#   copyright (c) 2021 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 unittest
import os
import time
import sys
import random
import math
import functools
import contextlib
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.dataset.common import download
from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization

paddle.enable_static()

random.seed(0)
np.random.seed(0)


class TestPostTrainingQuantization(unittest.TestCase):

    def setUp(self):
        self.download_path = 'int8/download'
        self.cache_folder = os.path.expanduser('~/.cache/paddle/dataset/' +
                                               self.download_path)
        self.timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
        self.int8_model_path = os.path.join(os.getcwd(),
                                            "post_training_" + self.timestamp)
        try:
            os.system("mkdir -p " + self.int8_model_path)
        except Exception as e:
            print("Failed to create {} due to {}".format(
                self.int8_model_path, str(e)))
            sys.exit(-1)

    def tearDown(self):
        try:
            os.system("rm -rf {}".format(self.int8_model_path))
        except Exception as e:
            print("Failed to delete {} due to {}".format(
                self.int8_model_path, str(e)))

    def cache_unzipping(self, target_folder, zip_path):
        cmd = 'tar xf {0} -C {1}'.format(zip_path, target_folder)
        os.system(cmd)

    def download_model(self, data_url, data_md5, folder_name):
        download(data_url, self.download_path, data_md5)
        file_name = data_url.split('/')[-1]
        zip_path = os.path.join(self.cache_folder, file_name)
        print('Data is downloaded at {0}'.format(zip_path))

        data_cache_folder = os.path.join(self.cache_folder, folder_name)
        self.cache_unzipping(self.cache_folder, zip_path)
        return data_cache_folder

    def run_program(self, model_path, batch_size, infer_iterations):
        print("test model path:" + model_path)
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        [infer_program, feed_dict, fetch_targets] = \
            fluid.io.load_inference_model(model_path,
                    model_filename='model.pdmodel',
                    params_filename='model.pdiparams', executor=exe)
        val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size)

        img_shape = [1, 28, 28]
        test_info = []
        cnt = 0
        periods = []
        for batch_id, data in enumerate(val_reader()):
            image = np.array([x[0].reshape(img_shape)
                              for x in data]).astype("float32")
            input_label = np.array([x[1] for x in data]).astype("int64")

            t1 = time.time()
            out = exe.run(infer_program,
                          feed={feed_dict[0]: image},
                          fetch_list=fetch_targets)
            t2 = time.time()
            period = t2 - t1
            periods.append(period)

            out_label = np.argmax(np.array(out[0]), axis=1)
            top1_num = sum(input_label == out_label)
            test_info.append(top1_num)
            cnt += len(data)

            if (batch_id + 1) == infer_iterations:
                break

        throughput = cnt / np.sum(periods)
        latency = np.average(periods)
        acc1 = np.sum(test_info) / cnt
        return (throughput, latency, acc1)

    def generate_quantized_model(self,
                                 model_path,
                                 algo="KL",
                                 quantizable_op_type=["conv2d"],
                                 is_full_quantize=False,
                                 is_use_cache_file=False,
                                 is_optimize_model=False,
                                 batch_size=10,
                                 batch_nums=10,
                                 is_data_loader=False):

        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        scope = fluid.global_scope()
        val_reader = paddle.dataset.mnist.train()

        def val_data_generator():
            batches = []
            for data in val_reader():
                batches.append(data[0].reshape(1, 28, 28))
                if len(batches) == batch_size:
                    batches = np.asarray(batches)
                    yield {"x": batches}
                    batches = []

        ptq = PostTrainingQuantization(
            executor=exe,
            model_dir=model_path,
            model_filename='model.pdmodel',
            params_filename='model.pdiparams',
            sample_generator=val_reader if not is_data_loader else None,
            data_loader=val_data_generator if is_data_loader else None,
            batch_size=batch_size,
            batch_nums=batch_nums,
            algo=algo,
            quantizable_op_type=quantizable_op_type,
            is_full_quantize=is_full_quantize,
            optimize_model=is_optimize_model,
            is_use_cache_file=is_use_cache_file)
        ptq.quantize()
        ptq.save_quantized_model(self.int8_model_path,
                                 model_filename='model.pdmodel',
                                 params_filename='model.pdiparams')

    def run_test(self,
                 model_name,
                 data_url,
                 data_md5,
                 algo,
                 quantizable_op_type,
                 is_full_quantize,
                 is_use_cache_file,
                 is_optimize_model,
                 diff_threshold,
                 batch_size=10,
                 infer_iterations=10,
                 quant_iterations=5,
                 is_data_loader=False):

        origin_model_path = self.download_model(data_url, data_md5, model_name)
        #origin_model_path = os.path.join(origin_model_path, model_name)

        print("Start FP32 inference for {0} on {1} images ...".format(
            model_name, infer_iterations * batch_size))
        (fp32_throughput, fp32_latency,
         fp32_acc1) = self.run_program(origin_model_path, batch_size,
                                       infer_iterations)

        print("Start INT8 post training quantization for {0} on {1} images ...".
              format(model_name, quant_iterations * batch_size))
        self.generate_quantized_model(origin_model_path,
                                      algo,
                                      quantizable_op_type,
                                      is_full_quantize,
                                      is_use_cache_file,
                                      is_optimize_model,
                                      batch_size,
                                      quant_iterations,
                                      is_data_loader=is_data_loader)

        print("Start INT8 inference for {0} on {1} images ...".format(
            model_name, infer_iterations * batch_size))
        (int8_throughput, int8_latency,
         int8_acc1) = self.run_program(self.int8_model_path, batch_size,
                                       infer_iterations)

        print("---Post training quantization of {} method---".format(algo))
        print(
            "FP32 {0}: batch_size {1}, throughput {2} img/s, latency {3} s, acc1 {4}."
            .format(model_name, batch_size, fp32_throughput, fp32_latency,
                    fp32_acc1))
        print(
            "INT8 {0}: batch_size {1}, throughput {2} img/s, latency {3} s, acc1 {4}.\n"
            .format(model_name, batch_size, int8_throughput, int8_latency,
                    int8_acc1))
        sys.stdout.flush()

        delta_value = fp32_acc1 - int8_acc1
        self.assertLess(delta_value, diff_threshold)


class TestPostTrainingKLForWhile(TestPostTrainingQuantization):

    def test_post_training_kl(self):
        model_name = "mnist_while"
        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_while.tar.gz"
        data_md5 = "2387390beeb37b51dec041c27b8a681f"
        algo = "KL"
        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
        is_full_quantize = False
        is_use_cache_file = False
        is_optimize_model = True
        diff_threshold = 0.01
        batch_size = 10
        infer_iterations = 50
        quant_iterations = 5
        self.run_test(model_name, data_url, data_md5, algo, quantizable_op_type,
                      is_full_quantize, is_use_cache_file, is_optimize_model,
                      diff_threshold, batch_size, infer_iterations,
                      quant_iterations)


class TestPostTraininghistForWhile(TestPostTrainingQuantization):

    def test_post_training_hist(self):
        model_name = "mnist_while"
        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_while.tar.gz"
        data_md5 = "2387390beeb37b51dec041c27b8a681f"
        algo = "hist"
        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
        is_full_quantize = False
        is_use_cache_file = False
        is_optimize_model = True
        diff_threshold = 0.01
        batch_size = 10
        infer_iterations = 50
        quant_iterations = 5
        self.run_test(model_name, data_url, data_md5, algo, quantizable_op_type,
                      is_full_quantize, is_use_cache_file, is_optimize_model,
                      diff_threshold, batch_size, infer_iterations,
                      quant_iterations)


class TestPostTrainingmseForWhile(TestPostTrainingQuantization):

    def test_post_training_mse(self):
        model_name = "mnist_while"
        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_while.tar.gz"
        data_md5 = "2387390beeb37b51dec041c27b8a681f"
        algo = "mse"
        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
        is_full_quantize = False
        is_use_cache_file = False
        is_optimize_model = True
        diff_threshold = 0.01
        batch_size = 10
        infer_iterations = 50
        quant_iterations = 5
        self.run_test(model_name, data_url, data_md5, algo, quantizable_op_type,
                      is_full_quantize, is_use_cache_file, is_optimize_model,
                      diff_threshold, batch_size, infer_iterations,
                      quant_iterations)


class TestPostTrainingavgForWhile(TestPostTrainingQuantization):

    def test_post_training_avg(self):
        model_name = "mnist_while"
        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_while.tar.gz"
        data_md5 = "2387390beeb37b51dec041c27b8a681f"
        algo = "avg"
        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
        is_full_quantize = False
        is_use_cache_file = False
        is_optimize_model = True
        diff_threshold = 0.01
        batch_size = 10
        infer_iterations = 50
        quant_iterations = 5
        self.run_test(model_name, data_url, data_md5, algo, quantizable_op_type,
                      is_full_quantize, is_use_cache_file, is_optimize_model,
                      diff_threshold, batch_size, infer_iterations,
                      quant_iterations)


class TestPostTrainingMinMaxForWhile(TestPostTrainingQuantization):

    def test_post_training_min_max(self):
        model_name = "mnist_while"
        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_while.tar.gz"
        data_md5 = "2387390beeb37b51dec041c27b8a681f"
        algo = "min_max"
        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
        is_full_quantize = False
        is_use_cache_file = False
        is_optimize_model = True
        diff_threshold = 0.01
        batch_size = 10
        infer_iterations = 50
        quant_iterations = 5
        self.run_test(model_name, data_url, data_md5, algo, quantizable_op_type,
                      is_full_quantize, is_use_cache_file, is_optimize_model,
                      diff_threshold, batch_size, infer_iterations,
                      quant_iterations)


class TestPostTrainingAbsMaxForWhile(TestPostTrainingQuantization):

    def test_post_training_abs_max(self):
        model_name = "mnist_while"
        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_while.tar.gz"
        data_md5 = "2387390beeb37b51dec041c27b8a681f"
        algo = "abs_max"
        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
        is_full_quantize = False
        is_use_cache_file = False
        is_optimize_model = True
        diff_threshold = 0.01
        batch_size = 10
        infer_iterations = 50
        quant_iterations = 5
        self.run_test(model_name, data_url, data_md5, algo, quantizable_op_type,
                      is_full_quantize, is_use_cache_file, is_optimize_model,
                      diff_threshold, batch_size, infer_iterations,
                      quant_iterations)
        self.run_test(model_name,
                      data_url,
                      data_md5,
                      algo,
                      quantizable_op_type,
                      is_full_quantize,
                      is_use_cache_file,
                      is_optimize_model,
                      diff_threshold,
                      batch_size,
                      infer_iterations,
                      quant_iterations,
                      is_data_loader=True)


if __name__ == '__main__':
    unittest.main()
