"""
Copyright 2020 The OneFlow 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 math
import os
from collections import OrderedDict

import numpy as np
import oneflow as flow
import tensorflow as tf
import test_global_storage
from test_util import GenArgList

gpus = tf.config.experimental.list_physical_devices("GPU")
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)


def compare_with_tensorflow(device_type, activation_type, shape, data_type):
    assert device_type in ["gpu", "cpu"]
    flow.clear_default_session()
    flow.config.enable_debug_mode(True)
    func_config = flow.FunctionConfig()
    if data_type == flow.float16:
        func_config.enable_auto_mixed_precision(True)
        data_type = flow.float

    func_config.default_data_type(data_type)

    of_activation_map = {
        "relu": flow.nn.relu,
        "sigmoid": flow.math.sigmoid,
        "tanh": flow.math.tanh,
    }
    tf_activation_map = {
        "relu": tf.nn.relu,
        "sigmoid": tf.math.sigmoid,
        "tanh": tf.math.tanh,
        #        "gelu": tfa.activations.gelu,
    }

    @flow.global_function(type="train", function_config=func_config)
    def ActivationJob():
        with flow.scope.placement(device_type, "0:0"):
            x = flow.get_variable(
                "x",
                shape=shape,
                dtype=data_type,
                initializer=flow.random_uniform_initializer(minval=-10, maxval=10),
                trainable=True,
            )
            loss = of_activation_map[activation_type](x)
            lr_scheduler = flow.optimizer.PiecewiseConstantScheduler([], [1e-4])
            flow.optimizer.SGD(lr_scheduler, momentum=0).minimize(loss)

            flow.watch(x, test_global_storage.Setter("x"))
            flow.watch_diff(x, test_global_storage.Setter("x_diff"))
            flow.watch(loss, test_global_storage.Setter("loss"))
            flow.watch_diff(loss, test_global_storage.Setter("loss_diff"))

            return loss

    # OneFlow
    check_point = flow.train.CheckPoint()
    check_point.init()
    of_out = ActivationJob().get()
    # TensorFlow
    with tf.GradientTape(persistent=True) as tape:
        x = tf.Variable(test_global_storage.Get("x"))
        tf_out = tf_activation_map[activation_type](x)
    loss_diff = test_global_storage.Get("loss_diff")
    tf_x_diff = tape.gradient(tf_out, x, loss_diff)

    rtol = 1e-3 if activation_type is "gelu" else 1e-5
    atol = 1e-3 if activation_type is "gelu" else 1e-5
    assert np.allclose(of_out.numpy(), tf_out.numpy(), rtol, atol)
    assert np.allclose(test_global_storage.Get("x_diff"), tf_x_diff.numpy(), rtol, atol)


def test_activations(test_case):
    arg_dict = OrderedDict()
    arg_dict["device_type"] = ["gpu", "cpu"]
    #    arg_dict["activation_type"] = ["relu", "sigmoid", "tanh", "gelu"]
    arg_dict["activation_type"] = ["relu", "sigmoid", "tanh"]
    arg_dict["shape"] = [(1024, 1024)]
    arg_dict["data_type"] = [flow.float, flow.double]
    for arg in GenArgList(arg_dict):
        compare_with_tensorflow(*arg)

    if os.getenv("ONEFLOW_TEST_CPU_ONLY") is None:
        for act_type in arg_dict["activation_type"]:
            compare_with_tensorflow("gpu", act_type, (1024, 1024), flow.float16)
