"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

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
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"""
# Adapted for token labeling

'''
- resize_pos_embed: resize position embedding
- load_for_transfer_learning: load pretrained paramters to model in transfer learning
- get_mean_and_std: calculate the mean and std value of dataset.
'''

import os
import sys
import time
import torch
import math

import torch.nn as nn
import torch.nn.init as init
import logging
import os
from collections import OrderedDict
import torch.nn.functional as F

_logger = logging.getLogger(__name__)

def resize_pos_embed(posemb, posemb_new): # example: 224:(14x14+1)-> 384: (24x24+1)
    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
    ntok_new = posemb_new.shape[1]

    posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]  # posemb_tok is for cls token, posemb_grid for the following tokens
    ntok_new -= 1
    gs_old = int(math.sqrt(len(posemb_grid)))     # 14
    gs_new = int(math.sqrt(ntok_new))             # 24
    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)  # [1, 196, dim]->[1, 14, 14, dim]->[1, dim, 14, 14]
    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bicubic') # [1, dim, 14, 14] -> [1, dim, 24, 24]
    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)   # [1, dim, 24, 24] -> [1, 24*24, dim]
    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)   # [1, 24*24+1, dim]
    return posemb

def resize_pos_embed_without_cls(posemb, posemb_new): # example: 224:(14x14+1)-> 384: (24x24+1)
    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
    ntok_new = posemb_new.shape[1]
    posemb_grid = posemb[0]
    gs_old = int(math.sqrt(len(posemb_grid)))     # 14
    gs_new = int(math.sqrt(ntok_new))             # 24
    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)  # [1, 196, dim]->[1, 14, 14, dim]->[1, dim, 14, 14]
    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bicubic') # [1, dim, 14, 14] -> [1, dim, 24, 24]
    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1)   # [1, dim, 24, 24] -> [1, 24*24, dim]
    return posemb_grid


def resize_pos_embed_4d(posemb, posemb_new): # example: 224:(14x14+1)-> 384: (24x24+1)
    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
    gs_old = posemb.shape[1]     # 14
    gs_new = posemb_new.shape[1]             # 24
    _logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
    posemb_grid = posemb
    posemb_grid = posemb_grid.permute(0, 3, 1, 2)  # [1, 14, 14, dim]->[1, dim, 14, 14]
    posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bicubic') # [1, dim, 14, 14] -> [1, dim, 24, 24]
    posemb_grid = posemb_grid.permute(0, 2, 3, 1)   # [1, dim, 24, 24]->[1, 24, 24, dim]
    return posemb_grid


def load_state_dict(checkpoint_path,model, use_ema=False, num_classes=1000):
    if checkpoint_path and os.path.isfile(checkpoint_path):
        checkpoint = torch.load(checkpoint_path, map_location='cpu')
        state_dict_key = 'state_dict'
        if isinstance(checkpoint, dict):
            if use_ema and 'state_dict_ema' in checkpoint:
                state_dict_key = 'state_dict_ema'
        if state_dict_key and state_dict_key in checkpoint:
            new_state_dict = OrderedDict()
            for k, v in checkpoint[state_dict_key].items():
                # strip `module.` prefix
                name = k[7:] if k.startswith('module') else k
                new_state_dict[name] = v
            state_dict = new_state_dict
        else:
            state_dict = checkpoint
        _logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
        if num_classes != 1000:
            # completely discard fully connected for all other differences between pretrained and created model
            del state_dict['head' + '.weight']
            del state_dict['head' + '.bias']
            old_aux_head_weight = state_dict.pop('aux_head.weight', None)
            old_aux_head_bias = state_dict.pop('aux_head.bias', None)


        old_posemb = state_dict['pos_embed']
        if model.pos_embed.shape != old_posemb.shape:  # need resize the position embedding by interpolate
            if len(old_posemb.shape)==3:
                if int(math.sqrt(old_posemb.shape[1]))**2==old_posemb.shape[1]:
                    new_posemb = resize_pos_embed_without_cls(old_posemb, model.pos_embed)
                else:
                    new_posemb = resize_pos_embed(old_posemb, model.pos_embed)
            elif len(old_posemb.shape)==4:
                new_posemb = resize_pos_embed_4d(old_posemb, model.pos_embed)
            state_dict['pos_embed'] = new_posemb

        return state_dict
    else:
        _logger.error("No checkpoint found at '{}'".format(checkpoint_path))
        raise FileNotFoundError()


def load_pretrained_weights(model, checkpoint_path, use_ema=False, strict=True, num_classes=1000):
    state_dict = load_state_dict(checkpoint_path, model, use_ema, num_classes)
    model.load_state_dict(state_dict, strict=strict)


def get_mean_and_std(dataset):
    '''Compute the mean and std value of dataset.'''
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)
    mean = torch.zeros(3)
    std = torch.zeros(3)
    print('==> Computing mean and std..')
    for inputs, targets in dataloader:
        for i in range(3):
            mean[i] += inputs[:,i,:,:].mean()
            std[i] += inputs[:,i,:,:].std()
    mean.div_(len(dataset))
    std.div_(len(dataset))
    return mean, std

def init_params(net):
    '''Init layer parameters.'''
    for m in net.modules():
        if isinstance(m, nn.Conv2d):
            init.kaiming_normal(m.weight, mode='fan_out')
            if m.bias:
                init.constant(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            init.constant(m.weight, 1)
            init.constant(m.bias, 0)
        elif isinstance(m, nn.Linear):
            init.normal(m.weight, std=1e-3)
            if m.bias:
                init.constant(m.bias, 0)