#   Copyright (c) 2018 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 unittest
import numpy as np
import paddle
import random
import sys
sys.path.append("../")
from op_test_xpu import XPUOpTest
from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types
from xpu.get_test_cover_info import XPUOpTestWrapper

paddle.enable_static()
np.set_printoptions(threshold=np.inf)


def seqconv(x,
            lod,
            filter,
            context_length,
            context_start,
            padding_trainable=False,
            padding_data=None):
    [T, M] = x.shape
    col = np.zeros((T, context_length * M)).astype('float32')
    offset = [0]
    for seq_len in lod[0]:
        offset.append(offset[-1] + seq_len)
    begin_pad = np.max([0, -context_start])
    for i in range(len(offset) - 1):
        for j in range(context_length):
            in_begin = offset[i] + context_start + j
            in_end = offset[i + 1] + context_start + j
            out_begin = offset[i]
            out_end = offset[i + 1]
            if in_begin < offset[i]:
                pad_size = np.min(
                    [offset[i] - in_begin, offset[i + 1] - offset[i]])
                if padding_trainable:
                    sub_w = padding_data[j:j + pad_size, :]
                    col[offset[i]:offset[i] + pad_size, j * M:(j + 1) *
                        M] = sub_w
                out_begin = offset[i] + pad_size
                in_begin = offset[i]

            if in_end > offset[i + 1]:
                pad_size = np.min(
                    [in_end - offset[i + 1], offset[i + 1] - offset[i]])
                if padding_trainable:
                    sub_w = padding_data[begin_pad + context_start + j -
                                         pad_size:begin_pad + context_start +
                                         j, :]
                    col[offset[i + 1] - pad_size:offset[i + 1], j * M:(j + 1) *
                        M] = sub_w
                in_end = offset[i + 1]
                out_end = offset[i + 1] - pad_size
            if in_end <= in_begin:
                continue
            in_sub = x[in_begin:in_end, :]
            col[out_begin:out_end, j * M:(j + 1) * M] += in_sub
    return np.dot(col, filter)


class XPUTestSequenceConv(XPUOpTestWrapper):
    def __init__(self):
        self.op_name = 'sequence_conv'

    class TestSeqProject(XPUOpTest):
        def setUp(self):
            self.init_test_case()
            self.op_type = 'sequence_conv'
            self.dtype = self.in_type
            self.use_xpu = True

            if self.context_length == 1 \
                    and self.context_start == 0 \
                    and self.padding_trainable:
                print("If context_start is 0 " \
                      "and context_length is 1," \
                      " padding_trainable should be false.")
                return

            # one level, batch size
            x = np.random.uniform(-6.10907e-05, 0.000104218,
                                  [self.input_size[0],
                                   self.input_size[1]]).astype(self.dtype)
            w = np.random.uniform(-3.17068e-05, 0.000159822, [
                self.context_length * self.input_size[1],
                self.output_represention
            ]).astype(self.dtype)

            begin_pad = np.max([0, -self.context_start])
            end_pad = np.max([0, self.context_start + self.context_length - 1])
            total_pad = begin_pad + end_pad
            padding_data = np.random.uniform(
                0, 0, [total_pad, self.input_size[1]]).astype(self.dtype)
            self.pad_data = padding_data
            self.inputs = {
                'X': (x, self.lod),
                'Filter': w,
            }
            self.inputs_val = ['X', 'Filter']
            self.inputs_val_no_x = ['Filter']
            self.inputs_val_no_f = ['X']

            if total_pad != 0:
                self.inputs['PaddingData'] = padding_data
                self.inputs_val = ['X', 'PaddingData', 'Filter']
                self.inputs_val_no_x = ['PaddingData', 'Filter']
                self.inputs_val_no_f = ['PaddingData', 'X']

            self.attrs = {
                'contextStart': self.context_start,
                'contextLength': self.context_length,
                'paddingTrainable': self.padding_trainable,
                'contextStride': self.context_stride
            }
            out = seqconv(x, self.lod, w, self.context_length,
                          self.context_start, self.padding_trainable,
                          self.pad_data)
            self.outputs = {'Out': out}

        def test_check_output(self):
            place = paddle.XPUPlace(0)
            self.check_output_with_place(place)

        def test_check_grad_input(self):
            self.check_grad(['X'], 'Out', no_grad_set=set(self.inputs_val_no_x))

        def test_check_grad_padding_data(self):
            if self.padding_trainable:
                self.check_grad(
                    ['PaddingData'], 'Out', no_grad_set=set(['X', 'Filter']))

        def test_check_grad_Filter(self):
            self.check_grad(
                ['Filter'], 'Out', no_grad_set=set(self.inputs_val_no_f))

        def test_check_grad_input_filter(self):
            if self.padding_trainable:
                self.check_grad(
                    ['X', 'Filter'], 'Out', no_grad_set=set(['PaddingData']))

        def test_check_grad_padding_input(self):
            if self.padding_trainable:
                self.check_grad(
                    self.inputs_val_no_f, 'Out', no_grad_set=set(['Filter']))

        def test_check_grad_padding_filter(self):
            if self.padding_trainable:
                self.check_grad(
                    self.inputs_val_no_x, 'Out', no_grad_set=set(['X']))

        def init_test_case(self):
            self.input_row = 7
            self.input_col = 25
            self.context_start = -2
            self.context_length = 5
            self.padding_trainable = False
            self.context_stride = 1

            self.input_size = [self.input_row, self.input_col]
            offset_lod = [[0, 1, self.input_row]]
            self.lod = [[]]
            # convert from offset-based lod to length-based lod
            for i in range(len(offset_lod[0]) - 1):
                self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
            self.output_represention = 8  # output feature size

    class TestSeqProjectCase1(TestSeqProject):
        def init_test_case(self):
            self.input_row = 11
            self.context_start = -2
            self.context_length = 5
            self.padding_trainable = False
            self.context_stride = 1

            self.input_size = [self.input_row, 50]
            offset_lod = [[0, 4, 5, 8, self.input_row]]
            self.lod = [[]]
            # convert from offset-based lod to length-based lod
            for i in range(len(offset_lod[0]) - 1):
                self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
            self.output_represention = 8  # output feature size

    class TestSeqProjectCase2Len0(TestSeqProject):
        def init_test_case(self):
            self.input_row = 11
            self.context_start = -2
            self.context_length = 5
            self.padding_trainable = False
            self.context_stride = 1

            self.input_size = [self.input_row, 50]
            offset_lod = [[0, 0, 4, 5, 5, 8, self.input_row, self.input_row]]
            self.lod = [[]]
            # convert from offset-based lod to length-based lod
            for i in range(len(offset_lod[0]) - 1):
                self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
            self.output_represention = 8  # output feature size

    class TestSeqProjectCase3(TestSeqProject):
        def init_test_case(self):
            self.input_row = 25
            self.context_start = -2
            self.context_length = 5
            self.padding_trainable = False
            self.context_stride = 1

            self.input_size = [self.input_row, 25]
            idx = list(range(self.input_size[0]))
            del idx[0]
            offset_lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +
                          [self.input_size[0]]]
            self.lod = [[]]
            # convert from offset-based lod to length-based lod
            for i in range(len(offset_lod[0]) - 1):
                self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
            self.output_represention = 8  # output feature size

    class TestSeqProjectCase4(TestSeqProject):
        def init_test_case(self):
            self.input_row = 7835
            self.input_col = 128
            self.context_start = -2
            self.context_length = 5
            self.padding_trainable = False
            self.context_stride = 1

            self.input_size = [self.input_row, self.input_col]
            offset_lod = [[
                0, 1, 2, 3, 131, 241, 242, 263, 264, 265, 266, 267, 268, 387,
                515, 516, 644, 645, 772, 794, 922, 923, 924, 944, 945, 1073,
                1074, 1202, 1330, 1458, 1556, 1557, 1558, 1686, 1748, 1876,
                1912, 1913, 1914, 2032, 2066, 2194, 2308, 2309, 2347, 2475,
                2476, 2477, 2478, 2606, 2607, 2735, 2736, 2737, 2738, 2838,
                2966, 2967, 2968, 2969, 3097, 3225, 3353, 3481, 3482, 3520,
                3642, 3643, 3754, 3882, 3883, 4010, 4011, 4012, 4140, 4219,
                4228, 4356, 4357, 4415, 4475, 4476, 4604, 4605, 4606, 4694,
                4695, 4808, 4936, 4961, 4962, 5004, 5132, 5260, 5312, 5440,
                5441, 5569, 5570, 5675, 5676, 5750, 5810, 5811, 5939, 6021,
                6149, 6277, 6278, 6364, 6425, 6519, 6647, 6648, 6739, 6867,
                6995, 6996, 7120, 7223, 7244, 7367, 7407, 7408, 7467, 7595,
                7699, 7827, 7835
            ]]
            self.lod = [[]]
            # convert from offset-based lod to length-based lod
            for i in range(len(offset_lod[0]) - 1):
                self.lod[0].append(offset_lod[0][i + 1] - offset_lod[0][i])
            self.output_represention = 8  # output feature size


support_types = get_xpu_op_support_types('sequence_conv')
for stype in support_types:
    create_test_class(globals(), XPUTestSequenceConv, stype)


class TestSeqConvApi(unittest.TestCase):
    def test_api(self):
        import paddle.fluid as fluid

        x = fluid.layers.data('x', shape=[32], lod_level=1)
        y = fluid.layers.sequence_conv(
            input=x, num_filters=2, filter_size=3, padding_start=None)

        place = fluid.CPUPlace()
        x_tensor = fluid.create_lod_tensor(
            np.random.rand(10, 32).astype("float32"), [[2, 3, 1, 4]], place)
        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        ret = exe.run(feed={'x': x_tensor}, fetch_list=[y], return_numpy=False)


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