#   Copyright (c) 2019 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.

from __future__ import print_function

import os
import unittest
import tempfile
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
from op_test import OpTest
import paddle.inference as paddle_infer

paddle.enable_static()


class TestBincountOpAPI(unittest.TestCase):
    """Test bincount api."""

    def test_static_graph(self):
        startup_program = fluid.Program()
        train_program = fluid.Program()
        with fluid.program_guard(train_program, startup_program):
            inputs = fluid.data(name='input', dtype='int64', shape=[7])
            weights = fluid.data(name='weights', dtype='int64', shape=[7])
            output = paddle.bincount(inputs, weights=weights)
            place = fluid.CPUPlace()
            if fluid.core.is_compiled_with_cuda():
                place = fluid.CUDAPlace(0)
            exe = fluid.Executor(place)
            exe.run(startup_program)
            img = np.array([0, 1, 1, 3, 2, 1, 7]).astype(np.int64)
            w = np.array([0, 1, 1, 2, 2, 1, 0]).astype(np.int64)
            res = exe.run(train_program,
                          feed={
                              'input': img,
                              'weights': w
                          },
                          fetch_list=[output])
            actual = np.array(res[0])
            expected = np.bincount(img, weights=w)
            self.assertTrue((actual == expected).all(),
                            msg='bincount output is wrong, out =' + str(actual))

    def test_dygraph(self):
        with fluid.dygraph.guard():
            inputs_np = np.array([0, 1, 1, 3, 2, 1, 7]).astype(np.int64)
            inputs = fluid.dygraph.to_variable(inputs_np)
            actual = paddle.bincount(inputs)
            expected = np.bincount(inputs)
            self.assertTrue(
                (actual.numpy() == expected).all(),
                msg='bincount output is wrong, out =' + str(actual.numpy()))


class TestBincountOpError(unittest.TestCase):
    """Test bincount op error."""

    def run_network(self, net_func):
        with fluid.dygraph.guard():
            net_func()

    def test_input_value_error(self):
        """Test input tensor should be non-negative."""

        def net_func():
            input_value = paddle.to_tensor([1, 2, 3, 4, -5])
            paddle.bincount(input_value)

        with self.assertRaises(ValueError):
            self.run_network(net_func)

    def test_input_shape_error(self):
        """Test input tensor should be 1-D tansor."""

        def net_func():
            input_value = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
            paddle.bincount(input_value)

        with self.assertRaises(ValueError):
            self.run_network(net_func)

    def test_minlength_value_error(self):
        """Test minlength is non-negative ints."""

        def net_func():
            input_value = paddle.to_tensor([1, 2, 3, 4, 5])
            paddle.bincount(input_value, minlength=-1)

        with self.assertRaises(IndexError):
            self.run_network(net_func)

    def test_input_type_errors(self):
        """Test input tensor should only contain non-negative ints."""

        def net_func():
            input_value = paddle.to_tensor([1., 2., 3., 4., 5.])
            paddle.bincount(input_value)

        with self.assertRaises(TypeError):
            self.run_network(net_func)

    def test_weights_shape_error(self):
        """Test weights tensor should have the same shape as input tensor."""

        def net_func():
            input_value = paddle.to_tensor([1, 2, 3, 4, 5])
            weights = paddle.to_tensor([1, 1, 1, 1, 1, 1])
            paddle.bincount(input_value, weights=weights)

        with self.assertRaises(ValueError):
            self.run_network(net_func)


class TestBincountOp(OpTest):
    # without weights
    def setUp(self):
        self.op_type = "bincount"
        self.python_api = paddle.bincount
        self.init_test_case()
        self.inputs = {"X": self.np_input}
        self.attrs = {"minlength": self.minlength}
        self.outputs = {"Out": self.Out}

    def init_test_case(self):
        self.minlength = 0
        self.np_input = np.random.randint(low=0, high=20, size=10)
        self.Out = np.bincount(self.np_input, minlength=self.minlength)

    def test_check_output(self):
        self.check_output(check_eager=False)


class TestCase1(TestBincountOp):
    # with weights(FLOAT32)
    def setUp(self):
        self.op_type = "bincount"
        self.python_api = paddle.bincount
        self.init_test_case()
        self.inputs = {"X": self.np_input, "Weights": self.np_weights}
        self.attrs = {"minlength": self.minlength}
        self.outputs = {"Out": self.Out}

    def init_test_case(self):
        self.minlength = 0
        self.np_weights = np.random.randint(low=0, high=20,
                                            size=10).astype(np.float32)
        self.np_input = np.random.randint(low=0, high=20, size=10)
        self.Out = np.bincount(self.np_input,
                               weights=self.np_weights,
                               minlength=self.minlength).astype(np.float32)


class TestCase2(TestBincountOp):
    # with weights(other)
    def setUp(self):
        self.op_type = "bincount"
        self.python_api = paddle.bincount
        self.init_test_case()
        self.inputs = {"X": self.np_input, "Weights": self.np_weights}
        self.attrs = {"minlength": self.minlength}
        self.outputs = {"Out": self.Out}

    def init_test_case(self):
        self.minlength = 0
        self.np_weights = np.random.randint(low=0, high=20, size=10)
        self.np_input = np.random.randint(low=0, high=20, size=10)
        self.Out = np.bincount(self.np_input,
                               weights=self.np_weights,
                               minlength=self.minlength)


class TestCase3(TestBincountOp):
    # empty input
    def init_test_case(self):
        self.minlength = 0
        self.np_input = np.array([], dtype=np.int64)
        self.Out = np.bincount(self.np_input, minlength=self.minlength)


class TestCase4(TestBincountOp):
    # with input(INT32)
    def init_test_case(self):
        self.minlength = 0
        self.np_input = np.random.randint(low=0, high=20,
                                          size=10).astype(np.int32)
        self.Out = np.bincount(self.np_input, minlength=self.minlength)


class TestCase5(TestBincountOp):
    # with minlength greater than max(X)
    def init_test_case(self):
        self.minlength = 20
        self.np_input = np.random.randint(low=0, high=10, size=10)
        self.Out = np.bincount(self.np_input, minlength=self.minlength)


class TestTensorMinlength(unittest.TestCase):

    def setUp(self):
        paddle.disable_static()
        paddle.seed(2022)
        self.temp_dir = tempfile.TemporaryDirectory()
        self.save_path = os.path.join(self.temp_dir.name,
                                      'tensor_minlength_bincount')
        self.place = paddle.CUDAPlace(
            0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace()

    def test_dygraph(self):
        paddle.disable_static()
        x = np.random.randint(0, 10, [20])
        minlength = 2
        np_out = np.bincount(x, minlength=minlength)
        pd_out = paddle.bincount(paddle.to_tensor(x),
                                 minlength=paddle.to_tensor([2], dtype='int32'))
        np.testing.assert_allclose(np_out, pd_out.numpy())

    def test_static_and_infer(self):
        paddle.enable_static()
        np_x = np.random.randn(100).astype('float32')
        main_prog = paddle.static.Program()
        starup_prog = paddle.static.Program()
        with paddle.static.program_guard(main_prog, starup_prog):
            # run static
            x = paddle.static.data(shape=np_x.shape, name='x', dtype=np_x.dtype)
            linear = paddle.nn.Linear(np_x.shape[0], np_x.shape[0])
            linear_out = linear(x)
            relu_out = paddle.nn.functional.relu(linear_out)
            minlength = paddle.full([1], 3, dtype='int32')
            out = paddle.bincount(paddle.cast(relu_out, 'int32'),
                                  minlength=minlength)

            exe = paddle.static.Executor(self.place)
            exe.run(starup_prog)
            static_out = exe.run(feed={'x': np_x}, fetch_list=[out])

            # run infer
            paddle.static.save_inference_model(self.save_path, [x], [out], exe)
            config = paddle_infer.Config(self.save_path + '.pdmodel',
                                         self.save_path + '.pdiparams')
            if paddle.is_compiled_with_cuda():
                config.enable_use_gpu(100, 0)
            else:
                config.disable_gpu()

            predictor = paddle_infer.create_predictor(config)
            input_names = predictor.get_input_names()
            input_handle = predictor.get_input_handle(input_names[0])
            fake_input = np_x
            input_handle.reshape(np_x.shape)
            input_handle.copy_from_cpu(fake_input)
            predictor.run()
            output_names = predictor.get_output_names()
            output_handle = predictor.get_output_handle(output_names[0])
            infer_out = output_handle.copy_to_cpu()
            np.testing.assert_allclose(static_out[0], infer_out)


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