#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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 numpy as np
import paddle
from paddle.fluid import core

__all__ = ['get_prune_params_config', 'prune_params']


def get_prune_params_config(graph, origin_model_config):
    param_config = {}
    precedor = None
    for op in graph.ops():
        ### TODO(ceci3):
        ### 1. fix config when this op is concat by graph.pre_ops(op)
        ### 2. add kernel_size in config
        ### 3. add channel in config
        for inp in op.all_inputs():
            n_ops = graph.next_ops(op)
            if inp._var.name in origin_model_config.keys():
                if 'expand_ratio' in origin_model_config[inp._var.name].keys():
                    tmp = origin_model_config[inp._var.name]['expand_ratio']
                    if len(inp._var.shape) > 1:
                        if inp._var.name in param_config.keys():
                            param_config[inp._var.name].append(tmp)
                        ### first op
                        else:
                            param_config[inp._var.name] = [precedor, tmp]
                    else:
                        param_config[inp._var.name] = [tmp]
                    precedor = tmp
                else:
                    precedor = None
            for n_op in n_ops:
                for next_inp in n_op.all_inputs():
                    if next_inp._var.persistable == True:
                        if next_inp._var.name in origin_model_config.keys():
                            if 'expand_ratio' in origin_model_config[
                                    next_inp._var.name].keys():
                                tmp = origin_model_config[next_inp._var.name][
                                    'expand_ratio']
                                pre = tmp if precedor is None else precedor
                                if len(next_inp._var.shape) > 1:
                                    param_config[next_inp._var.name] = [pre]
                                else:
                                    param_config[next_inp._var.name] = [tmp]
                            else:
                                if len(next_inp._var.
                                       shape) > 1 and precedor != None:
                                    param_config[
                                        next_inp._var.name] = [precedor, None]
                        else:
                            param_config[next_inp._var.name] = [precedor]

    return param_config


def prune_params(model, param_config, super_model_sd=None):
    for name, param in model.named_parameters():
        t_value = param.value().get_tensor()
        value = np.array(t_value).astype("float32")

        if super_model_sd != None:
            super_t_value = super_model_sd[name].value().get_tensor()
            super_value = np.array(super_t_value).astype("float32")

        if param.name in param_config.keys():
            if len(param_config[param.name]) > 1:
                in_exp = param_config[param.name][0]
                out_exp = param_config[param.name][1]
                in_chn = int(value.shape[0]) if in_exp == None else int(
                    value.shape[0] * in_exp)
                out_chn = int(value.shape[1]) if out_exp == None else int(
                    value.shape[1] * out_exp)
                prune_value = super_value[:in_chn, :out_chn, ...] \
                                 if super_model_sd != None else value[:in_chn, :out_chn, ...]
            else:
                out_chn = int(value.shape[0]) if param_config[param.name][
                    0] == None else int(value.shape[0] *
                                        param_config[param.name][0])
                prune_value = super_value[:out_chn, ...] \
                                 if super_model_sd != None else value[:out_chn, ...]
        else:
            prune_value = super_value if super_model_sd != None else value

        p = t_value._place()
        if p.is_cpu_place():
            place = core.CPUPlace()
        elif p.is_cuda_pinned_place():
            place = core.CUDAPinnedPlace()
        else:
            place = core.CUDAPlace(p.gpu_device_id())
        t_value.set(prune_value, place)
        if param.trainable:
            param.clear_gradient()