# Copyright (c) 2021 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 unittest
import numpy as np

from op_test import OpTest
import random
import paddle

import paddle.nn as nn
import paddle.nn.functional as F
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import compiler, Program, program_guard

np.random.seed(0)


# define cumprod grad function.
def cumprod_grad(x, y, dy, dx, shape, dim):
    if dim < 0:
        dim += len(shape)
    mid_dim = shape[dim]
    outer_dim = 1
    inner_dim = 1
    for i in range(0, dim):
        outer_dim *= shape[i]
    for i in range(dim + 1, len(shape)):
        inner_dim *= shape[i]
    for i in range(outer_dim):
        for k in range(inner_dim):
            for j in range(mid_dim):
                index = i * mid_dim * inner_dim + j * inner_dim + k
                for n in range(mid_dim):
                    pos = i * mid_dim * inner_dim + n * inner_dim + k
                    elem = 0
                    if j == 0:
                        elem = dy[pos]
                    else:
                        elem = dy[pos] * y[index - inner_dim]
                    if pos > index:
                        for m in range(index + inner_dim, pos + inner_dim,
                                       inner_dim):
                            elem *= x[m]
                    elif pos < index:
                        elem = 0
                    dx[index] += elem


# test function.
class TestCumprod(OpTest):
    def init_params(self):
        self.shape = (2, 3, 4, 5)
        self.zero_nums = [0, 10, 20, 30, int(np.prod(self.shape))]

    def init_dtype(self):
        self.dtype = np.float64

    def setUp(self):
        paddle.enable_static()
        self.init_params()
        self.init_dtype()
        self.op_type = "cumprod"
        self.inputs = {'X': None}
        self.outputs = {'Out': None}
        self.attrs = {'dim': None}

    def prepare_inputs_outputs_attrs(self, dim, zero_num):
        self.x = np.random.random(self.shape).astype(self.dtype) + 0.5
        if zero_num > 0:
            zero_num = min(zero_num, self.x.size)
            shape = self.x.shape
            self.x = self.x.flatten()
            indices = random.sample(range(self.x.size), zero_num)
            for i in indices:
                self.x[i] = 0
            self.x = np.reshape(self.x, self.shape)
        self.out = np.cumprod(self.x, axis=dim)
        self.inputs = {'X': self.x}
        self.outputs = {'Out': self.out}
        self.attrs = {'dim': dim}

    def init_grad_input_output(self, dim):
        reshape_x = self.x.reshape(self.x.size)
        self.grad_out = np.ones(self.x.size, self.dtype)
        self.grad_x = np.zeros(self.x.size, self.dtype)
        out_data = self.out.reshape(self.x.size)
        if self.dtype == np.complex128 or self.dtype == np.complex64:
            reshape_x = np.conj(reshape_x)
            out_data = np.conj(out_data)
        cumprod_grad(reshape_x, out_data, self.grad_out, self.grad_x,
                     self.shape, dim)
        self.grad_x = self.grad_x.reshape(self.shape)
        self.grad_out = self.grad_out.reshape(self.shape)

    # test forward.
    def test_check_output(self):
        for dim in range(-len(self.shape), len(self.shape)):
            for zero_num in self.zero_nums:
                self.prepare_inputs_outputs_attrs(dim, zero_num)
                self.check_output()

    # test backward.
    def test_check_grad(self):
        for dim in range(-len(self.shape), len(self.shape)):
            for zero_num in self.zero_nums:
                self.prepare_inputs_outputs_attrs(dim, zero_num)
                self.init_grad_input_output(dim)
                if self.dtype == np.float64:
                    self.check_grad(['X'], 'Out')
                else:
                    self.check_grad(
                        ['X'],
                        'Out',
                        user_defined_grads=[self.grad_x],
                        user_defined_grad_outputs=[self.grad_out])


# test float32 case.
class TestCumprod_float32(TestCumprod):
    def init_dtype(self):
        self.dtype = np.float32


# test complex64 case.
class TestCumprod_complex64(TestCumprod):
    def init_dtype(self):
        self.dtype = np.complex64


# test complex128 case.
class TestCumprod_complex128(TestCumprod):
    def init_dtype(self):
        self.dtype = np.complex128


# test api.
class TestCumprodAPI(unittest.TestCase):
    def init_dtype(self):
        self.dtype = 'float64'
        self.shape = [2, 3, 10, 10]

    def setUp(self):
        paddle.enable_static()
        self.init_dtype()
        self.x = (np.random.rand(2, 3, 10, 10) + 0.5).astype(self.dtype)
        self.place = [paddle.CPUPlace()]
        if core.is_compiled_with_cuda():
            self.place.append(paddle.CUDAPlace(0))

    # test static graph api.
    def test_static_api(self):
        paddle.enable_static()

        def run(place):
            with paddle.static.program_guard(paddle.static.Program()):
                x = paddle.fluid.data('X', self.shape, dtype=self.dtype)
                out = paddle.cumprod(x, -2)
                exe = paddle.static.Executor(place)
                res = exe.run(feed={'X': self.x}, fetch_list=[out])
            out_ref = np.cumprod(self.x, -2)

            for r in res:
                self.assertEqual(np.allclose(out_ref, r), True)

        for place in self.place:
            run(place)

    # test dynamic graph api.
    def test_dygraph_api(self):
        def run(place):
            paddle.disable_static(place)
            x = paddle.to_tensor(self.x)
            out = paddle.cumprod(x, 1)
            out_ref = np.cumprod(self.x, 1)
            self.assertEqual(np.allclose(out_ref, out.numpy()), True)
            paddle.enable_static()

        for place in self.place:
            run(place)


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