# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 os
import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..', '..', '..')))
sys.path.append(
    os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))

import argparse

import paddle
from paddle.jit import to_static

from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import load_model
from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser
from ppocr.metrics import build_metric
import tools.program as program
from paddleslim.dygraph.quant import QAT
from ppocr.data import build_dataloader


def export_single_model(quanter, model, infer_shape, save_path, logger):
    quanter.save_quantized_model(
        model,
        save_path,
        input_spec=[
            paddle.static.InputSpec(
                shape=[None] + infer_shape, dtype='float32')
        ])
    logger.info('inference QAT model is saved to {}'.format(save_path))


def main():
    ############################################################################################################
    # 1. quantization configs
    ############################################################################################################
    quant_config = {
        # weight preprocess type, default is None and no preprocessing is performed. 
        'weight_preprocess_type': None,
        # activation preprocess type, default is None and no preprocessing is performed.
        'activation_preprocess_type': None,
        # weight quantize type, default is 'channel_wise_abs_max'
        'weight_quantize_type': 'channel_wise_abs_max',
        # activation quantize type, default is 'moving_average_abs_max'
        'activation_quantize_type': 'moving_average_abs_max',
        # weight quantize bit num, default is 8
        'weight_bits': 8,
        # activation quantize bit num, default is 8
        'activation_bits': 8,
        # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
        'dtype': 'int8',
        # window size for 'range_abs_max' quantization. default is 10000
        'window_size': 10000,
        # The decay coefficient of moving average, default is 0.9
        'moving_rate': 0.9,
        # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
        'quantizable_layer_type': ['Conv2D', 'Linear'],
    }
    FLAGS = ArgsParser().parse_args()
    config = load_config(FLAGS.config)
    config = merge_config(config, FLAGS.opt)
    logger = get_logger()
    # build post process

    post_process_class = build_post_process(config['PostProcess'],
                                            config['Global'])

    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
        char_num = len(getattr(post_process_class, 'character'))
        if config['Architecture']["algorithm"] in ["Distillation",
                                                   ]:  # distillation model
            for key in config['Architecture']["Models"]:
                config['Architecture']["Models"][key]["Head"][
                    'out_channels'] = char_num
        else:  # base rec model
            config['Architecture']["Head"]['out_channels'] = char_num

    model = build_model(config['Architecture'])

    # get QAT model
    quanter = QAT(config=quant_config)
    quanter.quantize(model)

    load_model(config, model)
    model.eval()

    # build metric
    eval_class = build_metric(config['Metric'])

    # build dataloader
    valid_dataloader = build_dataloader(config, 'Eval', device, logger)

    use_srn = config['Architecture']['algorithm'] == "SRN"
    model_type = config['Architecture'].get('model_type', None)
    # start eval
    metric = program.eval(model, valid_dataloader, post_process_class,
                          eval_class, model_type, use_srn)

    logger.info('metric eval ***************')
    for k, v in metric.items():
        logger.info('{}:{}'.format(k, v))

    infer_shape = [3, 32, 100] if model_type == "rec" else [3, 640, 640]

    save_path = config["Global"]["save_inference_dir"]

    arch_config = config["Architecture"]
    if arch_config["algorithm"] in ["Distillation", ]:  # distillation model
        for idx, name in enumerate(model.model_name_list):
            model.model_list[idx].eval()
            sub_model_save_path = os.path.join(save_path, name, "inference")
            export_single_model(quanter, model.model_list[idx], infer_shape,
                                sub_model_save_path, logger)
    else:
        save_path = os.path.join(save_path, "inference")
        export_single_model(quanter, model, infer_shape, save_path, logger)


if __name__ == "__main__":
    config, device, logger, vdl_writer = program.preprocess()
    main()