import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module

import torch
import torchvision
from torch.utils import model_zoo

import mmcv
from .dist_utils import get_dist_info

open_mmlab_model_urls = {
    'vgg16_caffe': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/vgg16_caffe-292e1171.pth',  # noqa: E501
    'resnet50_caffe': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet50_caffe-788b5fa3.pth',  # noqa: E501
    'resnet101_caffe': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet101_caffe-3ad79236.pth',  # noqa: E501
    'resnext50_32x4d': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext50-32x4d-0ab1a123.pth',  # noqa: E501
    'resnext101_32x4d': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext101_32x4d-a5af3160.pth',  # noqa: E501
    'resnext101_64x4d': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext101_64x4d-ee2c6f71.pth',  # noqa: E501
    'contrib/resnet50_gn': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet50_gn_thangvubk-ad1730dd.pth',  # noqa: E501
    'detectron/resnet50_gn': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet50_gn-9186a21c.pth',  # noqa: E501
    'detectron/resnet101_gn': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet101_gn-cac0ab98.pth',  # noqa: E501
    'jhu/resnet50_gn_ws': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet50_gn_ws-15beedd8.pth',  # noqa: E501
    'jhu/resnet101_gn_ws': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnet101_gn_ws-3e3c308c.pth',  # noqa: E501
    'jhu/resnext50_32x4d_gn_ws': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext50_32x4d_gn_ws-0d87ac85.pth',  # noqa: E501
    'jhu/resnext101_32x4d_gn_ws': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext101_32x4d_gn_ws-34ac1a9e.pth',  # noqa: E501
    'jhu/resnext50_32x4d_gn': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext50_32x4d_gn-c7e8b754.pth',  # noqa: E501
    'jhu/resnext101_32x4d_gn': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/resnext101_32x4d_gn-ac3bb84e.pth',  # noqa: E501
    'msra/hrnetv2_w18': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/hrnetv2_w18-00eb2006.pth',  # noqa: E501
    'msra/hrnetv2_w32': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/hrnetv2_w32-dc9eeb4f.pth',  # noqa: E501
    'msra/hrnetv2_w40': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/hrnetv2_w40-ed0b031c.pth',  # noqa: E501
    'bninception_caffe': 'https://open-mmlab.s3.ap-northeast-2.amazonaws.com/pretrain/third_party/bn_inception_caffe-ed2e8665.pth',  # noqa: E501
    'kin400/i3d_r50_f32s2_k400': 'https://open-mmlab.s3.ap-northeast-2.amazonaws.com/pretrain/third_party/i3d_r50_f32s2_k400-2c57e077.pth',  # noqa: E501
    'kin400/nl3d_r50_f32s2_k400': 'https://open-mmlab.s3.ap-northeast-2.amazonaws.com/pretrain/third_party/nl3d_r50_f32s2_k400-fa7e7caa.pth',  # noqa: E501
}  # yapf: disable


def load_state_dict(module, state_dict, strict=False, logger=None):
    """Load state_dict to a module.

    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
    Default value for ``strict`` is set to ``False`` and the message for
    param mismatch will be shown even if strict is False.

    Args:
        module (Module): Module that receives the state_dict.
        state_dict (OrderedDict): Weights.
        strict (bool): whether to strictly enforce that the keys
            in :attr:`state_dict` match the keys returned by this module's
            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
        logger (:obj:`logging.Logger`, optional): Logger to log the error
            message. If not specified, print function will be used.
    """
    unexpected_keys = []
    all_missing_keys = []
    err_msg = []

    metadata = getattr(state_dict, '_metadata', None)
    state_dict = state_dict.copy()
    if metadata is not None:
        state_dict._metadata = metadata

    # use _load_from_state_dict to enable checkpoint version control
    def load(module, prefix=''):
        local_metadata = {} if metadata is None else metadata.get(
            prefix[:-1], {})
        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
                                     all_missing_keys, unexpected_keys,
                                     err_msg)
        for name, child in module._modules.items():
            if child is not None:
                load(child, prefix + name + '.')

    load(module)
    load = None  # break load->load reference cycle

    # ignore "num_batches_tracked" of BN layers
    missing_keys = [
        key for key in all_missing_keys if 'num_batches_tracked' not in key
    ]

    if unexpected_keys:
        err_msg.append('unexpected key in source state_dict: {}\n'.format(
            ', '.join(unexpected_keys)))
    if missing_keys:
        err_msg.append('missing keys in source state_dict: {}\n'.format(
            ', '.join(missing_keys)))

    rank, _ = get_dist_info()
    if len(err_msg) > 0 and rank == 0:
        err_msg.insert(
            0, 'The model and loaded state dict do not match exactly\n')
        err_msg = '\n'.join(err_msg)
        if strict:
            raise RuntimeError(err_msg)
        elif logger is not None:
            logger.warning(err_msg)
        else:
            print(err_msg)


def load_url_dist(url):
    """ In distributed setting, this function only download checkpoint at
    local rank 0 """
    rank, world_size = get_dist_info()
    rank = int(os.environ.get('LOCAL_RANK', rank))
    if rank == 0:
        checkpoint = model_zoo.load_url(url)
    if world_size > 1:
        torch.distributed.barrier()
        if rank > 0:
            checkpoint = model_zoo.load_url(url)
    return checkpoint


def get_torchvision_models():
    model_urls = dict()
    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
        if ispkg:
            continue
        _zoo = import_module('torchvision.models.{}'.format(name))
        if hasattr(_zoo, 'model_urls'):
            _urls = getattr(_zoo, 'model_urls')
            model_urls.update(_urls)
    return model_urls


def load_checkpoint(model,
                    filename,
                    map_location=None,
                    strict=False,
                    logger=None):
    """Load checkpoint from a file or URI.

    Args:
        model (Module): Module to load checkpoint.
        filename (str): Either a filepath or URL or modelzoo://xxxxxxx.
        map_location (str): Same as :func:`torch.load`.
        strict (bool): Whether to allow different params for the model and
            checkpoint.
        logger (:mod:`logging.Logger` or None): The logger for error message.

    Returns:
        dict or OrderedDict: The loaded checkpoint.
    """
    # load checkpoint from modelzoo or file or url
    if filename.startswith('modelzoo://'):
        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
                      'use "torchvision://" instead')
        model_urls = get_torchvision_models()
        model_name = filename[11:]
        checkpoint = load_url_dist(model_urls[model_name])
    elif filename.startswith('torchvision://'):
        model_urls = get_torchvision_models()
        model_name = filename[14:]
        checkpoint = load_url_dist(model_urls[model_name])
    elif filename.startswith('open-mmlab://'):
        model_name = filename[13:]
        checkpoint = load_url_dist(open_mmlab_model_urls[model_name])
    elif filename.startswith(('http://', 'https://')):
        checkpoint = load_url_dist(filename)
    else:
        if not osp.isfile(filename):
            raise IOError('{} is not a checkpoint file'.format(filename))
        checkpoint = torch.load(filename, map_location=map_location)
    # get state_dict from checkpoint
    if isinstance(checkpoint, OrderedDict):
        state_dict = checkpoint
    elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
        state_dict = checkpoint['state_dict']
    else:
        raise RuntimeError(
            'No state_dict found in checkpoint file {}'.format(filename))
    # strip prefix of state_dict
    if list(state_dict.keys())[0].startswith('module.'):
        state_dict = {k[7:]: v for k, v in checkpoint['state_dict'].items()}
    # load state_dict
    if hasattr(model, 'module'):
        load_state_dict(model.module, state_dict, strict, logger)
    else:
        load_state_dict(model, state_dict, strict, logger)
    return checkpoint


def weights_to_cpu(state_dict):
    """Copy a model state_dict to cpu.

    Args:
        state_dict (OrderedDict): Model weights on GPU.

    Returns:
        OrderedDict: Model weights on GPU.
    """
    state_dict_cpu = OrderedDict()
    for key, val in state_dict.items():
        state_dict_cpu[key] = val.cpu()
    return state_dict_cpu


def save_checkpoint(model, filename, optimizer=None, meta=None):
    """Save checkpoint to file.

    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
    ``optimizer``. By default ``meta`` will contain version and time info.

    Args:
        model (Module): Module whose params are to be saved.
        filename (str): Checkpoint filename.
        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
        meta (dict, optional): Metadata to be saved in checkpoint.
    """
    if meta is None:
        meta = {}
    elif not isinstance(meta, dict):
        raise TypeError('meta must be a dict or None, but got {}'.format(
            type(meta)))
    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())

    mmcv.mkdir_or_exist(osp.dirname(filename))
    if hasattr(model, 'module'):
        model = model.module

    checkpoint = {
        'meta': meta,
        'state_dict': weights_to_cpu(model.state_dict())
    }
    if optimizer is not None:
        checkpoint['optimizer'] = optimizer.state_dict()

    torch.save(checkpoint, filename)
