#   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 unittest
from collections import Counter, OrderedDict

import numpy as np
from dygraph_to_static_utils import (
    Dy2StTestBase,
    enable_to_static_guard,
    test_ast_only,
)
from test_fetch_feed import Linear, Pool2D

import paddle
from paddle.jit.dy2static import convert_to_static


class TestCacheProgram(Dy2StTestBase):
    def setUp(self):
        self.batch_num = 5
        self.dygraph_class = Pool2D
        self.data = np.random.random((1, 2, 4, 4)).astype('float32')

    @test_ast_only
    def test_cache(self):
        prev_ops, cur_ops = Counter(), Counter()
        prev_out, cur_out = None, None
        static_net = paddle.jit.to_static(self.dygraph_class())
        for batch_id in range(self.batch_num):
            out = static_net(paddle.to_tensor(self.data))
            # Check outputs
            prev_out = cur_out
            cur_out = out
            # Check forward ops
            prev_ops = cur_ops

            cur_ops = Counter(
                [
                    op.name()
                    for op in static_net.forward.concrete_program.main_program.global_block().ops
                ]
            )

            if batch_id > 0:
                prev_out_numpy = (
                    prev_out[0].numpy()
                    if isinstance(prev_out, (tuple, list))
                    else prev_out.numpy()
                )
                cur_out_numpy = (
                    cur_out[0].numpy()
                    if isinstance(cur_out, (tuple, list))
                    else cur_out.numpy()
                )
                np.testing.assert_allclose(
                    prev_out_numpy,
                    cur_out_numpy,
                    rtol=1e-05,
                    err_msg=f'Output in previous batch is {prev_out_numpy}\n Output in current batch is \n{cur_out_numpy}',
                )
                self.assertEqual(prev_ops, cur_ops)


class TestCacheProgram2(TestCacheProgram):
    def setUp(self):
        self.batch_num = 5
        self.dygraph_class = Linear
        self.data = np.random.random((4, 10)).astype('float32')


class TestCacheProgramWithDictInput(TestCacheProgram):
    def setUp(self):
        class DummyModel(paddle.nn.Layer):
            def __init__(self):
                super().__init__()
                self.linear = paddle.nn.Linear(3, 4)

            def forward(self, x_dict):
                x, y = x_dict["x"], x_dict["y"]
                return (x * y).sum()

        self.batch_num = 2
        self.dygraph_class = DummyModel
        self.data = [
            {"x": paddle.randn(7, 3), "y": paddle.randn(1, 3)},
            {"y": paddle.randn(1, 3), "x": paddle.randn(7, 3)},
        ]

    @test_ast_only
    def test_cache(self):
        static_net = paddle.jit.to_static(self.dygraph_class(), full_graph=True)
        _ = static_net(self.data[0])
        cache1 = OrderedDict({**static_net.forward._program_cache._caches})
        _ = static_net(self.data[1])
        cache2 = static_net.forward._program_cache._caches

        self.assertEqual(
            cache1,
            cache2,
            msg=f"\ncache1({cache1})\n should be equal to \ncache2({cache2})",
        )


class TestCacheProgramWithOptimizer(Dy2StTestBase):
    def setUp(self):
        self.dygraph_class = Linear
        self.data = np.random.random((4, 10)).astype('float32')
        self.batch_num = 5

    def train_static(self):
        with enable_to_static_guard(True):
            return self.train()

    def train_dygraph(self):
        with enable_to_static_guard(False):
            return self.train()

    def train(self):
        static_net = paddle.jit.to_static(self.dygraph_class())
        adam = paddle.optimizer.Adam(
            learning_rate=0.001, parameters=static_net.parameters()
        )
        loss_data = []
        for batch_id in range(self.batch_num):
            input = paddle.to_tensor(self.data)
            pred, avg_loss = static_net(input)

            loss_data.append(avg_loss.numpy())
            avg_loss.backward()
            adam.minimize(avg_loss)
            static_net.clear_gradients()

        return loss_data

    def test_with_optimizer(self):
        dygraph_loss = self.train_dygraph()
        static_loss = self.train_static()
        np.testing.assert_allclose(
            dygraph_loss,
            static_loss,
            rtol=1e-05,
            err_msg=f'dygraph is {dygraph_loss}\n static_res is \n{static_loss}',
        )


def simple_func(x):
    inputs = paddle.assign(x)
    mean = paddle.mean(inputs)
    return mean


class TestConvertWithCache(Dy2StTestBase):
    def test_cache(self):
        static_func = convert_to_static(simple_func)
        # Get transformed function from cache.
        cached_func = convert_to_static(simple_func)
        self.assertTrue(id(static_func), id(cached_func))


def sum_even_until_limit(max_len, limit):
    ret_sum = paddle.to_tensor(np.zeros(1).astype('int32'))
    for i in range(max_len):
        if i % 2 > 0:
            continue
        elif i > limit:
            break

        ret_sum += i
    return ret_sum


def sum_under_while(limit):
    i = paddle.to_tensor(np.zeros(1).astype('int32'))
    ret_sum = paddle.to_tensor(np.zeros(1).astype('int32'))
    while i <= limit:
        ret_sum += i
        i += 1
    return ret_sum


class TestToOutputWithCache(Dy2StTestBase):
    def test_output(self):
        ret = paddle.jit.to_static(sum_even_until_limit)(80, 10)
        self.assertEqual(ret.numpy(), 30)

        ret = paddle.jit.to_static(sum_under_while)(100)
        self.assertEqual(ret.numpy(), 5050)


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