# 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.
"""
Contrib layers just related to the neural network.
"""

from __future__ import print_function

import numpy as np
import six
import os
import inspect
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.layers import utils

__all__ = [
    'fused_elemwise_activation',
    'sequence_topk_avg_pooling',
    'var_conv_2d',
    'match_matrix_tensor',
    'tree_conv',
    'multiclass_nms2',
    'search_pyramid_hash',
]


def fused_elemwise_activation(x,
                              y,
                              functor_list,
                              axis=-1,
                              scale=0.0,
                              save_intermediate_out=True):
    """
    **Fused elementwise_add/mul and activation layers**

    This function computes an elementwise_add/mul cooperated with an activation.

    .. math::

        out = Unary(Binary(x, y))

    or

    .. math::

        out = Binary(x, Unary(y))

    Unary operators can be: `scale`, `relu`, `tanh`. Binary operators can be:
    `elementwise_add`, `elementwise_mul`.

    Args:
        x (Variable): left operation of the binary operator.
        y (Variable): right operator of the binary operator.
        functor_list (list of str): types of operator which will be executed
            by this layer. For example, ['elementwise_add', 'relu']
            (out = elementwise_add(x, relu(y))),
            or ['relu', 'elemmentwise_add'] (out = relu(elementwise_add(x, y))).
        axis (int32, default -1): axis of elementwise op.
        scale (float32, default 0): parameter of scale op.
        save_intermediate_out (bool, default True): whether to save the
            intermediate result, Unary(y) or Binary(x, y).

    Returns:
        Variable: The computation result.
    """
    if isinstance(functor_list, str):
        functor_list = functor_list.split(',')

    if not isinstance(functor_list, list) or len(functor_list) != 2:
        raise ValueError(
            'functor_list should be a list of str, and the length should be 2.')

    helper = LayerHelper('fused_elemwise_activation', **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    intermediate_out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='fused_elemwise_activation',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out,
                 'IntermediateOut': intermediate_out},
        attrs={
            'axis': axis,
            'scale': scale,
            'save_intermediate_out': save_intermediate_out,
            'functor_list': functor_list
        })
    return out


def var_conv_2d(input,
                row,
                col,
                input_channel,
                output_channel,
                filter_size,
                stride=1,
                param_attr=None,
                act=None,
                dtype='float32',
                name=None):
    """
    The var_conv_2d layer calculates the output base on the :attr:`input` with variable length,
    row, col, input channel, filter size and strides. Both :attr:`input`, :attr:`row`,
    and :attr:`col` are 1-level LodTensor. The covolution operation is same as conv2d layer with 
    padding. Besides, input.dims[1] should be 1. 

    .. code-block:: text
            
            If input_channel is 2 and given row lodTensor and col lodTensor as follows:
                row.lod = [[5, 4]]
                col.lod = [[6, 7]]
            input is a lodTensor: 
                input.lod = [[60, 56]]	# where 60 = input_channel * 5 * 6
                input.dims = [116, 1]	# where 116 = 60 + 56
            
            If set output_channel is 3, filter_size is [3, 3], stride is [1, 1]:
                output.lod = [[90, 84]] # where 90 = output_channel * [(5-1)/stride + 1] * [(6-1)/stride + 1]
                output.dims = [174, 1]  # where 174 = 90 + 84

    Args:
        input (Variable): The input shoud be 1-level LodTensor with dims[1] equals 1.
        row (Variable): The row shoud be 1-level LodTensor to provide height information.
        col (Variable): The col shoud be 1-level LodTensor to provide width information.
        input_channel (int): The number of input channel.
        output_channel (int): The number of output channel.
        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square.
        stride (int|tuple): The stride size. If stride is a tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of var_conv2d. If it is set to None or one attribute of ParamAttr, var_conv2d
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
            and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None
        dtype ('float32'): The data type of parameter and output.
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None

    Returns:
        Variable: Output variable with LoD specified by this layer.

    Examples:
        .. code-block:: python

            import numpy as np
            from paddle.fluid import layers
            from paddle.fluid import contrib

            x_lod_tensor = layers.data(name='x', shape=[1], lod_level=1)
            row_lod_tensor = layers.data(name='row', shape=[6], lod_level=1)
            col_lod_tensor = layers.data(name='col', shape=[6], lod_level=1)
            out = contrib.var_conv_2d(input=x_lod_tensor, 
                                     row=row_lod_tensor,
                                     col=col_lod_tensor,
                                     input_channel=3,
                                     output_channel=5,
                                     filter_size=[3, 3],
                                     stride=1)
    """
    helper = LayerHelper('var_conv_2d', **locals())
    x_shape = list(input.shape)
    assert len(x_shape) == 2

    filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
    stride = utils.convert_to_list(stride, 2, 'stride')

    filter_shape = [
        int(output_channel),
        int(input_channel) * filter_size[0] * filter_size[1]
    ]
    filter_param = helper.create_parameter(
        attr=helper.param_attr,
        shape=filter_shape,
        dtype=dtype, )

    conv_res = helper.create_variable_for_type_inference(dtype)
    tmp_res = helper.create_variable_for_type_inference(
        dtype, stop_gradient=True)

    helper.append_op(
        type='var_conv_2d',
        inputs={
            'X': input,
            'ROW': row,
            'COLUMN': col,
            'W': filter_param,
        },
        outputs={"Out": conv_res,
                 "Col": tmp_res},
        attrs={
            'InputChannel': input_channel,
            'OutputChannel': output_channel,
            'StrideH': stride[0],
            'StrideW': stride[1],
            'KernelH': filter_size[0],
            'KernelW': filter_size[1],
        })

    return helper.append_activation(conv_res)


def match_matrix_tensor(x,
                        y,
                        channel_num,
                        act=None,
                        param_attr=None,
                        dtype='float32',
                        name=None):
    """
    Calculate the semantic matching matrix of two word sequences with variable length.
    Given a query A of length `n` and a title B of length `m`, the input shape are respectively
    [n, h] and [m, h], which h is hidden_size. If :attr:`channel_num` is set to 3,
    it will generate a learnable parameter matrix W with shape [h, 3, h].
    Then the semantic matching matrix of query A and title B is calculated by 
    A * W * B.T = [n, h]*[h, 3, h]*[h, m] = [n, 3, m]. The learnable parameter matrix `W` 
    is equivalent to a fully connected layer in the calculation process. If :attr:`act` is provided, 
    the corresponding activation function will be applied to output matrix.
    The :attr:`x` and :attr:`y` should be LodTensor and only one level LoD is supported.

    .. code-block:: text

            Given a 1-level LoDTensor x:
                x.lod =  [[2,                     3,                               ]]
                x.data = [[0.3, 0.1], [0.2, 0.3], [0.5, 0.6], [0.7, 0.1], [0.3, 0.4]]
                x.dims = [5, 2]
            y is a Tensor:
                y.lod =  [[3,                                 1,       ]]
                y.data = [[0.1, 0.2], [0.3, 0.7], [0.9, 0.2], [0.4, 0.1]]
                y.dims = [4, 2]
            set channel_num 2, then we get a 1-level LoDTensor:
                out.lod =  [[12, 6]]   # where 12 = channel_num * x.lod[0][0] * y.lod[0][0]
                out.dims = [18, 1]     # where 18 = 12 + 6

    Args:
        x (Variable): Input variable x which should be 1-level LodTensor.
        y (Variable): Input variable y which should be 1-level LodTensor.
        channel_num (int): The channel number of learnable parameter W.
        act (str, default None): Activation to be applied to the output of this layer.
        param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
            parameters/weights of this layer.
        dtype ('float32'): The data type of w data.
        name (str|None): A name for this layer(optional). If set None, the layer will be named automatically. Default: None

    Returns:
        Variable: output with LoD specified by this layer.

    Examples:
        .. code-block:: python

            import numpy as np
            from paddle.fluid import layers
            from paddle.fluid import contrib

            x_lod_tensor = layers.data(name='x', shape=[10], lod_level=1)
            y_lod_tensor = layers.data(name='y', shape=[10], lod_level=1)
            out, out_tmp = contrib.match_matrix_tensor(x=x_lod_tensor, y=y_lod_tensor, channel_num=3)
    """
    helper = LayerHelper('match_matrix_tensor', **locals())

    x_shape = list(x.shape)
    y_shape = list(y.shape)
    assert len(x_shape) == 2 and len(y_shape) == 2 and x_shape[-1] == y_shape[
        -1]

    weight_shape = [x_shape[-1], channel_num, y_shape[-1]]
    w = helper.create_parameter(
        attr=helper.param_attr, shape=weight_shape, dtype=dtype, is_bias=False)
    mm_res = helper.create_variable_for_type_inference(dtype)
    tmp_res = helper.create_variable_for_type_inference(
        dtype, stop_gradient=True)
    helper.append_op(
        type='match_matrix_tensor',
        inputs={
            'X': x,
            'Y': y,
            'W': w,
        },
        outputs={"Out": mm_res,
                 "Tmp": tmp_res},
        attrs={'dim_t': channel_num})

    return helper.append_activation(mm_res), tmp_res


def sequence_topk_avg_pooling(input, row, col, topks, channel_num):
    """
    The :attr:`topks` is a list with incremental values in this function. For each topk,
    it will average the topk features as an output feature for each channel of every 
    input sequence. Both :attr:`row` and :attr:`col` are LodTensor, which provide height 
    and width information for :attr:`input` tensor. If feature size of input sequence is less 
    than topk, it will padding 0 at the back.

    .. code-block:: text

            If channel_num is 2 and given row LoDTensor and col LoDTensor as follows:
                row.lod = [[5, 4]]
                col.lod = [[6, 7]]

            input is a LoDTensor with input.lod[0][i] = channel_num * row.lod[0][i] * col.lod[0][i] 
                input.lod = [[60, 56]]  # where 60 = channel_num * 5 * 6
                input.dims = [116, 1]   # where 116 = 60 + 56

            If topks is [1, 3, 5], then we get a 1-level LoDTensor:
                out.lod =  [[5, 4]] 	# share Lod info with row LodTensor
                out.dims = [9, 6]   	# where 6 = len(topks) * channel_num

    Args:
        input (Variable): The input should be 2D LodTensor with dims[1] equals 1.
        row (Variable): The row shoud be 1-level LodTensor to provide the height information
                        of the input tensor data.
        col (Variable): The col shoud be 1-level LodTensor to provide the width information
                        of the input tensor data.
        topks (list): A list of incremental value to average the topk feature.
        channel_num (int): The number of input channel.

    Returns:
        Variable: output LodTensor specified by this layer.

    Examples:

        .. code-block:: python

            import numpy as np
            from paddle.fluid import layers
            from paddle.fluid import contrib

            x_lod_tensor = layers.data(name='x', shape=[1], lod_level=1)
            row_lod_tensor = layers.data(name='row', shape=[6], lod_level=1)
            col_lod_tensor = layers.data(name='col', shape=[6], lod_level=1)
            out = contrib.sequence_topk_avg_pooling(input=x_lod_tensor,
                                                   row=row_lod_tensor,
                                                   col=col_lod_tensor,
                                                   topks=[1, 3, 5],
                                                   channel_num=5)
    """
    helper = LayerHelper('sequence_topk_avg_pooling', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    pos = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype(), stop_gradient=True)
    helper.append_op(
        type='sequence_topk_avg_pooling',
        inputs={'X': input,
                'ROW': row,
                'COLUMN': col},
        outputs={'Out': out,
                 'pos': pos},
        attrs={'topks': topks,
               'channel_num': channel_num})

    return out


def tree_conv(nodes_vector,
              edge_set,
              output_size,
              num_filters=1,
              max_depth=2,
              act='tanh',
              param_attr=None,
              bias_attr=None,
              name=None):
    """ 
    ${comment}
    		
    Args:
        nodes_vector(${nodes_vector_type}): ${nodes_vector_comment}
        edge_set(${edge_set_type}): ${edge_set_comment}
        output_size(int): output feature width
        num_filters(int): number of filters, Default 1
        max_depth(int): max depth of filters, Default 2
        act(str): activation function, Default tanh
        param_attr(ParamAttr): the parameter attribute for the filters, Default None
        bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None
        name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None

    Returns:
        out(${out_type}): ${out_comment}

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid
          # 10 for max_node_size of dataset, 5 for vector width
          nodes_vector = fluid.layers.data(name='vectors', shape=[10, 5], dtype='float32')
          # 10 for max_node_size of dataset, 2 for every edge has two nodes
          # edges must be directional
          edge_set = fluid.layers.data(name='edge_set', shape=[10, 2], dtype='float32')
          # the shape of output will be [10, 6, 1],
          # 10 for max_node_size of dataset, 6 for output size, 1 for 1 filter
          out_vector = fluid.layers.tree_conv(nodes_vector, edge_set, 6, 1, 2)
          # After reshape, output tensor could be nodes_vector for next tree convolution
          out_vector = fluid.layers.reshape(out_vector, shape=[-1, 10, 6])
          out_vector_2 = fluid.layers.tree_conv(out_vector, edge_set, 3, 4, 2)
          # also output tensor could be pooling(the pooling in paper called global pooling)
          pooled = fluid.layers.reduce_max(out_vector, dim=2) # global pooling
    """
    helper = LayerHelper("tree_conv", **locals())
    dtype = helper.input_dtype('nodes_vector')
    feature_size = nodes_vector.shape[2]
    W_shape = [feature_size, 3, output_size, num_filters]
    W = helper.create_parameter(
        attr=param_attr, shape=W_shape, dtype=dtype, is_bias=False)
    out = helper.create_variable_for_type_inference(dtype=dtype)
    helper.append_op(
        type='tree_conv',
        inputs={'NodesVector': nodes_vector,
                'EdgeSet': edge_set,
                'Filter': W},
        outputs={'Out': out, },
        attrs={'max_depth': max_depth})
    if helper.bias_attr:
        pre_activation = helper.append_bias_op(out)
    else:
        pre_activation = out
    return helper.append_activation(pre_activation)


def multiclass_nms2(bboxes,
                    scores,
                    score_threshold,
                    nms_top_k,
                    keep_top_k,
                    nms_threshold=0.3,
                    normalized=True,
                    nms_eta=1.,
                    background_label=0,
                    return_index=False,
                    name=None):
    """
    **Multiclass NMS2**
    
    This operator is to do multi-class non maximum suppression (NMS) on
    boxes and scores.

    In the NMS step, this operator greedily selects a subset of detection bounding
    boxes that have high scores larger than score_threshold, if providing this
    threshold, then selects the largest nms_top_k confidences scores if nms_top_k
    is larger than -1. Then this operator pruns away boxes that have high IOU
    (intersection over union) overlap with already selected boxes by adaptive
    threshold NMS based on parameters of nms_threshold and nms_eta.

    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
    per image if keep_top_k is larger than -1.

    Args:
        bboxes (Variable): Two types of bboxes are supported:
                           1. (Tensor) A 3-D Tensor with shape
                           [N, M, 4 or 8 16 24 32] represents the
                           predicted locations of M bounding bboxes,
                           N is the batch size. Each bounding box has four
                           coordinate values and the layout is 
                           [xmin, ymin, xmax, ymax], when box size equals to 4.
                           2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
                           M is the number of bounding boxes, C is the 
                           class number   
        scores (Variable): Two types of scores are supported:
                           1. (Tensor) A 3-D Tensor with shape [N, C, M]
                           represents the predicted confidence predictions.
                           N is the batch size, C is the class number, M is 
                           number of bounding boxes. For each category there 
                           are total M scores which corresponding M bounding
                           boxes. Please note, M is equal to the 2nd dimension
                           of BBoxes.
                           2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
                           M is the number of bbox, C is the class number.
                           In this case, input BBoxes should be the second
                           case with shape [M, C, 4].
        background_label (int): The index of background label, the background 
                                label will be ignored. If set to -1, then all
                                categories will be considered. Default: 0
        score_threshold (float): Threshold to filter out bounding boxes with
                                 low confidence score. If not provided, 
                                 consider all boxes.
        nms_top_k (int): Maximum number of detections to be kept according to
                         the confidences aftern the filtering detections based
                         on score_threshold.
        nms_threshold (float): The threshold to be used in NMS. Default: 0.3
        nms_eta (float): The threshold to be used in NMS. Default: 1.0
        keep_top_k (int): Number of total bboxes to be kept per image after NMS
                          step. -1 means keeping all bboxes after NMS step.
        normalized (bool): Whether detections are normalized. Default: True
        return_index(bool): Whether return selected index. Default: False
        name(str): Name of the multiclass nms op. Default: None.

    Returns:
        A tuple with two Variables: (Out, Index) if return_index is True,
        otherwise, a tuple with one Variable(Out) is returned. 

        Out: A 2-D LoDTensor with shape [No, 6] represents the detections. 
        Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax] 
        or A 2-D LoDTensor with shape [No, 10] represents the detections. 
        Each row has 10 values: [label, confidence, x1, y1, x2, y2, x3, y3, 
        x4, y4]. No is the total number of detections. 

        If all images have not detected results, all elements in LoD will be
        0, and output tensor is empty (None).

        Index: Only return when return_index is True. A 2-D LoDTensor with 
        shape [No, 1] represents the selected index which type is Integer. 
        The index is the absolute value cross batches. No is the same number 
        as Out. If the index is used to gather other attribute such as age, 
        one needs to reshape the input(N, M, 1) to (N * M, 1) as first, where 
        N is the batch size and M is the number of boxes.


    Examples:
        .. code-block:: python


            import paddle.fluid as fluid
            boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
                                      dtype='float32', lod_level=1)
            scores = fluid.layers.data(name='scores', shape=[81],
                                      dtype='float32', lod_level=1)
            out, index = fluid.layers.multiclass_nms2(bboxes=boxes,
                                              scores=scores,
                                              background_label=0,
                                              score_threshold=0.5,
                                              nms_top_k=400,
                                              nms_threshold=0.3,
                                              keep_top_k=200,
                                              normalized=False,
                                              return_index=True)
    """
    helper = LayerHelper('multiclass_nms2', **locals())

    output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
    index = helper.create_variable_for_type_inference(dtype='int')
    helper.append_op(
        type="multiclass_nms2",
        inputs={'BBoxes': bboxes,
                'Scores': scores},
        attrs={
            'background_label': background_label,
            'score_threshold': score_threshold,
            'nms_top_k': nms_top_k,
            'nms_threshold': nms_threshold,
            'nms_eta': nms_eta,
            'keep_top_k': keep_top_k,
            'nms_eta': nms_eta,
            'normalized': normalized
        },
        outputs={'Out': output,
                 'Index': index})
    output.stop_gradient = True
    index.stop_gradient = True

    if return_index:
        return output, index
    return output


def search_pyramid_hash(input,
                        num_emb,
                        space_len,
                        pyramid_layer,
                        rand_len,
                        drop_out_percent,
                        is_training,
                        use_filter,
                        white_list_len,
                        black_list_len,
                        seed,
                        lr,
                        param_attr=None,
                        param_attr_wl=None,
                        param_attr_bl=None,
                        name=None,
                        dtype='float32'):
    """
    **Pyramid hash embedding**
    Args:
        input (Variable): LoDTensor<int32> Variable contained the IDs' information.
        num_emb (int): The embedding size of output.
        space_len (int): The length of pyramid hash embedding space.
        pyramid_layer (int): The number of pyramid layers. It should be greater than 2.
        rand_len (int): The minimum length of pyramid hash cell.
        drop_out_percent (float): The probability of dropping out the input token randomly.
            It should satisfy: [0., 1.]
        is_training (bool): Whether in training or testing phrase.
        use_filter(bool): If set True, the white filter and black filter should be given by
            :attr:`param_attr_wl` and :attr:`param_attr_bl` .
        white_list_len(int): If set :math:`white_list_len>0` , white filter with shape [white_list_len, 1]
            should be provided by param_attr_wl.
        black_list_len(int): If set :math:`black_list_len>0` , black filter with shape [black_list_len, 1]
            should be provided by param_attr_bl.
        seed(int): The number of random seed.
        lr(float): The learning rate of weight created by :attr:`param_attr` with shape [space_len+rand_len, 1]
            in this layer.
        param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
            default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` .
        param_attr_wl(ParamAttr): Specified parameters of white filter.
        param_attr_bl(ParamAttr): Specified parameters of black filter.
        name(str, optional): The default value is None.  Normally there is no need for user to set this property.
            For more information, please refer to :ref:`api_guide_Name` .
        dtype(str): The data type of output variable, float32.
    Returns:
        Variable: LoDTensor of pyramid hash embedding.
    """
    helper = LayerHelper('search_pyramid_hash', **locals())

    w_shape = [space_len + rand_len, 1]
    w = helper.create_parameter(
        attr=param_attr, shape=w_shape, dtype=dtype, is_bias=False)
    w.stop_gradient = True

    input_vars = {'X': input, 'W': w}
    if white_list_len > 0:
        wl_shape = [white_list_len, 1]
        white_list = helper.create_parameter(
            attr=param_attr_wl, shape=wl_shape, dtype=dtype, is_bias=False)
        white_list.stop_gradient = True
        input_vars['WhiteList'] = white_list

    if black_list_len >= 0:
        bl_shape = [black_list_len, 1]
        black_list = helper.create_parameter(
            attr=param_attr_bl, shape=bl_shape, dtype=dtype, is_bias=False)
        black_list.stop_gradient = True
        input_vars['BlackList'] = black_list

    res = helper.create_variable_for_type_inference(dtype)
    drop_pos = helper.create_variable_for_type_inference(dtype)
    x_temp_out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='pyramid_hash',
        inputs=input_vars,
        outputs={"Out": res,
                 "X_Temp_Out": x_temp_out,
                 'DropPos': drop_pos},
        attrs={
            'num_emb': num_emb,
            'space_len': space_len,
            'pyramid_layer': pyramid_layer,
            'rand_len': rand_len,
            'drop_out_percent': drop_out_percent,
            'is_training': is_training,
            'use_filter': use_filter,
            'white_list_len': white_list_len,
            'black_list_len': black_list_len,
            'seed': seed,
            'lr': lr,
        })

    return res
