# Copyright (c) 2020 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import errno
import os
import pickle
import six

import paddle

from ppocr.utils.logging import get_logger

__all__ = ['load_model']


def _mkdir_if_not_exist(path, logger):
    """
    mkdir if not exists, ignore the exception when multiprocess mkdir together
    """
    if not os.path.exists(path):
        try:
            os.makedirs(path)
        except OSError as e:
            if e.errno == errno.EEXIST and os.path.isdir(path):
                logger.warning(
                    'be happy if some process has already created {}'.format(
                        path))
            else:
                raise OSError('Failed to mkdir {}'.format(path))


def load_model(config, model, optimizer=None):
    """
    load model from checkpoint or pretrained_model
    """
    logger = get_logger()
    global_config = config['Global']
    checkpoints = global_config.get('checkpoints')
    pretrained_model = global_config.get('pretrained_model')
    best_model_dict = {}
    if checkpoints:
        if checkpoints.endswith('pdparams'):
            checkpoints = checkpoints.replace('.pdparams', '')
        assert os.path.exists(checkpoints + ".pdparams"), \
        f"The {checkpoints}.pdparams does not exists!"
        
        # load params from trained model
        params = paddle.load(checkpoints + '.pdparams')
        state_dict = model.state_dict()
        new_state_dict = {}
        for key, value in state_dict.items():
            if key not in params:
                logger.warning(f"{key} not in loaded params {params.keys()} !")
            pre_value = params[key]
            if list(value.shape) == list(pre_value.shape):
                new_state_dict[key] = pre_value
            else:
                logger.warning(
                    f"The shape of model params {key} {value.shape} not matched with loaded params shape {pre_value.shape} !"
                )
        model.set_state_dict(new_state_dict)

        optim_dict = paddle.load(checkpoints + '.pdopt')
        if optimizer is not None:
            optimizer.set_state_dict(optim_dict)

        if os.path.exists(checkpoints + '.states'):
            with open(checkpoints + '.states', 'rb') as f:
                states_dict = pickle.load(f) if six.PY2 else pickle.load(
                    f, encoding='latin1')
            best_model_dict = states_dict.get('best_model_dict', {})
            if 'epoch' in states_dict:
                best_model_dict['start_epoch'] = states_dict['epoch'] + 1
        logger.info("resume from {}".format(checkpoints))
    elif pretrained_model:
        load_pretrained_params(model, pretrained_model)
    else:
        logger.info('train from scratch')
    return best_model_dict


def load_pretrained_params(model, path):
    logger = get_logger()
    if path.endswith('pdparams'):
        path = path.replace('.pdparams', '')
    assert os.path.exists(path + ".pdparams"), \
        f"The {path}.pdparams does not exists!"

    params = paddle.load(path + '.pdparams')
    state_dict = model.state_dict()
    new_state_dict = {}
    for k1, k2 in zip(state_dict.keys(), params.keys()):
        if list(state_dict[k1].shape) == list(params[k2].shape):
            new_state_dict[k1] = params[k2]
        else:
            logger.warning(
                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
            )
    model.set_state_dict(new_state_dict)
    logger.info(f"load pretrain successful from {path}")
    return model


def save_model(model,
               optimizer,
               model_path,
               logger,
               is_best=False,
               prefix='ppocr',
               **kwargs):
    """
    save model to the target path
    """
    _mkdir_if_not_exist(model_path, logger)
    model_prefix = os.path.join(model_path, prefix)
    paddle.save(model.state_dict(), model_prefix + '.pdparams')
    paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')

    # save metric and config
    with open(model_prefix + '.states', 'wb') as f:
        pickle.dump(kwargs, f, protocol=2)
    if is_best:
        logger.info('save best model is to {}'.format(model_prefix))
    else:
        logger.info("save model in {}".format(model_prefix))