# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
#
# 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 poptorch
import torch
import torch.nn as nn


def get_layer_ipu(layers_per_ipu):
    # List of the IPU Id for each encoder layer
    layer_ipu = []
    for ipu, n_layers in enumerate(layers_per_ipu):
        layer_ipu += [ipu] * n_layers
    return layer_ipu


def recomputation_checkpoint(module: nn.Module) -> torch.utils.hooks.RemovableHandle:
    """Annotates the output of a module to be checkpointed instead of
    recomputed"""

    def recompute_outputs(module, inputs, outputs):
        if type(outputs) is tuple:
            return tuple(poptorch.recomputationCheckpoint(y) for y in outputs)
        else:
            return poptorch.recomputationCheckpoint(outputs)

    return module.register_forward_hook(recompute_outputs)


def weight_init(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_normal_(m.weight)
        if m.bias is not None:
            nn.init.constant_(m.bias, 0)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)