#   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 os
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard

_set_use_system_allocator(True)


def _reference_testing(x, scale, offset, mean, var, epsilon, data_format):
    x_shape = x.shape
    if len(x_shape) == 2:
        if data_format == "NCHW":
            x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1))
        else:
            x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
    if len(x_shape) == 3:
        if data_format == "NCHW":  # NCL -> NCL1
            x = np.reshape(x, (x_shape[0], x_shape[1], x_shape[2], 1))
        else:  # NLC -> NL1C
            x = np.reshape(x, (x_shape[0], x_shape[1], 1, x_shape[2]))

    if data_format == "NCHW":
        n, c, h, w = x.shape
        mean_tile = np.reshape(mean, (1, c, 1, 1))
        mean_tile = np.tile(mean_tile, (n, 1, h, w))
        var_tile = np.reshape(var, (1, c, 1, 1))
        var_tile = np.tile(var_tile, (n, 1, h, w))
        normalized = (x - mean_tile) / np.sqrt(var_tile + epsilon)
        scale_tile = np.reshape(scale, (1, c, 1, 1))
        scale_tile = np.tile(scale_tile, (n, 1, h, w))
        offset_tile = np.reshape(offset, (1, c, 1, 1))
        offset_tile = np.reshape(offset_tile, (1, c, 1, 1))
        y = normalized * scale_tile + offset_tile
    elif data_format == "NHWC":
        normalized = (x - mean) / np.sqrt(var + epsilon)
        y = normalized * scale + offset
    else:
        raise ValueError("Unknown data order.")

    if len(x_shape) == 2 or len(x_shape) == 3:
        y = np.reshape(y, x_shape)
    return y


def _cal_mean_variance(x, epsilon, data_format):
    assert data_format in ['NCHW', 'NHWC']
    x_shape = x.shape
    if len(x_shape) == 3:
        if data_format == "NCHW":  # NCL -> NCL1
            x = np.reshape(x, (x_shape[0], x_shape[1], x_shape[2], 1))
        else:  # NLC -> NL1C
            x = np.reshape(x, (x_shape[0], x_shape[1], 1, x_shape[2]))
    x_square = x * x
    axis = (0, 2, 3) if data_format == 'NCHW' else (0, 1, 2)
    C = x.shape[1] if data_format == 'NCHW' else x.shape[-1]
    x_square_sum = np.sum(x_square, axis)
    x_sum = np.sum(x, axis=axis)
    element_count = np.size(x) / C
    mean = x_sum / element_count
    var = x_square_sum / element_count - mean * mean
    return mean, var


def _reference_training(x, scale, offset, epsilon, data_format):
    x_shape = x.shape

    if len(x_shape) == 3:
        if data_format == "NCHW":  # NCL -> NCL1
            x = np.reshape(x, (x_shape[0], x_shape[1], x_shape[2], 1))
        else:  # NLC -> NL1C
            x = np.reshape(x, (x_shape[0], x_shape[1], 1, x_shape[2]))

    if data_format == "NCHW":
        n, c, h, w = x.shape
        x_square = x * x
        x_square_sum = np.sum(x_square, (0, 2, 3))
        x_sum = np.sum(x, axis=(0, 2, 3))
        element_count = np.size(x) / int(np.shape(x)[1])
        mean = x_sum / element_count
        var = x_square_sum / element_count - mean * mean
        mean_tile = np.reshape(mean, (1, c, 1, 1))
        mean_tile = np.tile(mean_tile, (n, 1, h, w))
        var_tile = np.reshape(var, (1, c, 1, 1))
        var_tile = np.tile(var_tile, (n, 1, h, w))
        normalized = (x - mean_tile) / np.sqrt(var_tile + epsilon)
        scale_tile = np.reshape(scale, (1, c, 1, 1))
        scale_tile = np.tile(scale_tile, (n, 1, h, w))
        offset_tile = np.reshape(offset, (1, c, 1, 1))
        offset_tile = np.reshape(offset_tile, (1, c, 1, 1))
        y = normalized * scale_tile + offset_tile
    elif data_format == "NHWC":
        x_square = x * x
        x_square_sum = np.sum(x_square, (0, 1, 2))
        x_sum = np.sum(x, axis=(0, 1, 2))
        element_count = np.size(x) / int(np.shape(x)[-1])
        mean = x_sum / element_count
        var = x_square_sum / element_count - mean * mean
        normalized = (x - mean) / np.sqrt(var + epsilon)
        y = normalized * scale + offset
    else:
        raise ValueError("Unknown data order.")

    if len(x_shape) == 3:
        y = np.reshape(y, x_shape)
    return y, mean, var


def _reference_grad(x, y_grad, scale, mean, var, epsilon, data_format):
    # Use the following formulas to calculate gradients:
    # grad_scale =
    #   sum(grad_y * (x - mean)) * rsqrt(var + epsilon)
    #
    # grad_offset = sum(output_y)
    #
    # x_grad =
    #   1/N * scale * rsqrt(var + epsilon) * (N * grad_y - sum(grad_y) -
    #   (x - mean) * sum(grad_y * (x - mean)) / (var + epsilon))

    # transfer from (N, C, H, W) to (N, H, W, C) to simplify computation
    if data_format != "NCHW" and data_format != "NHWC":
        raise ValueError("Unknown data order.")

    x_shape = x.shape
    if len(x_shape) == 3:
        if data_format == "NCHW":  # NCL -> NCL1
            x = np.reshape(x, (x_shape[0], x_shape[1], x_shape[2], 1))
            y_grad = np.reshape(y_grad, (x_shape[0], x_shape[1], x_shape[2], 1))
        else:  # NLC -> NL1C
            x = np.reshape(x, (x_shape[0], x_shape[1], 1, x_shape[2]))
            y_grad = np.reshape(y_grad, (x_shape[0], x_shape[1], 1, x_shape[2]))

    if data_format == "NCHW":
        x = np.transpose(x, (0, 2, 3, 1))
        y_grad = np.transpose(y_grad, (0, 2, 3, 1))

    x_grad = scale * (y_grad - np.mean(
        y_grad, axis=(0, 1, 2)) - (x - mean) * np.mean(
            y_grad * (x - mean), axis=(0, 1, 2)) /
                      (var + epsilon)) / np.sqrt(var + epsilon)
    grad_scale = np.sum(y_grad * (x - mean) / np.sqrt(var + epsilon),
                        axis=(0, 1, 2))
    grad_offset = np.sum(y_grad, axis=(0, 1, 2))

    # transfer back to N, C, H, W
    if data_format == "NCHW":
        x_grad = np.transpose(x_grad, (0, 3, 1, 2))
        x = np.transpose(x, (0, 3, 1, 2))
        y_grad = np.transpose(y_grad, (0, 3, 1, 2))

    if len(x_shape) == 3:
        x_grad = np.reshape(x_grad, x_shape)

    return x_grad, grad_scale, grad_offset


def create_or_get_tensor(scope, var_name, var, place):
    tensor = scope.var(var_name).get_tensor()
    if var is not None:
        assert isinstance(var, np.ndarray)
        tensor.set_recursive_sequence_lengths([])
        tensor.set(var, place)
    return tensor


def set_output_grad(scope, outputs, place, feed_dict=None):
    def __set_tensor__(name, data=None):
        out_tensor = scope.find_var(name).get_tensor()
        grad_tensor = scope.var(grad_var_name(name)).get_tensor()
        out_dtype = out_tensor.dtype()
        if data is None:
            if out_dtype == core.VarDesc.VarType.FP64:
                data = np.ones(out_tensor.shape(), dtype=np.float64)
            elif out_dtype == core.VarDesc.VarType.FP32:
                data = np.ones(out_tensor.shape(), dtype=np.float32)
            else:
                raise ValueError("Not supported data type " + str(out_dtype))
        grad_tensor.set(data, place)

    for output in outputs:
        data = None
        if output in feed_dict:
            data = feed_dict[output]
        __set_tensor__(output, data)


class TestBatchNormOpInference(unittest.TestCase):
    def setUp(self):
        self.dtype = np.float32
        self.use_mkldnn = False
        self.fuse_with_relu = False
        self.init_kernel_type()

    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)

    def check_with_place(self, place, data_layout, dtype, shape):
        epsilon = 0.00001
        if len(shape) == 2:
            x_shape = shape
            c = x_shape[1]
        else:
            n, h, w, c = shape[0], shape[1], shape[2], shape[3]
            if data_layout == "NHWC":
                x_shape = [n, h, w, c]
            elif data_layout == "NCHW":
                x_shape = [n, c, h, w]
            else:
                raise ValueError("Unknown data layout.")
        scale_shape = [c]

        x_val = np.random.random_sample(x_shape).astype(dtype)
        # generate some negative values to test case with relu fused
        x_val = x_val - 0.5
        scale_val = np.random.random_sample(scale_shape).astype(np.float32)
        bias_val = np.random.random_sample(scale_shape).astype(np.float32)

        mean = np.zeros(scale_shape).astype(np.float32)
        variance = np.ones(scale_shape).astype(np.float32)

        y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
                                   epsilon, data_layout).astype(dtype)
        if self.fuse_with_relu:
            y_out = np.maximum(y_out, 0)

        scope = core.Scope()

        # create input
        x_tensor = create_or_get_tensor(scope, "x_val",
                                        OpTest.np_dtype_to_fluid_dtype(x_val),
                                        place)
        scale_tensor = create_or_get_tensor(
            scope, "scale_val",
            OpTest.np_dtype_to_fluid_dtype(scale_val), place)
        bias_tensor = create_or_get_tensor(
            scope, "bias_val", OpTest.np_dtype_to_fluid_dtype(bias_val), place)
        mean_tensor = create_or_get_tensor(scope, "mean",
                                           OpTest.np_dtype_to_fluid_dtype(mean),
                                           place)
        variance_tensor = create_or_get_tensor(
            scope, "variance", OpTest.np_dtype_to_fluid_dtype(variance), place)

        # create output
        y_tensor = create_or_get_tensor(scope, "y_out", None, place)
        saved_mean_tensor = create_or_get_tensor(scope, "saved_mean", None,
                                                 place)
        saved_variance_tensor = create_or_get_tensor(scope, "saved_variance",
                                                     None, place)
        mean_out_tensor = mean_tensor
        variance_out_tensor = variance_tensor

        batch_norm_op = Operator(
            "batch_norm",
            # inputs
            X="x_val",
            Scale="scale_val",
            Bias="bias_val",
            Mean="mean",
            Variance="variance",
            # outputs
            Y="y_out",
            MeanOut="mean",
            VarianceOut="variance",
            SavedMean="saved_mean",
            SavedVariance="saved_variance",
            # attrs
            is_test=True,
            data_layout=data_layout,
            use_mkldnn=self.use_mkldnn,
            fuse_with_relu=self.fuse_with_relu,
            epsilon=epsilon)

        batch_norm_op.run(scope, place)

        # When op is called without Executor then
        # MKL-DNN Tensor is returned. For NHWC data layout
        # dims will be in NCHW order as it is MKL-DNN way
        # of memory descripting. So we need to convert NCHW
        # dims into NHWC.
        if data_layout == "NHWC" and self.use_mkldnn == True:
            # Create executor to have MKL-DNN cache 
            # cleared after NHWC unit test
            place = core.CPUPlace()
            exe = fluid.Executor(place)
            dims = y_tensor.shape()
            c = dims.pop(1)
            dims.append(c)
            y_tensor._set_dims(dims)

        # check inference result
        self.__assert_close(
            y_tensor,
            y_out,
            "inference output are different at " + str(place) + ", " +
            data_layout + ", " + str(np.dtype(dtype)) +
            str(np.array(y_tensor)) + str(y_out),
            atol=1e-3)

    def test_check_output(self):
        places = [core.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))

        for place in places:
            for data_format in ["NCHW", "NHWC"]:
                self.check_with_place(place, data_format, self.dtype,
                                      [2, 3, 4, 5])
                self.check_with_place(place, data_format, self.dtype, [2, 3])

    def init_kernel_type(self):
        pass


class TestFP16BatchNormOpInference(TestBatchNormOpInference):
    def setUp(self):
        self.dtype = np.float16
        self.use_mkldnn = False
        self.fuse_with_relu = False
        self.init_kernel_type()

    def test_check_output(self):
        places = []
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place):
                places.append(place)
        for place in places:
            #for data_format in ["NCHW", "NHWC"]:
            for data_format in ["NCHW"]:
                self.check_with_place(place, data_format, self.dtype,
                                      [2, 3, 4, 5])
                self.check_with_place(place, data_format, self.dtype, [2, 3])


class TestBatchNormOpTraining(unittest.TestCase):
    def setUp(self):
        self.use_mkldnn = False
        self.fuse_with_relu = False
        self.data_formats = ["NCHW", "NHWC"]
        self.momentum = 0.9
        self.use_momentum_variable = False
        self.epsilon = 0.00001
        self.init_kernel_type()
        self.init_test_case()

    def init_test_case(self):
        self.use_global_stats = False
        self.no_grad_set = set()
        self.fetch_list = [
            'y', 'mean', 'variance', 'saved_mean', 'saved_variance', 'x@GRAD',
            'scale@GRAD', 'bias@GRAD'
        ]

    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
        np.allclose(np.array(tensor), np_array, atol=atol)

    def ref_forward_backward(self, x, y_grad, scale, bias, mean, variance,
                             epsilon, momentum, shape, data_layout):
        # run forward
        y, saved_mean, var_ref = _reference_training(x, scale, bias, epsilon,
                                                     data_layout)
        mean_out = saved_mean * (1. - momentum) + momentum * mean
        variance_out = var_ref * (1. - momentum) + momentum * variance
        saved_variance = 1. / np.sqrt(var_ref + epsilon)
        # run backward
        x_grad, scale_grad, bias_grad = _reference_grad(
            x, y_grad, scale, saved_mean, var_ref, epsilon, data_layout)

        return y, mean_out, variance_out, saved_mean, saved_variance, x_grad, scale_grad, bias_grad

    def set_mean_variance(self, scale_shape, x, data_layout):
        mean, variance = _cal_mean_variance(x, self.epsilon, data_layout)
        mean_pre = np.zeros(scale_shape).astype(np.float32)
        variance_pre = np.ones(scale_shape).astype(np.float32)
        # computing global mean/variance for one step
        if self.use_global_stats:
            mom = self.momentum
            mean = mean * (1. - mom) + mom * mean_pre
            variance = variance * (1. - mom) + mom * variance_pre
        return mean, variance

    def test_forward_backward(self):
        def test_with_place(place, data_layout, shape):
            # attr
            epsilon = self.epsilon
            momentum = self.momentum
            if data_layout == "NCHW":
                n, c, h, w = shape[0], shape[1], shape[2], shape[3]
            else:
                n, h, w, c = shape[0], shape[1], shape[2], shape[3]
            scale_shape = [c]

            np.random.seed(123)
            x = np.random.random_sample(shape).astype(np.float32)
            scale = np.random.random_sample(scale_shape).astype(np.float32)
            bias = np.random.random_sample(scale_shape).astype(np.float32)
            mean, variance = self.set_mean_variance(scale_shape, x, data_layout)
            y_grad = np.random.random_sample(shape).astype(np.float32)
            momentum_var = np.array([momentum]).astype(np.float32)

            y, mean_out, variance_out, saved_mean, saved_variance, x_grad, scale_grad, bias_grad = self.ref_forward_backward(
                x, y_grad, scale, bias, mean, variance, epsilon, momentum,
                shape, data_layout)

            var_dict = locals()
            var_dict['y@GRAD'] = y_grad
            var_dict['x@GRAD'] = x_grad
            var_dict['scale@GRAD'] = scale_grad
            var_dict['bias@GRAD'] = bias_grad

            var_names = [
                'x', 'scale', 'bias', 'mean', 'variance', 'y', 'saved_mean',
                'saved_variance', 'momentum_var'
            ]
            ground_truth = {name: var_dict[name] for name in var_names}

            program = fluid.Program()
            with fluid.program_guard(program):
                block = program.global_block()
                for name in ground_truth:
                    block.create_var(
                        name=name,
                        dtype='float32',
                        shape=ground_truth[name].shape)
                inputs = {
                    "X": block.var('x'),
                    "Scale": block.var('scale'),
                    "Bias": block.var('bias'),
                    "Mean": block.var('mean'),
                    "Variance": block.var('variance')
                }
                attrs = {
                    "epsilon": epsilon,
                    "is_test": False,
                    "data_layout": data_layout,
                    "use_mkldnn": self.use_mkldnn,
                    "fuse_with_relu": self.fuse_with_relu,
                    "use_global_stats": self.use_global_stats
                }
                if self.use_momentum_variable:
                    inputs['MomentumTensor'] = block.var('momentum_var')
                else:
                    attrs['momentum'] = momentum

                outputs = {
                    "Y": block.var('y'),
                    "MeanOut": block.var('mean'),  # share memory
                    "VarianceOut": block.var('variance'),  # share memory
                    "SavedMean": block.var('saved_mean'),
                    "SavedVariance": block.var('saved_variance')
                }
                block.create_var(name="reserve_space", dtype='float32')
                outputs["ReserveSpace"] = block.var('reserve_space')
                bn_op = block.append_op(
                    type="batch_norm",
                    inputs=inputs,
                    outputs=outputs,
                    attrs=attrs)
                block.create_var(name='y@GRAD', dtype='float32', shape=y.shape)

                # generate backward op_desc
                grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
                    bn_op.desc, self.no_grad_set, [])
                grad_op_desc = grad_op_desc_list[0]
                new_op_desc = block.desc.append_op()
                new_op_desc.copy_from(grad_op_desc)
                for var_name in grad_op_desc.output_arg_names():
                    block.desc.var(var_name.encode("ascii"))
                grad_op_desc.infer_var_type(block.desc)
                grad_op_desc.infer_shape(block.desc)
                for arg in grad_op_desc.output_arg_names():
                    grad_var = block.desc.find_var(arg.encode("ascii"))
                    grad_var.set_dtype(core.VarDesc.VarType.FP32)

                program._sync_with_cpp()

                exe = fluid.Executor(place)
                out = exe.run(program,
                              feed={
                                  name: var_dict[name]
                                  for name in [
                                      'x', 'scale', 'bias', 'mean', 'variance',
                                      'y@GRAD', 'momentum_var'
                                  ]
                              },
                              fetch_list=self.fetch_list)

            for id, name in enumerate(self.fetch_list):
                if name == 'variance':
                    self.__assert_close(
                        var_dict[name], out[id], name, atol=1e-3)
                    continue
                self.__assert_close(var_dict[name], out[id], name)
            print("op test forward passed: ", str(place), data_layout)

        places = [core.CPUPlace()]

        if core.is_compiled_with_cuda():
            places.append(core.CUDAPlace(0))

        for place in places:
            for data_format in self.data_formats:
                test_with_place(place, data_format, [2, 3, 4, 5])

    def init_kernel_type(self):
        pass


class TestBatchNormOpTrainingCase1(TestBatchNormOpTraining):
    def init_test_case(self):
        self.use_global_stats = False
        self.no_grad_set = set(['scale@GRAD', 'bias@GRAD'])
        self.fetch_list = ['y', 'mean', 'variance', 'x@GRAD']


class TestBatchNormOpTrainingCase2(TestBatchNormOpTraining):
    def init_test_case(self):
        self.use_global_stats = False
        self.no_grad_set = set()
        self.fetch_list = [
            'y', 'mean', 'variance', 'saved_mean', 'saved_variance', 'x@GRAD',
            'scale@GRAD', 'bias@GRAD'
        ]
        os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = "1"


class TestBatchNormOpTrainingCase3(TestBatchNormOpTraining):
    def init_test_case(self):
        self.use_global_stats = False
        self.no_grad_set = set(['x@GRAD'])
        self.fetch_list = ['y', 'mean', 'variance', 'scale@GRAD', 'bias@GRAD']


class TestBatchNormOpTrainingMomentumVariable(TestBatchNormOpTraining):
    def init_test_case(self):
        self.use_momentum_variable = True
        self.use_global_stats = False
        self.no_grad_set = set()
        self.fetch_list = [
            'y', 'mean', 'variance', 'saved_mean', 'saved_variance', 'x@GRAD',
            'scale@GRAD', 'bias@GRAD'
        ]


class TestBatchNormOpFreezeStatsTraining(TestBatchNormOpTraining):
    def init_test_case(self):
        self.use_global_stats = True
        self.no_grad_set = set()
        self.fetch_list = [
            'y', 'mean', 'variance', 'x@GRAD', 'scale@GRAD', 'bias@GRAD'
        ]

    def reference_grad(self, x, y_grad, scale, mean, var, epsilon, data_format):
        if data_format == "NCHW":
            x = np.transpose(x, (0, 2, 3, 1))
            y_grad = np.transpose(y_grad, (0, 2, 3, 1))

        x_grad = scale * y_grad / np.sqrt(var + epsilon)
        grad_scale = np.sum(y_grad * (x - mean) / np.sqrt(var + epsilon),
                            axis=(0, 1, 2))
        grad_offset = np.sum(y_grad, axis=(0, 1, 2))

        # transfer back to N, C, H, W
        if data_format == "NCHW":
            x_grad = np.transpose(x_grad, (0, 3, 1, 2))
            x = np.transpose(x, (0, 3, 1, 2))
            y_grad = np.transpose(y_grad, (0, 3, 1, 2))

        return x_grad, grad_scale, grad_offset

    def ref_forward_backward(self, x, y_grad, scale, bias, mean, variance,
                             epsilon, momentum, shape, data_layout):
        if data_layout != "NCHW" and data_layout != "NHWC":
            raise ValueError("Unknown data order.")

        if data_layout == "NCHW":
            x = np.transpose(x, (0, 2, 3, 1))

        # run normalizaton
        normalized = (x - mean) / np.sqrt(variance + epsilon)
        y = normalized * scale + bias

        # transfer back to N, C, H, W
        if data_layout == "NCHW":
            x = np.transpose(x, (0, 3, 1, 2))
            y = np.transpose(y, (0, 3, 1, 2))

        mean_out = mean
        variance_out = variance
        saved_variance = 1. / np.sqrt(variance + epsilon)
        # run backward
        x_grad, scale_grad, bias_grad = self.reference_grad(
            x, y_grad, scale, mean, variance, epsilon, data_layout)

        return y, mean_out, variance_out, mean, saved_variance, x_grad, scale_grad, bias_grad


class TestBatchNormOpFreezeStatsAndScaleBiasTraining(
        TestBatchNormOpFreezeStatsTraining):
    def init_test_case(self):
        self.use_global_stats = True
        self.no_grad_set = set(['scale@GRAD', 'bias@GRAD'])
        self.fetch_list = ['y', 'mean', 'variance', 'x@GRAD']


class TestBatchNormOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # the input of batch_norm must be Variable.
            x1 = fluid.create_lod_tensor(
                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
            self.assertRaises(TypeError, fluid.layers.batch_norm, x1)

            # the input dtype of batch_norm must be float16 or float32 or float64
            # float16 only can be set on GPU place
            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="int32")
            self.assertRaises(TypeError, fluid.layers.batch_norm, x2)


class TestDygraphBatchNormAPIError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            batch_norm = fluid.dygraph.BatchNorm(10)
            # the input of BatchNorm must be Variable.
            x1 = fluid.create_lod_tensor(
                np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
            self.assertRaises(TypeError, batch_norm, x1)

            # the input dtype of BatchNorm must be float16 or float32 or float64
            # float16 only can be set on GPU place
            x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="int32")
            self.assertRaises(TypeError, batch_norm, x2)


class TestDygraphBatchNormTrainableStats(unittest.TestCase):
    def test_dygraph(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            shape = [4, 10, 4, 4]

            def compute(x, is_test, trainable_statistics):
                with fluid.dygraph.guard(p):
                    bn = fluid.dygraph.BatchNorm(
                        shape[1],
                        is_test=is_test,
                        trainable_statistics=trainable_statistics)
                    y = bn(fluid.dygraph.to_variable(x))
                return y.numpy()

            x = np.random.randn(*shape).astype("float32")
            y1 = compute(x, False, False)
            y2 = compute(x, True, True)
            self.assertTrue(np.allclose(y1, y2))

    def test_static(self):
        places = [fluid.CPUPlace()]
        if core.is_compiled_with_cuda():
            places.append(fluid.CUDAPlace(0))
        for p in places:
            exe = fluid.Executor(p)
            shape = [4, 10, 16, 16]

            def compute(x_np, is_test, trainable_statistics):
                with program_guard(Program(), Program()):
                    bn = fluid.dygraph.BatchNorm(
                        shape[1],
                        is_test=is_test,
                        trainable_statistics=trainable_statistics)
                    x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
                    y = bn(x)
                    exe.run(fluid.default_startup_program())
                    r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
                return r

            x = np.random.randn(*shape).astype("float32")
            y1 = compute(x, False, False)
            y2 = compute(x, True, True)
            self.assertTrue(np.allclose(y1, y2))


class TestDygraphBatchNormOpenReserveSpace(unittest.TestCase):
    def test_reservespace(self):
        with program_guard(Program(), Program()):
            paddle.enable_static()
            x = np.random.random(size=(3, 10, 3, 7)).astype('float32')
            x = fluid.data(name='x', shape=x.shape, dtype=x.dtype)
            # Set this FLAG, the BatchNorm API will pass "reserve_space" argument into batch_norm op.
            os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '1'
            batch_norm = fluid.dygraph.BatchNorm(7, data_layout="NHWC")
            hidden1 = batch_norm(x)
            os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '0'


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