#!/usr/bin/env python3

# Copyright (c) 2021 CINN 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 sys
import unittest

import cinn
import numpy as np
from cinn import Target, ir, lang, runtime
from cinn.common import *
from cinn.framework import *
from cinn.frontend import *

import paddle
from paddle import fluid, static

assert len(sys.argv) == 3
enable_gpu = sys.argv.pop()
naive_model_dir = sys.argv.pop()


class TestNetBuilder(unittest.TestCase):
    def setUp(self):
        if enable_gpu == "ON":
            self.target = DefaultNVGPUTarget()
        else:
            self.target = DefaultHostTarget()

    def get_paddle_result(self, inputdata):
        paddle.enable_static()

        a = static.data(name='A', shape=[24, 56, 56], dtype='float32')
        b = static.data(name='B', shape=[24, 56, 56], dtype='float32')
        c = paddle.add(a, b)
        d = paddle.nn.initializer.NumpyArrayInitializer(
            np.array(inputdata[2]).reshape((144, 24, 1, 1)).astype('float32')
        )
        res = static.nn.conv2d(
            input=c,
            num_filters=144,
            filter_size=1,
            stride=1,
            padding=0,
            dilation=1,
            param_attr=d,
        )

        exe = static.Executor(paddle.CPUPlace())
        exe.run(static.default_startup_program())

        x = np.array(inputdata[0]).reshape((1, 24, 56, 56)).astype("float32")
        y = np.array(inputdata[1]).reshape((1, 24, 56, 56)).astype("float32")
        output = exe.run(feed={"A": x, "B": y}, fetch_list=[res])
        return np.array(output)

    def test_build_and_compile(self):
        builder = NetBuilder("test_basic")
        a = builder.create_input(Float(32), (1, 24, 56, 56), "A")
        b = builder.create_input(Float(32), (1, 24, 56, 56), "B")
        c = builder.add(a, b)
        d = builder.create_input(Float(32), (144, 24, 1, 1), "D")
        e = builder.conv(c, d)

        computation = Computation.build_and_compile(self.target, builder)

        A_data = np.random.random([1, 24, 56, 56]).astype("float32")
        B_data = np.random.random([1, 24, 56, 56]).astype("float32")
        D_data = np.random.random([144, 24, 1, 1]).astype("float32")

        computation.get_tensor("A").from_numpy(A_data, self.target)
        computation.get_tensor("B").from_numpy(B_data, self.target)
        computation.get_tensor("D").from_numpy(D_data, self.target)

        computation.execute()

        e_tensor = computation.get_tensor(str(e))
        edata_cinn = e_tensor.numpy(self.target)

        edata_paddle = self.get_paddle_result([A_data, B_data, D_data])

        np.testing.assert_allclose(edata_cinn, edata_paddle, atol=1e-5)


class TestCompilePaddleModel(unittest.TestCase):
    def setUp(self):
        if enable_gpu == "ON":
            self.target = DefaultNVGPUTarget()
        else:
            self.target = DefaultHostTarget()

    def test_compile_paddle_model(self):
        A_shape = [4, 30]
        A_data = np.random.random(A_shape).astype("float32")
        computation = Computation.compile_paddle_model(
            self.target, naive_model_dir, ["A"], [A_shape], False
        )

        A_tensor = computation.get_tensor("A")
        A_tensor.from_numpy(A_data, self.target)

        computation.execute()

        out = computation.get_tensor("fc_0.tmp_2")
        res_cinn = out.numpy(self.target)

        config = fluid.core.AnalysisConfig(naive_model_dir)
        config.disable_gpu()
        config.switch_ir_optim(False)
        paddle_predictor = fluid.core.create_paddle_predictor(config)
        data = fluid.core.PaddleTensor(A_data)
        paddle_out = paddle_predictor.run([data])
        res_paddle = paddle_out[0].as_ndarray()

        np.testing.assert_allclose(res_cinn, res_paddle, atol=1e-5)


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