"""
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 os
import unittest
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import test_global_storage
from test_util import GenArgList, type_name_to_flow_type, type_name_to_np_type

import oneflow.compatible.single_client.unittest
from oneflow.compatible import single_client as flow
from oneflow.compatible.single_client import typing as oft

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, data_type, shape):
    assert device_type in ["gpu", "cpu"]
    flow.clear_default_session()
    func_config = flow.FunctionConfig()
    if data_type == "float16":
        dtype = flow.float
    else:
        dtype = type_name_to_flow_type[data_type]

    def np_softmax(x):
        return np.exp(x) / np.sum(np.exp(x), axis=1, keepdims=True)

    @flow.global_function(type="train", function_config=func_config)
    def SoftmaxCrossEntropyWithLogitsJob(labels: oft.Numpy.Placeholder(shape, dtype)):
        with flow.scope.placement(device_type, "0:0"):
            if data_type == "float16":
                x = flow.get_variable(
                    "x",
                    shape=shape,
                    dtype=dtype,
                    initializer=flow.constant_initializer(20),
                    trainable=True,
                )
                loss = flow.cast(
                    flow.nn.softmax_cross_entropy_with_logits(
                        flow.cast(labels, dtype=flow.float16),
                        flow.cast(x, dtype=flow.float16),
                    ),
                    dtype=flow.float,
                )
            else:
                x = flow.get_variable(
                    "x",
                    shape=shape,
                    dtype=type_name_to_flow_type[data_type],
                    initializer=flow.random_uniform_initializer(minval=-10, maxval=10),
                    trainable=True,
                )
                loss = flow.nn.softmax_cross_entropy_with_logits(
                    labels=labels, logits=x
                )
            flow.optimizer.SGD(
                flow.optimizer.PiecewiseConstantScheduler([], [0.0001]), 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

    if data_type == "float16":
        labels = np_softmax(np.random.randint(0, 10, size=shape)).astype(np.float32)
    else:
        labels = np_softmax(np.random.randint(0, 10, size=shape)).astype(
            type_name_to_np_type[data_type]
        )
    of_out = SoftmaxCrossEntropyWithLogitsJob(labels).get()
    with tf.GradientTape(persistent=True) as tape:
        x = tf.Variable(test_global_storage.Get("x"))
        tf_out = tf.nn.softmax_cross_entropy_with_logits(labels, x)
        tf16_out = tf.nn.softmax_cross_entropy_with_logits(
            tf.cast(labels, dtype=tf.float16), tf.cast(x, dtype=tf.float16)
        )
        loss_diff = test_global_storage.Get("loss_diff")
        tf_x_diff = tape.gradient(tf_out, x, loss_diff)
    if data_type == "float16":
        tolerance = 0.001
    else:
        tolerance = 1e-05
    assert np.allclose(of_out.numpy(), tf_out.numpy(), rtol=tolerance, atol=tolerance)
    assert np.allclose(
        test_global_storage.Get("x_diff"),
        tf_x_diff.numpy(),
        rtol=tolerance,
        atol=tolerance,
    )
    flow.clear_default_session()


@flow.unittest.skip_unless_1n1d()
class TestSoftmaxCrossEntropy(flow.unittest.TestCase):
    def test_softmax_cross_entropy_with_logits(test_case):
        arg_dict = OrderedDict()
        arg_dict["device_type"] = ["gpu", "cpu"]
        arg_dict["data_type"] = ["double", "float32", "float16"]
        arg_dict["shape"] = [(64, 1000), (5, 5, 1000)]
        for arg in GenArgList(arg_dict):
            if arg[0] == "cpu" and arg[1] == "float16":
                continue
            compare_with_tensorflow(*arg)


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