import Tuple

import copy

from . import measure
from ..p_utils import get_layer_metric_array

@measure('grad_norm', bn=True)
def get_grad_norm_arr(net, inputs, targets, loss_fn, split_data=1, skip_grad=False):
    net.zero_grad()
    N = inputs.shape[0]
    for sp in range(split_data):
        st=sp*N//split_data
        en=(sp+1)*N//split_data

        outputs = net.forward(inputs[st:en])
        # natsbench sss produces (activation, logits) tuple
        if isinstance(outputs, Tuple) and len(outputs) == 2:
            outputs = outputs[1]
        loss = loss_fn(outputs, targets[st:en])
        loss.backward()

        grad_norm_arr = get_layer_metric_array(net, lambda l: l.weight.grad.norm() if l.weight.grad is not None else torch.zeros_like(l.weight), mode='param')
        
    return grad_norm_arr
