#! /usr/bin/python
# -*- coding: utf-8 -*-
import dragon as D
from dragon.core.ops import vision_ops
from dragon.core.ops import activation_ops


def padding_format(padding):
    """
    Checks that the padding format correspond format.

    Parameters
    ----------
    padding : str
        Must be one of the following:"same", "SAME", "VALID", "valid"

    Returns
    -------
        str "SAME" or "VALID"
    """

    if padding in ["SAME", "same"]:
        padding = "SAME"
    elif padding in ["VALID", "valid"]:
        padding = "VALID"
    elif padding == None:
        padding = None
    else:
        raise Exception("Unsupported padding: " + str(padding))
    return padding


def preprocess_1d_format(data_format, padding):
    """
    Checks that the 1-D dataformat format correspond format.

    Parameters
    ----------
    data_format : str
        Must be one of the following:"channels_last","NWC","NCW","channels_first"
    padding : str
        Must be one of the following:"same","valid","SAME","VALID"

    Returns
    -------
        str "NWC" or "NCW" and "SAME" or "VALID"
    """

    if data_format in ["channels_last", "NWC"]:
        data_format = "NWC"
    elif data_format in ["channels_first", "NCW"]:
        data_format = "NCW"
    elif data_format == None:
        data_format = None
    else:
        raise Exception("Unsupported data format: " + str(data_format))
    padding = padding_format(padding)
    return data_format, padding


def preprocess_2d_format(data_format, padding):
    """
    Checks that the 2-D dataformat format correspond format.

    Parameters
    ----------
    data_format : str
        Must be one of the following:"channels_last","NHWC","NCHW","channels_first"
    padding : str
        Must be one of the following:"same","valid","SAME","VALID"

    Returns
    -------
        str "NHWC" or "NCHW" and "SAME" or "VALID"
    """

    if data_format in ["channels_last", "NHWC", "nhwc"]:
        data_format = "NHWC"
    elif data_format in ["channels_first", "NCHW", "nchw"]:
        data_format = "NCHW"
    elif data_format == None:
        data_format = None
    else:
        raise Exception("Unsupported data format: " + str(data_format))
    padding = padding_format(padding)
    return data_format, padding


def preprocess_3d_format(data_format, padding):
    """
    Checks that the 3-D dataformat format correspond format.

    Parameters
    ----------
    data_format : str
        Must be one of the following:"channels_last","NDHWC","NCDHW","channels_first"
    padding : str
        Must be one of the following:"same","valid","SAME","VALID"

    Returns
    -------
        str "NDHWC" or "NCDHW" and "SAME" or "VALID"
    """

    if data_format in ['channels_last', 'NDHWC']:
        data_format = 'NDHWC'
    elif data_format in ['channels_first', 'NCDHW']:
        data_format = 'NCDHW'
    elif data_format == None:
        data_format = None
    else:
        raise Exception("Unsupported data format: " + str(data_format))
    padding = padding_format(padding)
    return data_format, padding


def nchw_to_nhwc(x):
    """
    Channels first to channels last

    Parameters
    ----------
    x : tensor
        channels first tensor data

    Returns
    -------
        channels last tensor data
    """

    pass


def nhwc_to_nchw(x):
    """
    Channles last to channels first

    Parameters
    ----------
    x : tensor
        channels last tensor data

    Returns
    -------
        channels first tensor data
    """

    pass


class ReLU(object):

    def __init__(self):
        pass

    def __call__(self, x):
        return D.nn.relu(x)


def relu(x):
    """
    Computes rectified linear: max(features, 0).

    Parameters
    ----------
    x : tensor
        Must be one of the following types: float32, float64, int32, uint8, int16,
        int8, int64, bfloat16, uint16, half, uint32, uint64, qint8.

    Returns
    -------
        A Tensor. Has the same type as features.
    """
    return D.nn.relu(x)


class ReLU6(object):

    def __init__(self):
        pass

    def __call__(self, x):
        return D.nn.relu6(x)


def relu6(x):
    """
    Computes Rectified Linear 6: min(max(features, 0), 6).

    Parameters
    ----------
    x : tensor
        Must be one of the following types: float32, float64, int32, uint8, int16,
        int8, int64, bfloat16, uint16, half, uint32, uint64, qint8.

    Returns
    -------
        A Tensor with the same type as features.
    """
    return D.nn.relu6(x)


class LeakyReLU(object):

    def __init__(self, alpha=0.2):
        self.alpha = alpha

    def __call__(self, x):
        return D.nn.leaky_relu(x, alpha=self.alpha)


def leaky_relu(x):
    """
    Compute the Leaky ReLU activation function.

    Parameters
    ----------
    x : tensor
        representing preactivation values. Must be one of the following types:
        float16, float32, float64, int32, int64.

    Returns
    -------
        The activation value.
    """

    return D.nn.leaky_relu(x)


class Softplus(object):

    def __init__(self):
        pass

    def __call__(self, x):
        raise NotImplementedError


def softplus(x):
    """
    Computes softplus: log(exp(features) + 1).

    Parameters
    ----------
    x : tensor
        Must be one of the following types: half, bfloat16, float32, float64.

    Returns
    -------
        A Tensor. Has the same type as features.
    """

    raise NotImplementedError


class Tanh(object):

    def __init__(self):
        pass

    def __call__(self, x):
        return activation_ops.tanh(x)


def tanh(x):
    """
    Computes hyperbolic tangent of x element-wise.

    Parameters
    ----------
    x : tensor
        Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.

    Returns
    -------
        A Tensor. Has the same type as x.
    """

    return activation_ops.tanh(x)


class Sigmoid(object):

    def __init__(self):
        pass

    def __call__(self, x):
        return activation_ops.sigmoid(x)


def sigmoid(x):
    """
    Computes sigmoid of x element-wise.

    Parameters
    ----------
    x : tensor
        A Tensor with type float16, float32, float64, complex64, or complex128.

    Returns
    -------
        A Tensor with the same type as x.
    """
    return activation_ops.sigmoid(x)


class Softmax(object):

    def __init__(self):
        pass

    def __call__(self, x):
        return D.nn.softmax(x)


def softmax(logits, axis=None):
    """
    Computes softmax activations.

    Parameters
    ----------
    logits : tensor
        Must be one of the following types: half, float32, float64.
    axis : int
        The dimension softmax would be performed on. The default is -1 which indicates the last dimension.

    Returns
    -------
        A Tensor. Has the same type and shape as logits.
    """
    return D.nn.softmax(logits)


class Dropout(object):

    def __init__(self, keep, seed=1):
        self.keep = 1 - keep
        self.seed = seed

    def __call__(self, inputs):
        return D.nn.dropout(inputs, prob=self.keep)


class BiasAdd(object):
    """
    Adds bias to value.

    Parameters
    ----------
    x : tensor
        A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
    bias : tensor
        Must be the same type as value unless value is a quantized type,
        in which case a different quantized type may be used.
    Returns
    -------
        A Tensor with the same type as value.
    """

    def __init__(self, data_format='NHWC'):
        self.data_format = data_format

    def __call__(self, x, bias):
        inputs = [x, bias]
        return vision_ops.bias_add(inputs, data_format=self.data_format)


def bias_add(x, bias):
    """
    Adds bias to value.

    Parameters
    ----------
    x : tensor
        A Tensor with type float, double, int64, int32, uint8, int16, int8, complex64, or complex128.
    bias : tensor
        Must be the same type as value unless value is a quantized type,
        in which case a different quantized type may be used.
    data_format : A string.
        'N...C' and 'NC...' are supported.
    name : str
        A name for the operation (optional).
    Returns
    -------
        A Tensor with the same type as value.
    """
    inputs = [x, bias]
    return vision_ops.bias_add(inputs, data_format='NHWC')


class Conv1D(object):
    pass
    # raise NotImplementedError


def conv1d(input, filters, stride, padding, data_format='NWC', dilations=None, name=None):
    """
    Computes a 1-D convolution given 3-D input and filter tensors.

    Parameters
    ----------
    input : tensor
        A 3D Tensor. Must be of type float16, float32, or float64
    filters : tensor
        A 3D Tensor. Must have the same type as input.
    stride : int of list
         An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
    padding : string
         'SAME' or 'VALID'
    data_format : string
        An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of
        [batch, in_width, in_channels]. The "NCW" format stores data as [batch, in_channels, in_width].
    dilations : int or list
        An int or list of ints that has length 1 or 3 which defaults to 1.
        The dilation factor for each dimension of input. If set to k > 1,
        there will be k-1 skipped cells between each filter element on that dimension.
        Dilations in the batch and depth dimensions must be 1.
    name : string
        A name for the operation (optional).
    Returns
    -------
        A Tensor. Has the same type as input.
    """

    pass


class Conv2D(object):

    def __init__(self, strides, padding, data_format='NHWC', dilations=None, out_channel=None, k_size=None):
        self.data_format, self.padding = preprocess_2d_format(data_format, padding)
        self.ksize = k_size[0]
        if self.data_format is 'NHWC':
            self.dg_stride = strides[1]
            self.dg_dilation = dilations[1]
        elif self.data_format is 'NCHW':
            self.dg_stride = strides[2]
            self.dg_dilation = dilations[2]

    def __call__(self, inputs, filters):
        outputs = vision_ops.conv2d(
            [inputs, filters],
            kernel_shape=self.ksize,
            strides=self.dg_stride,
            padding=self.padding,
            dilations=self.dg_dilation,
            data_format=self.data_format,
        )
        return outputs


def conv2d(input, filters, strides, padding, data_format='NCHW', dilations=None):
    """
    Computes a 2-D convolution given 4-D input and filters tensors.

    Parameters
    ----------
    input : tensor
        Must be one of the following types: half, bfloat16, float32, float64. A 4-D tensor.
        The dimension order is interpreted according to the value of data_format, see below for details.
    filters : tensor
         Must have the same type as input. A 4-D tensor of shape [filter_height, filter_width, in_channels, out_channels]
    strides : int of list
        The stride of the sliding window for each dimension of input. If a single value is given it is replicated in the H and W dimension.
        By default the N and C dimensions are set to 1. The dimension order is determined by the value of data_format, see below for details.
    padding : string
        "SAME" or "VALID"
    data_format : string
        "NHWC", "NCHW". Defaults to "NCHW".
    dilations : list or ints
        list of ints that has length 1, 2 or 4, defaults to 1. The dilation factor for each dimension ofinput.

    Returns
    -------
        A Tensor. Has the same type as input.
    """
    raise NotImplementedError


class Conv3D(object):
    pass
    # raise NotImplementedError


def conv3d(input, filters, strides, padding, data_format='NDHWC', dilations=None, name=None):
    """
    Computes a 3-D convolution given 5-D input and filters tensors.

    Parameters
    ----------
    input : tensor
        Must be one of the following types: half, bfloat16, float32, float64.
        Shape [batch, in_depth, in_height, in_width, in_channels].
    filters : tensor
        Must have the same type as input. Shape [filter_depth, filter_height, filter_width, in_channels, out_channels].
        in_channels must match between input and filters.
    strides : list of ints
        A list of ints that has length >= 5. 1-D tensor of length 5.
        The stride of the sliding window for each dimension of input.
        Must have strides[0] = strides[4] = 1.
    padding : string
        A string from: "SAME", "VALID". The type of padding algorithm to use.
    data_format : string
        An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data.
        With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels].
        Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
    dilations : list of ints
        Defaults to [1, 1, 1, 1, 1]. 1-D tensor of length 5. The dilation factor for each dimension of input.
        If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension.
        The dimension order is determined by the value of data_format, see above for details.
        Dilations in the batch and depth dimensions must be 1.
    name : string
        A name for the operation (optional).

    Returns
    -------
        A Tensor. Has the same type as input.
    """

    raise NotImplementedError


def lrn(inputs, depth_radius, bias, alpha, beta):
    """
    Local Response Normalization.

    Parameters
    ----------
    inputs : tensor
        Must be one of the following types: half, bfloat16, float32. 4-D.
    depth_radius : int
        Defaults to 5. 0-D. Half-width of the 1-D normalization window.
    bias : float
        Defaults to 1. An offset (usually positive to avoid dividing by 0).
    alpha : float
        Defaults to 1. A scale factor, usually positive.
    beta : float
         Defaults to 0.5. An exponent.

    Returns
    -------
        A Tensor. Has the same type as input.
    """
    pass


def moments(x, axes, shift=None, keepdims=False):
    """
    Calculates the mean and variance of x.

    Parameters
    ----------
    x : tensor
        A Tensor
    axes : ints
        Axes along which to compute mean and variance.
    shift : int
        Not used in the current implementation.
    keepdims : bool
        produce moments with the same dimensionality as the input.

    Returns
    -------
        Two Tensor objects: mean and variance.
    """

    pass


class MaxPool(object):

    def __init__(self, ksize, strides, padding, data_format=None):
        self.data_format, self.padding = preprocess_2d_format(data_format, padding)
        self.ksize = ksize
        self.strides = strides

    def __call__(self, inputs):
        return vision_ops.pool2d(
            inputs,
            kernel_shape=self.ksize,
            strides=self.strides,
            padding=self.padding,
            mode='MAX',
            global_pooling=False,
            data_format=self.data_format,
        )


def max_pool(input, ksize, strides, padding, data_format=None):
    """
    Performs the max pooling on the input.

    Parameters
    ----------
    input : tensor
        Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels] if data_format does not start
        with "NC" (default), or [batch_size, num_channels] + input_spatial_shape if data_format starts with "NC".
        Pooling happens over the spatial dimensions only.
    ksize : int or list of ints
        An int or list of ints that has length 1, N or N+2.
        The size of the window for each dimension of the input tensor.
    strides : int or list of ints
        An int or list of ints that has length 1, N or N+2.
        The stride of the sliding window for each dimension of the input tensor.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.

    Returns
    -------
        A Tensor of format specified by data_format. The max pooled output tensor.
    """
    pass


class AvgPool(object):

    def __init__(self, ksize, strides, padding, data_format=None):
        self.data_format, self.padding = preprocess_2d_format(data_format, padding)
        self.filter_size = ksize
        self.strides = strides

    def __call__(self, inputs):
        return vision_ops.pool2d(
            inputs,
            kernel_shape=self.filter_size,
            strides=self.strides,
            padding=self.padding,
            mode='AVG',
            global_pooling=False,
            data_format=self.data_format,
        )


def avg_pool(input, ksize, strides, padding):
    """
    Performs the avg pooling on the input.

    Parameters
    ----------
    input : tensor
        Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels]
        if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape
        if data_format starts with "NC". Pooling happens over the spatial dimensions only.
    ksize : int or list of ints
        An int or list of ints that has length 1, N or N+2.
        The size of the window for each dimension of the input tensor.
    strides : int or list of ints
        An int or list of ints that has length 1, N or N+2.
        The stride of the sliding window for each dimension of the input tensor.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.

    Returns
    -------
        A Tensor of format specified by data_format. The average pooled output tensor.
    """
    pass


def max_pool3d(input, ksize, strides, padding, data_format=None, name=None):
    """
    Performs the max pooling on the input.

    Parameters
    ----------
    input : tensor
         A 5-D Tensor of the format specified by data_format.
    ksize : int or list of ints
        An int or list of ints that has length 1, 3 or 5.
        The size of the window for each dimension of the input tensor.
    strides : int or list of ints
        An int or list of ints that has length 1, 3 or 5.
        The stride of the sliding window for each dimension of the input tensor.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.
    data_format : string
         "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data.
         With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels].
         Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
    name : string
         A name for the operation (optional).

    Returns
    -------
        A Tensor of format specified by data_format. The max pooled output tensor.
    """
    pass


def avg_pool3d(input, ksize, strides, padding, data_format=None, name=None):
    """
    Performs the average pooling on the input.

    Parameters
    ----------
    input : tensor
        A 5-D Tensor of shape [batch, height, width, channels] and type float32, float64, qint8, quint8, or qint32.
    ksize : int or list of ints
        An int or list of ints that has length 1, 3 or 5. The size of the window for each dimension of the input tensor.
    strides : int or list of ints
        An int or list of ints that has length 1, 3 or 5.
        The stride of the sliding window for each dimension of the input tensor.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.
    data_format : string
        'NDHWC' and 'NCDHW' are supported.
    name : string
        Optional name for the operation.

    Returns
    -------
        A Tensor with the same type as value. The average pooled output tensor.
    """
    pass


def pool(input, window_shape, pooling_type, strides=None, padding='VALID', data_format=None, dilations=None, name=None):
    """
    Performs an N-D pooling operation.

    Parameters
    ----------
    input : tensor
        Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels]
        if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape
        if data_format starts with "NC". Pooling happens over the spatial dimensions only.
    window_shape : int
        Sequence of N ints >= 1.
    pooling_type : string
        Specifies pooling operation, must be "AVG" or "MAX".
    strides : ints
        Sequence of N ints >= 1. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1.
    padding : string
        The padding algorithm, must be "SAME" or "VALID". Defaults to "SAME".
        See the "returns" section of tf.ops.convolution for details.
    data_format : string
        Specifies whether the channel dimension of the input and output is the last dimension (default, or if data_format does not start with "NC"),
        or the second dimension (if data_format starts with "NC").
        For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW".
        For N=3, the valid values are "NDHWC" (default) and "NCDHW".
    dilations : list of ints
        Dilation rate. List of N ints >= 1. Defaults to [1]*N. If any value of dilation_rate is > 1, then all values of strides must be 1.
    name : string
        Optional. Name of the op.

    Returns
    -------
        Tensor of rank N+2, of shape [batch_size] + output_spatial_shape + [num_channels]
    """
    pass


class DepthwiseConv2d(object):

    def __init__(self, strides, padding, data_format=None, dilations=None, ksize=None, channel_multiplier=1):
        self.data_format, self.padding = preprocess_2d_format(data_format, padding)
        self.stride = strides
        self.dilations = dilations

    def __call__(self, input, filter):
        raise NotImplementedError("Not implemented depthwiseconv2d")


def depthwise_conv2d(input, filter, strides, padding, data_format=None, dilations=None, name=None):
    """
    Depthwise 2-D convolution.

    Parameters
    ----------
    input : tensor
        4-D with shape according to data_format.
    filter : tensor
        4-D with shape [filter_height, filter_width, in_channels, channel_multiplier].
    strides : list
        1-D of size 4. The stride of the sliding window for each dimension of input.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.
    data_format : string
        The data format for input. Either "NHWC" (default) or "NCHW".
    dilations : list
        1-D of size 2. The dilation rate in which we sample input values across the height and width dimensions in atrous convolution.
        If it is greater than 1, then all values of strides must be 1.
    name : string
        A name for this operation (optional).

    Returns
    -------
        A 4-D Tensor with shape according to data_format.
        E.g., for "NHWC" format, shape is [batch, out_height, out_width, in_channels * channel_multiplier].
    """

    pass


def conv1d_transpose(
    input, filters, output_shape, strides, padding='SAME', data_format='NWC', dilations=None, name=None
):
    """
    The transpose of conv1d.

    Parameters
    ----------
    input : tensor
        A 3-D Tensor of type float and shape [batch, in_width, in_channels]
        for NWC data format or [batch, in_channels, in_width] for NCW data format.
    filters : tensor
        A 3-D Tensor with the same type as value and shape [filter_width, output_channels, in_channels].
        filter's in_channels dimension must match that of value.
    output_shape : tensor
        A 1-D Tensor, containing three elements, representing the output shape of the deconvolution op.
    strides : list
        An int or list of ints that has length 1 or 3. The number of entries by which the filter is moved right at each step.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.
    data_format : string
        'NWC' and 'NCW' are supported.
    dilations : list
         An int or list of ints that has length 1 or 3 which defaults to 1.
         The dilation factor for each dimension of input. If set to k > 1,
         there will be k-1 skipped cells between each filter element on that dimension.
         Dilations in the batch and depth dimensions must be 1.
    name : string
        Optional name for the returned tensor.

    Returns
    -------
        A Tensor with the same type as value.
    """
    pass


def conv2d_transpose(
    input, filters, output_shape, strides, padding='SAME', data_format='NHWC', dilations=None, name=None
):
    """
    The transpose of conv2d.

    Parameters
    ----------
    input : tensor
        A 4-D Tensor of type float and shape [batch, height, width, in_channels]
        for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
    filters : tensor
        A 4-D Tensor with the same type as input and shape [height, width,
        output_channels, in_channels]. filter's in_channels dimension must match that of input.
    output_shape : tensor
        A 1-D Tensor representing the output shape of the deconvolution op.
    strides : list
        An int or list of ints that has length 1, 2 or 4. The stride of the sliding window for each dimension of input.
        If a single value is given it is replicated in the H and W dimension.
        By default the N and C dimensions are set to 0.
        The dimension order is determined by the value of data_format, see below for details.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.
    data_format : string
         'NHWC' and 'NCHW' are supported.
    dilations : list
        An int or list of ints that has length 1, 2 or 4, defaults to 1.
    name : string
        Optional name for the returned tensor.

    Returns
    -------
        A Tensor with the same type as input.
    """
    pass


def conv3d_transpose(
    input, filters, output_shape, strides, padding='SAME', data_format='NDHWC', dilations=None, name=None
):
    """
    The transpose of conv3d.

    Parameters
    ----------
    input : tensor
         A 5-D Tensor of type float and shape [batch, height, width, in_channels] for
         NHWC data format or [batch, in_channels, height, width] for NCHW data format.
    filters : tensor
        A 5-D Tensor with the same type as value and shape [height, width, output_channels, in_channels].
        filter's in_channels dimension must match that of value.
    output_shape : tensor
        A 1-D Tensor representing the output shape of the deconvolution op.
    strides : list
        An int or list of ints that has length 1, 3 or 5.
    padding : string
        'VALID' or 'SAME'. The padding algorithm. See the "returns" section of tf.ops.convolution for details.
    data_format : string
        'NDHWC' and 'NCDHW' are supported.
    dilations : list of ints
        An int or list of ints that has length 1, 3 or 5, defaults to 1.
    name : string
        Optional name for the returned tensor.

    Returns
    -------
        A Tensor with the same type as value.
    """

    pass


class BatchNorm(object):

    def __init__(self):
        pass

    def __call__(self, *args, **kwargs):
        pass