# 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
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# ==============================================================================

"""Inplace operations.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import math_ops


def _inplace_helper(x, i, v, op):
  """Applies an inplace op on (x, i, v).

  op is one of gen_array_ops.alias_inplace_update,
  gen_array_ops.alias_inplace_add, or gen_array_ops.alias_inplace_sub.

  If i is None, x and v must be the same shape. Computes
    x op v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    x[i, :] op v;
  Otherwise, x and v must have the same rank. Computes
    x[i, :] op v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.
    op: alias_inplace_update, alias_inplace_add, or alias_inplace_sub.

  Returns:
    Returns x.

  """
  x = ops.convert_to_tensor(x)
  v = ops.convert_to_tensor(v, x.dtype)
  if i is None:
    # Full tensor.
    return array_ops.reshape(
        op(array_ops.reshape(x, [1, -1]), [0], array_ops.reshape(v, [1, -1])),
        array_ops.shape(x))
  i = math_ops.to_int32(i)
  if i.get_shape().ndims == 0:
    # Single 0-dim update.
    return op(x, array_ops.reshape(i, [1]), array_ops.expand_dims(v, 0))
  return op(x, i, v)


def alias_inplace_update(x, i, v):
  """Applies an inplace update on input x at index i with value v. Aliases x.

  If i is None, x and v must be the same shape. Computes
    x = v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    x[i, :] = v;
  Otherwise, x and v must have the same rank. Computes
    x[i, :] = v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.

  Returns:
    Returns x.

  """
  return _inplace_helper(x, i, v, gen_array_ops.inplace_update)


def alias_inplace_add(x, i, v):
  """Applies an inplace add on input x at index i with value v. Aliases x.

  If i is None, x and v must be the same shape. Computes
    x += v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    x[i, :] += v;
  Otherwise, x and v must have the same rank. Computes
    x[i, :] += v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.

  Returns:
    Returns x.

  """
  return _inplace_helper(x, i, v, gen_array_ops.inplace_add)


def alias_inplace_sub(x, i, v):
  """Applies an inplace sub on input x at index i with value v. Aliases x.

  If i is None, x and v must be the same shape. Computes
    x -= v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    x[i, :] -= v;
  Otherwise, x and v must have the same rank. Computes
    x[i, :] -= v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.

  Returns:
    Returns x.

  """
  return _inplace_helper(x, i, v, gen_array_ops.inplace_sub)


def empty_like(x, init=None):
  """Returns a non-initialized tensor with the same shape and dtype as x.

  Args:
    x: A Tensor.
    init: Initialize the returned tensor with the default value of
      x.dtype(), if True. Otherwise, do not initialize. Defaults to
      None.

  Returns:
    A tensor y, whose dtype and shape are the same as those of x.
    y is guaranteed not to be an alias of x. Upon return, y may contain
    arbitrary data.

  """
  x = ops.convert_to_tensor(x)
  return gen_array_ops.empty(array_ops.shape(x), x.dtype, init=init)


def inplace_update(x, i, v):
  """Applies an inplace update on input x at index i with value v.

  Note that this function is not actually inplace - it allocates
  a copy of x.  The utility is not avoiding memory copies but rather
  specifying a sparse update.

  If i is None, x and v must be the same shape. Computes
    y = x; y = v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    y = x; y[i, :] = v;
  Otherwise, x and v must have the same rank. Computes
    y = x; y[i, :] = v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.

  Returns:
    Returns y, which is guaranteed not to be an alias of x.

  """
  return alias_inplace_update(gen_array_ops.deep_copy(x), i, v)


def inplace_add(x, i, v):
  """Applies an inplace add on input x at index i with value v.

  Note that this function is not actually inplace - it allocates
  a copy of x.  The utility is not avoiding memory copies but rather
  specifying a sparse update.

  If i is None, x and v must be the same shape. Computes
    y = x; y += v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    y = x; y[i, :] += v;
  Otherwise, x and v must have the same rank. Computes
    y = x; y[i, :] += v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.

  Returns:
    Returns y, which is guaranteed not to be an alias of x.

  """
  return alias_inplace_add(gen_array_ops.deep_copy(x), i, v)


def inplace_sub(x, i, v):
  """Applies an inplace sub on input x at index i with value v.

  Note that this function is not actually inplace - it allocates
  a copy of x.  The utility is not avoiding memory copies but rather
  specifying a sparse update.

  If i is None, x and v must be the same shape. Computes
    y = x; y -= v;
  If i is a scalar, x has a rank 1 higher than v's. Computes
    y = x; y[i, :] -= v;
  Otherwise, x and v must have the same rank. Computes
    y = x; y[i, :] -= v;

  Args:
    x: A Tensor.
    i: None, a scalar or a vector.
    v: A Tensor.

  Returns:
    Returns y, which is guaranteed not to be an alias of x.

  """
  return alias_inplace_sub(gen_array_ops.deep_copy(x), i, v)

empty = gen_array_ops.empty
