#   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 collections
import unittest
from functools import reduce

import numpy as np

import paddle
from paddle import base
from paddle.nn.utils import remove_weight_norm, weight_norm


class TestDygraphWeightNorm(unittest.TestCase):
    def setUp(self):
        self.init_test_case()
        self.set_data()

    def init_test_case(self):
        self.batch_size = 3
        self.data_desc = (['x', [2, 3, 3]],)
        self.dim = None

    def set_data(self):
        self.data = collections.OrderedDict()
        for desc in self.data_desc:
            data_name = desc[0]
            data_shape = desc[1]
            data_value = np.random.random(
                size=[self.batch_size] + data_shape
            ).astype('float32')
            self.data[data_name] = data_value

    def norm_except_dim(self, w, dim=None):
        shape = w.shape
        ndims = len(shape)
        shape_numel = reduce(lambda x, y: x * y, shape, 1)
        if dim == -1:
            return np.linalg.norm(w, axis=None, keepdims=True).flatten()
        elif dim == 0:
            tile_shape = list(w.shape)
            tile_shape[0] = 1
            w_matrix = np.reshape(w, (shape[0], shape_numel // shape[0]))
            return np.linalg.norm(w_matrix, axis=1, keepdims=True)
        elif dim == (ndims - 1):
            w_matrix = np.reshape(w, (shape_numel // shape[-1], shape[-1]))
            return np.linalg.norm(w_matrix, axis=0, keepdims=True)
        else:
            perm = list(range(ndims))
            perm_ori = list(range(ndims))
            perm[0] = dim
            perm[dim] = 0
            p_transposed = np.transpose(w, perm)
            return self.norm_except_dim(p_transposed, 0)

    def weight_normalize(self, w, dim=None):
        shape = w.shape
        ndims = len(shape)
        shape_numel = reduce(lambda x, y: x * y, shape, 1)
        v = w
        g = self.norm_except_dim(w, dim)
        g_mul = g

        if dim == -1:
            v_norm = v / (np.linalg.norm(v, axis=None, keepdims=True))
        elif dim == 0:
            w_matrix = np.reshape(w, (shape[0], shape_numel // shape[0]))
            v_norm = v / np.linalg.norm(w_matrix, axis=1)
            v_norm = np.reshape(v_norm, shape)
            g = np.squeeze(g, axis=1)
        elif dim == (ndims - 1):
            w_matrix = np.reshape(w, (shape_numel // shape[-1], shape[-1]))
            v_norm = v / np.linalg.norm(w_matrix, axis=0, keepdims=True)
            v_norm = np.reshape(v_norm, shape)
        else:
            perm = list(range(ndims))
            perm[0] = dim
            perm[dim] = 0
            p_transposed = np.transpose(v, perm)
            transposed_shape = p_transposed.shape
            transposed_shape_numel = reduce(
                lambda x, y: x * y, transposed_shape
            )
            p_matrix = np.reshape(
                p_transposed,
                (
                    p_transposed.shape[0],
                    transposed_shape_numel // p_transposed.shape[0],
                ),
            )
            v_norm = v / np.expand_dims(
                np.expand_dims(
                    np.linalg.norm(p_matrix, axis=1, keepdims=True), axis=0
                ),
                axis=(ndims - 1),
            )
            v_norm = np.reshape(v_norm, transposed_shape)
            v_norm = np.transpose(v_norm, perm)
            g = np.squeeze(g, axis=1)
            if dim == 1:
                eaxis = 2
            elif dim == 2:
                eaxis = 1
            g_mul = np.expand_dims(
                np.expand_dims(np.expand_dims(g, axis=0), axis=eaxis),
                axis=(ndims - 1),
            )
        w = g_mul * v_norm
        return g, v

    def test_check_output(self):
        base.enable_imperative()
        linear = paddle.nn.Conv2D(2, 3, 3)
        before_weight = linear.weight.numpy()
        if self.dim is None:
            self.dim = -1

        if self.dim != -1:
            self.dim = (self.dim + len(before_weight)) % len(before_weight)
        wn = weight_norm(linear, dim=self.dim)
        outputs = []
        for name, data in self.data.items():
            output = linear(base.dygraph.to_variable(data))
            outputs.append(output.numpy())
        after_weight = linear.weight
        self.actual_outputs = [linear.weight_g.numpy(), linear.weight_v.numpy()]

        expect_output = self.weight_normalize(before_weight, self.dim)

        for expect, actual in zip(expect_output, self.actual_outputs):
            np.testing.assert_allclose(
                np.array(actual), expect, rtol=1e-05, atol=0.001
            )


class TestDygraphWeightNormCase1(TestDygraphWeightNorm):
    def init_test_case(self):
        self.batch_size = 3
        self.data_desc = (['x', [2, 3, 3]],)
        self.dim = 0


class TestDygraphWeightNormCase2(TestDygraphWeightNorm):
    def init_test_case(self):
        self.batch_size = 3
        self.data_desc = (['x', [2, 3, 3]],)
        self.dim = 1


class TestDygraphWeightNormCase3(TestDygraphWeightNorm):
    def init_test_case(self):
        self.batch_size = 3
        self.data_desc = (['x', [2, 3, 3]],)
        self.dim = 3


class TestDygraphWeightNormCase4(TestDygraphWeightNorm):
    def init_test_case(self):
        self.batch_size = 3
        self.data_desc = (['x', [2, 3, 3]],)
        self.dim = -3


class TestDygraphRemoveWeightNorm(unittest.TestCase):
    def setUp(self):
        self.init_test_case()

    def init_test_case(self):
        self.batch_size = 3
        self.data_desc = (['x', [2, 3, 3]],)
        self.dim = None

    def test_check_output(self):
        base.enable_imperative()
        linear = paddle.nn.Conv2D(2, 3, 3)
        before_weight = linear.weight
        wn = weight_norm(linear, dim=self.dim)
        rwn = remove_weight_norm(linear)
        after_weight = linear.weight
        np.testing.assert_allclose(
            before_weight.numpy(), after_weight.numpy(), rtol=1e-05, atol=0.001
        )


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