# Copyright (c) 2021-2022, NVIDIA Corporation & Affiliates. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://github.com/NVlabs/FAN/blob/main/LICENSE

# Copyright (c) [2012]-[2021] Shanghai Yitu Technology Co., Ltd.
#
# This source code is licensed under the Clear BSD License
# LICENSE file in the root directory of this file
# All rights reserved.

# Modified by: Daquan Zhou

'''
- 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.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''

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]
    if True:
        posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]  # posemb_tok is for cls token, posemb_grid for the following tokens
        ntok_new -= 1
    else:
        posemb_tok, posemb_grid = posemb[:, :0], 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]
    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)   # [1, 24*24+1, dim]
    return posemb

def resize_pos_embed_cait(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_nocls(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, no_pos_embed=False):
    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)
        if not no_pos_embed:
            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_cait(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_nocls(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_for_transfer_learning(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 load_for_probing(model, checkpoint_path, use_ema=False, strict=False, num_classes=19167):
    state_dict = load_state_dict(checkpoint_path, model, use_ema, num_classes=19167, no_pos_embed=True)
    info=model.load_state_dict(state_dict, strict=strict)
    print(info)

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)

