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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Define functions about array.

from ..fluid import layers

__all__ = []


def array_length(array):
 """
 This OP is used to get the length of the input array.

 Args:
 array (list|Tensor): The input array that will be used to compute the length. In dynamic mode, ``array`` is a Python list. But in static mode, array is a Tensor whose VarType is LOD_TENSOR_ARRAY.

 Returns:
 Tensor: 1-D Tensor with shape [1], which is the length of array.

 Examples:
 .. code-block:: python

 import paddle

 arr = paddle.tensor.create_array(dtype='float32')
 x = paddle.full(shape=[3, 3], fill_value=5, dtype="float32")
 i = paddle.zeros(shape=[1], dtype="int32")

 arr = paddle.tensor.array_write(x, i, array=arr)

 arr_len = paddle.tensor.array_length(arr)
 print(arr_len) # 1
 """
 return layers.array_length(array)


def array_read(array, i):
 """
 This OP is used to read data at the specified position from the input array.

 Case:

 .. code-block:: text

 Input:
 The shape of first three tensors are [1], and that of the last one is [1,2]:
 array = ([0.6], [0.1], [0.3], [0.4, 0.2])
 And:
 i = [3]

 Output:
 output = [0.4, 0.2]

 Args:
 array (list|Tensor): The input array. In dynamic mode, ``array`` is a Python list. But in static mode, array is a Tensor whose ``VarType`` is ``LOD_TENSOR_ARRAY``.
 i (Tensor): 1-D Tensor, whose shape is [1] and dtype is int64. It represents the
 specified read position of ``array``.

 Returns:
 Tensor: A Tensor that is read at the specified position of ``array``.

 Examples:
 .. code-block:: python

 import paddle

 arr = paddle.tensor.create_array(dtype="float32")
 x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32")
 i = paddle.zeros(shape=[1], dtype="int32")

 arr = paddle.tensor.array_write(x, i, array=arr)

 item = paddle.tensor.array_read(arr, i)
 print(item) # [[5., 5., 5.]]
 """
 return layers.array_read(array, i)


def array_write(x, i, array=None):
 """
 This OP writes the input ``x`` into the i-th position of the ``array`` returns the modified array.
 If ``array`` is none, a new array will be created and returned.

 Args:
 x (Tensor): The input data to be written into array. It's multi-dimensional
 Tensor or LoDTensor. Data type: float32, float64, int32, int64 and bool.
 i (Tensor): 1-D Tensor with shape [1], which represents the position into which
 ``x`` is written.
 array (list|Tensor, optional): The array into which ``x`` is written. The default value is None,
 when a new array will be created and returned as a result. In dynamic mode, ``array`` is a Python list.
 But in static mode, array is a Tensor whose ``VarType`` is ``LOD_TENSOR_ARRAY``.

 Returns:
 list|Tensor: The input ``array`` after ``x`` is written into.

 Examples:
 .. code-block:: python

 import paddle

 arr = paddle.tensor.create_array(dtype="float32")
 x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32")
 i = paddle.zeros(shape=[1], dtype="int32")

 arr = paddle.tensor.array_write(x, i, array=arr)

 item = paddle.tensor.array_read(arr, i)
 print(item) # [[5., 5., 5.]]
 """
 return layers.array_write(x, i, array)


def create_array(dtype, initialized_list=None):
 """
 This OP creates an array. It is used as the input of :ref:`api_paddle_tensor_array_array_read` and
 :ref:`api_paddle_tensor_array_array_write`.

 Args:
 dtype (str): The data type of the elements in the array. Support data type: float32, float64, int32, int64 and bool.
 initialized_list(list): Used to initialize as default value for created array.
 All values in initialized list should be a Tensor.

 Returns:
 list|Tensor: An empty array. In dynamic mode, ``array`` is a Python list. But in static mode, array is a Tensor
 whose ``VarType`` is ``LOD_TENSOR_ARRAY``.

 Examples:
 .. code-block:: python

 import paddle

 arr = paddle.tensor.create_array(dtype="float32")
 x = paddle.full(shape=[1, 3], fill_value=5, dtype="float32")
 i = paddle.zeros(shape=[1], dtype="int32")

 arr = paddle.tensor.array_write(x, i, array=arr)

 item = paddle.tensor.array_read(arr, i)
 print(item) # [[5., 5., 5.]]

 """
 return layers.create_array(dtype, initialized_list)