# Copyright 2015 The TensorFlow 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.
# ==============================================================================
# pylint: disable=not-callable
# pylint: disable=redefined-builtin
"""Layers that can merge several inputs into one.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.keras._impl.keras import backend as K
from tensorflow.python.keras._impl.keras.engine.base_layer import Layer
from tensorflow.python.keras._impl.keras.engine.base_layer import shape_type_conversion
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.util.tf_export import tf_export


class _Merge(Layer):
  """Generic merge layer for elementwise merge functions.

  Used to implement `Sum`, `Average`, etc.

  Arguments:
      **kwargs: standard layer keyword arguments.
  """

  def __init__(self, **kwargs):
    super(_Merge, self).__init__(**kwargs)
    self.supports_masking = True

  def _merge_function(self, inputs):
    raise NotImplementedError

  def _compute_elemwise_op_output_shape(self, shape1, shape2):
    """Computes the shape of the resultant of an elementwise operation.

    Arguments:
        shape1: tuple or None. Shape of the first tensor
        shape2: tuple or None. Shape of the second tensor

    Returns:
        expected output shape when an element-wise operation is
        carried out on 2 tensors with shapes shape1 and shape2.
        tuple or None.

    Raises:
        ValueError: if shape1 and shape2 are not compatible for
            element-wise operations.
    """
    if None in [shape1, shape2]:
      return None
    elif len(shape1) < len(shape2):
      return self._compute_elemwise_op_output_shape(shape2, shape1)
    elif not shape2:
      return shape1
    output_shape = list(shape1[:-len(shape2)])
    for i, j in zip(shape1[-len(shape2):], shape2):
      if i is None or j is None:
        output_shape.append(None)
      elif i == 1:
        output_shape.append(j)
      elif j == 1:
        output_shape.append(i)
      else:
        if i != j:
          raise ValueError(
              'Operands could not be broadcast '
              'together with shapes ' + str(shape1) + ' ' + str(shape2))
        output_shape.append(i)
    return tuple(output_shape)

  @shape_type_conversion
  def build(self, input_shape):
    # Used purely for shape validation.
    if not isinstance(input_shape, list):
      raise ValueError('A merge layer should be called ' 'on a list of inputs.')
    if len(input_shape) < 2:
      raise ValueError('A merge layer should be called '
                       'on a list of at least 2 inputs. '
                       'Got ' + str(len(input_shape)) + ' inputs.')
    batch_sizes = [s[0] for s in input_shape if s is not None]
    batch_sizes = set(batch_sizes)
    batch_sizes -= set([None])
    if len(batch_sizes) > 1:
      raise ValueError(
          'Can not merge tensors with different '
          'batch sizes. Got tensors with shapes : ' + str(input_shape))
    if input_shape[0] is None:
      output_shape = None
    else:
      output_shape = input_shape[0][1:]
    for i in range(1, len(input_shape)):
      if input_shape[i] is None:
        shape = None
      else:
        shape = input_shape[i][1:]
      output_shape = self._compute_elemwise_op_output_shape(output_shape, shape)
    # If the inputs have different ranks, we have to reshape them
    # to make them broadcastable.
    if None not in input_shape and len(set(map(len, input_shape))) == 1:
      self._reshape_required = False
    else:
      self._reshape_required = True

  def call(self, inputs):
    if not isinstance(inputs, list):
      raise ValueError('A merge layer should be called ' 'on a list of inputs.')
    if self._reshape_required:
      reshaped_inputs = []
      input_ndims = list(map(K.ndim, inputs))
      if None not in input_ndims:
        # If ranks of all inputs are available,
        # we simply expand each of them at axis=1
        # until all of them have the same rank.
        max_ndim = max(input_ndims)
        for x in inputs:
          x_ndim = K.ndim(x)
          for _ in range(max_ndim - x_ndim):
            x = array_ops.expand_dims(x, axis=1)
          reshaped_inputs.append(x)
        return self._merge_function(reshaped_inputs)
      else:
        # Transpose all inputs so that batch size is the last dimension.
        # (batch_size, dim1, dim2, ... ) -> (dim1, dim2, ... , batch_size)
        transposed = False
        for x in inputs:
          x_ndim = K.ndim(x)
          if x_ndim is None:
            x_shape = array_ops.shape(x)
            batch_size = x_shape[0]
            new_shape = K.concatenate(
                [x_shape[1:],
                 array_ops.expand_dims(batch_size, axis=-1)])
            x_transposed = array_ops.reshape(
                x,
                array_ops.stack(
                    [batch_size, math_ops.reduce_prod(x_shape[1:])], axis=0))
            x_transposed = array_ops.transpose(x_transposed, perm=(1, 0))
            x_transposed = array_ops.reshape(x_transposed, new_shape)
            reshaped_inputs.append(x_transposed)
            transposed = True
          elif x_ndim > 1:
            dims = list(range(1, x_ndim)) + [0]
            reshaped_inputs.append(array_ops.transpose(x, perm=dims))
            transposed = True
          else:
            # We don't transpose inputs if they are 1D vectors or scalars.
            reshaped_inputs.append(x)
        y = self._merge_function(reshaped_inputs)
        y_ndim = K.ndim(y)
        if transposed:
          # If inputs have been transposed, we have to transpose the output too.
          if y_ndim is None:
            y_shape = array_ops.shape(y)
            y_ndim = array_ops.shape(y_shape)[0]
            batch_size = y_shape[y_ndim - 1]
            new_shape = K.concatenate([
                array_ops.expand_dims(batch_size, axis=-1), y_shape[:y_ndim - 1]
            ])
            y = array_ops.reshape(y, (-1, batch_size))
            y = array_ops.transpose(y, perm=(1, 0))
            y = array_ops.reshape(y, new_shape)
          elif y_ndim > 1:
            dims = [y_ndim - 1] + list(range(y_ndim - 1))
            y = array_ops.transpose(y, perm=dims)
        return y
    else:
      return self._merge_function(inputs)

  @shape_type_conversion
  def compute_output_shape(self, input_shape):
    if input_shape[0] is None:
      output_shape = None
    else:
      output_shape = input_shape[0][1:]
    for i in range(1, len(input_shape)):
      if input_shape[i] is None:
        shape = None
      else:
        shape = input_shape[i][1:]
      output_shape = self._compute_elemwise_op_output_shape(output_shape, shape)
    batch_sizes = [s[0] for s in input_shape if s is not None]
    batch_sizes = set(batch_sizes)
    batch_sizes -= set([None])
    if len(batch_sizes) == 1:
      output_shape = (list(batch_sizes)[0],) + output_shape
    else:
      output_shape = (None,) + output_shape
    return output_shape

  def compute_mask(self, inputs, mask=None):
    if mask is None:
      return None
    if not isinstance(mask, list):
      raise ValueError('`mask` should be a list.')
    if not isinstance(inputs, list):
      raise ValueError('`inputs` should be a list.')
    if len(mask) != len(inputs):
      raise ValueError('The lists `inputs` and `mask` '
                       'should have the same length.')
    if all([m is None for m in mask]):
      return None
    masks = [array_ops.expand_dims(m, axis=0) for m in mask if m is not None]
    return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)


@tf_export('keras.layers.Add')
class Add(_Merge):
  """Layer that adds a list of inputs.

  It takes as input a list of tensors,
  all of the same shape, and returns
  a single tensor (also of the same shape).

  Examples:

  ```python
      import keras

      input1 = keras.layers.Input(shape=(16,))
      x1 = keras.layers.Dense(8, activation='relu')(input1)
      input2 = keras.layers.Input(shape=(32,))
      x2 = keras.layers.Dense(8, activation='relu')(input2)
      added = keras.layers.Add()([x1, x2])  # equivalent to added =
      keras.layers.add([x1, x2])

      out = keras.layers.Dense(4)(added)
      model = keras.models.Model(inputs=[input1, input2], outputs=out)
  ```
  """

  def _merge_function(self, inputs):
    output = inputs[0]
    for i in range(1, len(inputs)):
      output += inputs[i]
    return output


class Subtract(_Merge):
  """Layer that subtracts two inputs.

  It takes as input a list of tensors of size 2,
  both of the same shape, and returns a single tensor, (inputs[0] - inputs[1]),
  also of the same shape.

  Examples:

  ```python
      import keras

      input1 = keras.layers.Input(shape=(16,))
      x1 = keras.layers.Dense(8, activation='relu')(input1)
      input2 = keras.layers.Input(shape=(32,))
      x2 = keras.layers.Dense(8, activation='relu')(input2)
      # Equivalent to subtracted = keras.layers.subtract([x1, x2])
      subtracted = keras.layers.Subtract()([x1, x2])

      out = keras.layers.Dense(4)(subtracted)
      model = keras.models.Model(inputs=[input1, input2], outputs=out)
  ```
  """

  @shape_type_conversion
  def build(self, input_shape):
    super(Subtract, self).build(input_shape)
    if len(input_shape) != 2:
      raise ValueError('A `Subtract` layer should be called '
                       'on exactly 2 inputs')

  def _merge_function(self, inputs):
    if len(inputs) != 2:
      raise ValueError('A `Subtract` layer should be called '
                       'on exactly 2 inputs')
    return inputs[0] - inputs[1]


@tf_export('keras.layers.Multiply')
class Multiply(_Merge):
  """Layer that multiplies (element-wise) a list of inputs.

  It takes as input a list of tensors,
  all of the same shape, and returns
  a single tensor (also of the same shape).
  """

  def _merge_function(self, inputs):
    output = inputs[0]
    for i in range(1, len(inputs)):
      output *= inputs[i]
    return output


@tf_export('keras.layers.Average')
class Average(_Merge):
  """Layer that averages a list of inputs.

  It takes as input a list of tensors,
  all of the same shape, and returns
  a single tensor (also of the same shape).
  """

  def _merge_function(self, inputs):
    output = inputs[0]
    for i in range(1, len(inputs)):
      output += inputs[i]
    return output / len(inputs)


@tf_export('keras.layers.Maximum')
class Maximum(_Merge):
  """Layer that computes the maximum (element-wise) a list of inputs.

  It takes as input a list of tensors,
  all of the same shape, and returns
  a single tensor (also of the same shape).
  """

  def _merge_function(self, inputs):
    output = inputs[0]
    for i in range(1, len(inputs)):
      output = math_ops.maximum(output, inputs[i])
    return output


class Minimum(_Merge):
  """Layer that computes the minimum (element-wise) a list of inputs.

  It takes as input a list of tensors,
  all of the same shape, and returns
  a single tensor (also of the same shape).
  """

  def _merge_function(self, inputs):
    output = inputs[0]
    for i in range(1, len(inputs)):
      output = math_ops.minimum(output, inputs[i])
    return output


@tf_export('keras.layers.Concatenate')
class Concatenate(_Merge):
  """Layer that concatenates a list of inputs.

  It takes as input a list of tensors,
  all of the same shape except for the concatenation axis,
  and returns a single tensor, the concatenation of all inputs.

  Arguments:
      axis: Axis along which to concatenate.
      **kwargs: standard layer keyword arguments.
  """

  def __init__(self, axis=-1, **kwargs):
    super(Concatenate, self).__init__(**kwargs)
    self.axis = axis
    self.supports_masking = True
    self._reshape_required = False

  @shape_type_conversion
  def build(self, input_shape):
    # Used purely for shape validation.
    if not isinstance(input_shape, list) or len(input_shape) < 2:
      raise ValueError('A `Concatenate` layer should be called '
                       'on a list of at least 2 inputs')
    if all([shape is None for shape in input_shape]):
      return
    reduced_inputs_shapes = [list(shape) for shape in input_shape]
    shape_set = set()
    for i in range(len(reduced_inputs_shapes)):
      del reduced_inputs_shapes[i][self.axis]
      shape_set.add(tuple(reduced_inputs_shapes[i]))
    if len(shape_set) > 1:
      raise ValueError('A `Concatenate` layer requires '
                       'inputs with matching shapes '
                       'except for the concat axis. '
                       'Got inputs shapes: %s' % (input_shape))

  def _merge_function(self, inputs):
    return K.concatenate(inputs, axis=self.axis)

  @shape_type_conversion
  def compute_output_shape(self, input_shape):
    if not isinstance(input_shape, list):
      raise ValueError('A `Concatenate` layer should be called '
                       'on a list of inputs.')
    input_shapes = input_shape
    output_shape = list(input_shapes[0])
    for shape in input_shapes[1:]:
      if output_shape[self.axis] is None or shape[self.axis] is None:
        output_shape[self.axis] = None
        break
      output_shape[self.axis] += shape[self.axis]
    return tuple(output_shape)

  def compute_mask(self, inputs, mask=None):
    if mask is None:
      return None
    if not isinstance(mask, list):
      raise ValueError('`mask` should be a list.')
    if not isinstance(inputs, list):
      raise ValueError('`inputs` should be a list.')
    if len(mask) != len(inputs):
      raise ValueError('The lists `inputs` and `mask` '
                       'should have the same length.')
    if all([m is None for m in mask]):
      return None
    # Make a list of masks while making sure
    # the dimensionality of each mask
    # is the same as the corresponding input.
    masks = []
    for input_i, mask_i in zip(inputs, mask):
      if mask_i is None:
        # Input is unmasked. Append all 1s to masks,
        masks.append(array_ops.ones_like(input_i, dtype='bool'))
      elif K.ndim(mask_i) < K.ndim(input_i):
        # Mask is smaller than the input, expand it
        masks.append(array_ops.expand_dims(mask_i, axis=-1))
      else:
        masks.append(mask_i)
    concatenated = K.concatenate(masks, axis=self.axis)
    return K.all(concatenated, axis=-1, keepdims=False)

  def get_config(self):
    config = {
        'axis': self.axis,
    }
    base_config = super(Concatenate, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@tf_export('keras.layers.Dot')
class Dot(_Merge):
  """Layer that computes a dot product between samples in two tensors.

  E.g. if applied to two tensors `a` and `b` of shape `(batch_size, n)`,
  the output will be a tensor of shape `(batch_size, 1)`
  where each entry `i` will be the dot product between
  `a[i]` and `b[i]`.

  Arguments:
      axes: Integer or tuple of integers,
          axis or axes along which to take the dot product.
      normalize: Whether to L2-normalize samples along the
          dot product axis before taking the dot product.
          If set to True, then the output of the dot product
          is the cosine proximity between the two samples.
      **kwargs: Standard layer keyword arguments.
  """

  def __init__(self, axes, normalize=False, **kwargs):
    super(Dot, self).__init__(**kwargs)
    if not isinstance(axes, int):
      if not isinstance(axes, (list, tuple)):
        raise TypeError('Invalid type for `axes` - '
                        'should be a list or an int.')
      if len(axes) != 2:
        raise ValueError('Invalid format for `axes` - '
                         'should contain two elements.')
      if not isinstance(axes[0], int) or not isinstance(axes[1], int):
        raise ValueError('Invalid format for `axes` - '
                         'list elements should be "int".')
    self.axes = axes
    self.normalize = normalize
    self.supports_masking = True
    self._reshape_required = False

  @shape_type_conversion
  def build(self, input_shape):
    # Used purely for shape validation.
    if not isinstance(input_shape, list) or len(input_shape) != 2:
      raise ValueError('A `Dot` layer should be called '
                       'on a list of 2 inputs.')
    shape1 = input_shape[0]
    shape2 = input_shape[1]
    if shape1 is None or shape2 is None:
      return
    if isinstance(self.axes, int):
      if self.axes < 0:
        axes = [self.axes % len(shape1), self.axes % len(shape2)]
      else:
        axes = [self.axes] * 2
    else:
      axes = self.axes
    if shape1[axes[0]] != shape2[axes[1]]:
      raise ValueError('Dimension incompatibility '
                       '%s != %s. ' % (shape1[axes[0]], shape2[axes[1]]) +
                       'Layer shapes: %s, %s' % (shape1, shape2))

  def _merge_function(self, inputs):
    if len(inputs) != 2:
      raise ValueError('A `Dot` layer should be called ' 'on exactly 2 inputs')
    x1 = inputs[0]
    x2 = inputs[1]
    if isinstance(self.axes, int):
      if self.axes < 0:
        axes = [self.axes % K.ndim(x1), self.axes % K.ndim(x2)]
      else:
        axes = [self.axes] * 2
    else:
      axes = []
      for i in range(len(self.axes)):
        if self.axes[i] < 0:
          axes.append(self.axes[i] % K.ndim(inputs[i]))
        else:
          axes.append(self.axes[i])
    if self.normalize:
      x1 = nn.l2_normalize(x1, axis=axes[0])
      x2 = nn.l2_normalize(x2, axis=axes[1])
    output = K.batch_dot(x1, x2, axes)
    return output

  @shape_type_conversion
  def compute_output_shape(self, input_shape):
    if not isinstance(input_shape, list) or len(input_shape) != 2:
      raise ValueError('A `Dot` layer should be called '
                       'on a list of 2 inputs.')
    shape1 = list(input_shape[0])
    shape2 = list(input_shape[1])
    if isinstance(self.axes, int):
      if self.axes < 0:
        axes = [self.axes % len(shape1), self.axes % len(shape2)]
      else:
        axes = [self.axes] * 2
    else:
      axes = self.axes
    shape1.pop(axes[0])
    shape2.pop(axes[1])
    shape2.pop(0)
    output_shape = shape1 + shape2
    if len(output_shape) == 1:
      output_shape += [1]
    return tuple(output_shape)

  def compute_mask(self, inputs, mask=None):
    return None

  def get_config(self):
    config = {
        'axes': self.axes,
        'normalize': self.normalize,
    }
    base_config = super(Dot, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@tf_export('keras.layers.add')
def add(inputs, **kwargs):
  """Functional interface to the `Add` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the sum of the inputs.

  Examples:

  ```python
      import keras

      input1 = keras.layers.Input(shape=(16,))
      x1 = keras.layers.Dense(8, activation='relu')(input1)
      input2 = keras.layers.Input(shape=(32,))
      x2 = keras.layers.Dense(8, activation='relu')(input2)
      added = keras.layers.add([x1, x2])

      out = keras.layers.Dense(4)(added)
      model = keras.models.Model(inputs=[input1, input2], outputs=out)
  ```
  """
  return Add(**kwargs)(inputs)


def subtract(inputs, **kwargs):
  """Functional interface to the `Subtract` layer.

  Arguments:
      inputs: A list of input tensors (exactly 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the difference of the inputs.

  Examples:

  ```python
      import keras

      input1 = keras.layers.Input(shape=(16,))
      x1 = keras.layers.Dense(8, activation='relu')(input1)
      input2 = keras.layers.Input(shape=(32,))
      x2 = keras.layers.Dense(8, activation='relu')(input2)
      subtracted = keras.layers.subtract([x1, x2])

      out = keras.layers.Dense(4)(subtracted)
      model = keras.models.Model(inputs=[input1, input2], outputs=out)
  ```
  """
  return Subtract(**kwargs)(inputs)


@tf_export('keras.layers.multiply')
def multiply(inputs, **kwargs):
  """Functional interface to the `Multiply` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the element-wise product of the inputs.
  """
  return Multiply(**kwargs)(inputs)


@tf_export('keras.layers.average')
def average(inputs, **kwargs):
  """Functional interface to the `Average` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the average of the inputs.
  """
  return Average(**kwargs)(inputs)


@tf_export('keras.layers.maximum')
def maximum(inputs, **kwargs):
  """Functional interface to the `Maximum` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the element-wise maximum of the inputs.
  """
  return Maximum(**kwargs)(inputs)


def minimum(inputs, **kwargs):
  """Functional interface to the `Minimum` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the element-wise minimum of the inputs.
  """
  return Minimum(**kwargs)(inputs)


@tf_export('keras.layers.concatenate')
def concatenate(inputs, axis=-1, **kwargs):
  """Functional interface to the `Concatenate` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      axis: Concatenation axis.
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the concatenation of the inputs alongside axis `axis`.
  """
  return Concatenate(axis=axis, **kwargs)(inputs)


@tf_export('keras.layers.dot')
def dot(inputs, axes, normalize=False, **kwargs):
  """Functional interface to the `Dot` layer.

  Arguments:
      inputs: A list of input tensors (at least 2).
      axes: Integer or tuple of integers,
          axis or axes along which to take the dot product.
      normalize: Whether to L2-normalize samples along the
          dot product axis before taking the dot product.
          If set to True, then the output of the dot product
          is the cosine proximity between the two samples.
      **kwargs: Standard layer keyword arguments.

  Returns:
      A tensor, the dot product of the samples from the inputs.
  """
  return Dot(axes=axes, normalize=normalize, **kwargs)(inputs)
