# -*- coding: utf-8 -*-
from __future__ import absolute_import

from .. import backend as K
from .. import activations
from .. import initializers
from .. import regularizers
from .. import constraints
from ..engine import Layer
from ..engine import InputSpec
from ..utils import conv_utils


class LocallyConnected1D(Layer):
    """Locally-connected layer for 1D inputs.

    The `LocallyConnected1D` layer works similarly to
    the `Conv1D` layer, except that weights are unshared,
    that is, a different set of filters is applied at each different patch
    of the input.

    # Example
    ```python
        # apply a unshared weight convolution 1d of length 3 to a sequence with
        # 10 timesteps, with 64 output filters
        model = Sequential()
        model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
        # now model.output_shape == (None, 8, 64)
        # add a new conv1d on top
        model.add(LocallyConnected1D(32, 3))
        # now model.output_shape == (None, 6, 32)
    ```

    # Arguments
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of a single integer,
            specifying the length of the 1D convolution window.
        strides: An integer or tuple/list of a single integer,
            specifying the stride length of the convolution.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: One of `"valid"` or `"same"` (case-insensitive).
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to the kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        3D tensor with shape: `(batch_size, steps, input_dim)`

    # Output shape
        3D tensor with shape: `(batch_size, new_steps, filters)`
        `steps` value might have changed due to padding or strides.
    """

    def __init__(self, filters,
                 kernel_size,
                 strides=1,
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(LocallyConnected1D, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        if self.padding != 'valid':
            raise ValueError('Invalid border mode for LocallyConnected1D '
                             '(only "valid" is supported): ' + padding)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.input_spec = InputSpec(ndim=3)

    def build(self, input_shape):
        input_dim = input_shape[2]
        if input_dim is None:
            raise ValueError('Axis 2 of input should be fully-defined. '
                             'Found shape:', input_shape)
        output_length = conv_utils.conv_output_length(input_shape[1],
                                                      self.kernel_size[0],
                                                      self.padding,
                                                      self.strides[0])
        self.kernel_shape = (output_length,
                             self.kernel_size[0] * input_dim,
                             self.filters)
        self.kernel = self.add_weight(
            self.kernel_shape,
            initializer=self.kernel_initializer,
            name='kernel',
            regularizer=self.kernel_regularizer,
            constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(
                (output_length, self.filters),
                initializer=self.bias_initializer,
                name='bias',
                regularizer=self.bias_regularizer,
                constraint=self.bias_constraint)
        else:
            self.bias = None
        self.input_spec = InputSpec(ndim=3, axes={2: input_dim})
        self.built = True

    def compute_output_shape(self, input_shape):
        length = conv_utils.conv_output_length(input_shape[1],
                                               self.kernel_size[0],
                                               self.padding,
                                               self.strides[0])
        return (input_shape[0], length, self.filters)

    def call(self, inputs):
        stride = self.strides[0]
        output_length, feature_dim, filters = self.kernel_shape

        xs = []
        for i in range(output_length):
            slice_length = slice(i * stride,
                                 i * stride + self.kernel_size[0])
            xs.append(K.reshape(inputs[:, slice_length, :],
                                (1, -1, feature_dim)))
        x_aggregate = K.concatenate(xs, axis=0)
        # Shape: `(output_length, batch_size, filters)`.
        output = K.batch_dot(x_aggregate, self.kernel)
        output = K.permute_dimensions(output, (1, 0, 2))

        if self.use_bias:
            output += K.reshape(self.bias, (1, output_length, filters))
        if self.activation is not None:
            output = self.activation(output)
        return output

    def get_config(self):
        config = {
            'filters': self.filters,
            'kernel_size': self.kernel_size,
            'strides': self.strides,
            'padding': self.padding,
            'activation': activations.serialize(self.activation),
            'use_bias': self.use_bias,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'bias_initializer': initializers.serialize(self.kernel_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'bias_regularizer': regularizers.serialize(self.bias_regularizer),
            'activity_regularizer': regularizers.serialize(self.activity_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
            'bias_constraint': constraints.serialize(self.bias_constraint)
        }
        base_config = super(LocallyConnected1D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class LocallyConnected2D(Layer):
    """Locally-connected layer for 2D inputs.

    The `LocallyConnected2D` layer works similarly
    to the `Conv2D` layer, except that weights are unshared,
    that is, a different set of filters is applied at each
    different patch of the input.

    # Examples
    ```python
        # apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image
        # with `data_format="channels_last"`:
        model = Sequential()
        model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3)))
        # now model.output_shape == (None, 30, 30, 64)
        # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters

        # add a 3x3 unshared weights convolution on top, with 32 output filters:
        model.add(LocallyConnected2D(32, (3, 3)))
        # now model.output_shape == (None, 28, 28, 32)
    ```

    # Arguments
        filters: Integer, the dimensionality of the output space
            (i.e. the number output of filters in the convolution).
        kernel_size: An integer or tuple/list of 2 integers, specifying the
            width and height of the 2D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 2 integers,
            specifying the strides of the convolution along the width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
            Specifying any stride value != 1 is incompatible with specifying
            any `dilation_rate` value != 1.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, width, height, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, width, height)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to the kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        4D tensor with shape:
        `(samples, channels, rows, cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, rows, cols, channels)` if data_format='channels_last'.

    # Output shape
        4D tensor with shape:
        `(samples, filters, new_rows, new_cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
        `rows` and `cols` values might have changed due to padding.
    """

    def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(LocallyConnected2D, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
        self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
        self.padding = conv_utils.normalize_padding(padding)
        if self.padding != 'valid':
            raise ValueError('Invalid border mode for LocallyConnected2D '
                             '(only "valid" is supported): ' + padding)
        self.data_format = conv_utils.normalize_data_format(data_format)
        self.activation = activations.get(activation)
        self.use_bias = use_bias
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)
        self.input_spec = InputSpec(ndim=4)

    def build(self, input_shape):
        if self.data_format == 'channels_last':
            input_row, input_col = input_shape[1:-1]
            input_filter = input_shape[3]
        else:
            input_row, input_col = input_shape[2:]
            input_filter = input_shape[1]
        if input_row is None or input_col is None:
            raise ValueError('The spatial dimensions of the inputs to '
                             ' a LocallyConnected2D layer '
                             'should be fully-defined, but layer received '
                             'the inputs shape ' + str(input_shape))
        output_row = conv_utils.conv_output_length(input_row, self.kernel_size[0],
                                                   self.padding, self.strides[0])
        output_col = conv_utils.conv_output_length(input_col, self.kernel_size[1],
                                                   self.padding, self.strides[1])
        self.output_row = output_row
        self.output_col = output_col
        self.kernel_shape = (output_row * output_col,
                             self.kernel_size[0] * self.kernel_size[1] * input_filter,
                             self.filters)
        self.kernel = self.add_weight(self.kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight((output_row, output_col, self.filters),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
        if self.data_format == 'channels_first':
            self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
        else:
            self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
        self.built = True

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            rows = input_shape[2]
            cols = input_shape[3]
        elif self.data_format == 'channels_last':
            rows = input_shape[1]
            cols = input_shape[2]

        rows = conv_utils.conv_output_length(rows, self.kernel_size[0],
                                             self.padding, self.strides[0])
        cols = conv_utils.conv_output_length(cols, self.kernel_size[1],
                                             self.padding, self.strides[1])

        if self.data_format == 'channels_first':
            return (input_shape[0], self.filters, rows, cols)
        elif self.data_format == 'channels_last':
            return (input_shape[0], rows, cols, self.filters)

    def call(self, inputs):
        stride_row, stride_col = self.strides
        _, feature_dim, filters = self.kernel_shape

        if self.data_format == 'channels_first':
            if K.backend() == 'theano':
                output = []
                for i in range(self.output_row):
                    for j in range(self.output_col):
                        slice_row = slice(i * stride_row,
                                          i * stride_row + self.kernel_size[0])
                        slice_col = slice(j * stride_col,
                                          j * stride_col + self.kernel_size[1])
                        x_flatten = K.reshape(inputs[:, :, slice_row, slice_col],
                                              (1, -1, feature_dim))
                        output.append(K.dot(x_flatten,
                                      self.kernel[i * self.output_col + j, :, :]))
                output = K.concatenate(output, axis=0)
            else:
                xs = []
                for i in range(self.output_row):
                    for j in range(self.output_col):
                        slice_row = slice(i * stride_row,
                                          i * stride_row + self.kernel_size[0])
                        slice_col = slice(j * stride_col,
                                          j * stride_col + self.kernel_size[1])
                        xs.append(K.reshape(inputs[:, :, slice_row, slice_col],
                                            (1, -1, feature_dim)))
                x_aggregate = K.concatenate(xs, axis=0)
                output = K.batch_dot(x_aggregate, self.kernel)
            output = K.reshape(output,
                               (self.output_row, self.output_col, -1, filters))
            output = K.permute_dimensions(output, (2, 3, 0, 1))

        elif self.data_format == 'channels_last':
            xs = []
            for i in range(self.output_row):
                for j in range(self.output_col):
                    slice_row = slice(i * stride_row,
                                      i * stride_row + self.kernel_size[0])
                    slice_col = slice(j * stride_col,
                                      j * stride_col + self.kernel_size[1])
                    xs.append(K.reshape(inputs[:, slice_row, slice_col, :],
                                        (1, -1, feature_dim)))
            x_aggregate = K.concatenate(xs, axis=0)
            output = K.batch_dot(x_aggregate, self.kernel)
            output = K.reshape(output,
                               (self.output_row, self.output_col, -1, filters))
            output = K.permute_dimensions(output, (2, 0, 1, 3))

        if self.use_bias:
            if self.data_format == 'channels_first':
                output += K.reshape(self.bias,
                                    (1, filters, self.output_row, self.output_col))
            elif self.data_format == 'channels_last':
                output += K.reshape(self.bias,
                                    (1, self.output_row, self.output_col, filters))
        output = self.activation(output)
        return output

    def get_config(self):
        config = {
            'filters': self.filters,
            'kernel_size': self.kernel_size,
            'strides': self.strides,
            'padding': self.padding,
            'data_format': self.data_format,
            'activation': activations.serialize(self.activation),
            'use_bias': self.use_bias,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'bias_initializer': initializers.serialize(self.kernel_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'bias_regularizer': regularizers.serialize(self.bias_regularizer),
            'activity_regularizer': regularizers.serialize(self.activity_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
            'bias_constraint': constraints.serialize(self.bias_constraint)
        }
        base_config = super(LocallyConnected2D, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))
