package AI::MXNet::Symbol::Doc;
use strict;
use warnings;
use AI::MXNet::Base;
use AI::MXNet::Function::Parameters;
use Exporter;
use base qw(Exporter);
@AI::MXNet::Symbol::Doc::EXPORT = qw/build_doc/;

=head1 

Guidelines
----------

To add extra doc to the operator `XXX`, write a class `XXXDoc`, deriving
from the base class `SymbolDoc`, and put the extra doc as the docstring
of `XXXDoc`.

The document added here should be Python-specific. Documents that are useful
for all language bindings should be added to the C++ side where the operator
is defined / registered.

The code snippet in the docstring will be run using `doctest`. During running,
the environment will have access to

- all the global names in this file (e.g. `SymbolDoc`)
- all the operators (e.g. `FullyConnected`)
- the name `test_utils` for `mxnet.test_utils` (e.g. `test_utils.reldiff`)
- the name `mxnet` (e.g. `mxnet.nd.zeros`)
- the name `numpy`

The following documents are recommended:

- *Examples*: simple and short code snippet showing how to use this operator.
  It should show typical calling examples and behaviors (e.g. maps an input
  of what shape to an output of what shape).
- *Regression Test*: longer test code for the operators. We normally do not
  expect the users to read those, but they will be executed by `doctest` to
  ensure the behavior of each operator does not change unintentionally.
=cut

=head2 get_output_shape

    Get user friendly information of the output shapes.
=cut

method get_output_shape(AI::MXNet::Symbol $sym, %input_shapes)
{
    my $s_outputs = $sym->infer_shape(%input_shapes);
    my %ret;
    @ret{ @{ $sym->list_outputs() } } = @$s_outputs;
    return bless \%ret, 'AI::MXNet::Util::Printable';
}

=head2 build_doc

    Build docstring for symbolic functions.
=cut

func build_doc(
                    Str $func_name,
                    Str $desc,
                    ArrayRef[Str] $arg_names,
                    ArrayRef[Str] $arg_types,
                    ArrayRef[Str] $arg_desc,
                    Str $key_var_num_args=,
                    Str $ret_type=
)
{
    my $param_str = build_param_doc($arg_names, $arg_types, $arg_desc);
    if($key_var_num_args)
    {
        $desc .= "\nThis function support variable length of positional input."
    }
    my $doc_str = sprintf("%s\n\n" .
               "%s\n" .
               "name : string, optional.\n" .
               "    Name of the resulting symbol.\n\n" .
               "Returns\n" .
               "-------\n" .
               "symbol: Symbol\n" .
               "    The result symbol.", $desc, $param_str);
    return $doc_str;
}

=head

class ActivationDoc(SymbolDoc):
    """
    Examples
    --------
    A one-hidden-layer MLP with ReLU activation:

    >>> data = Variable('data')
    >>> mlp = FullyConnected(data=data, num_hidden=128, name='proj')
    >>> mlp = Activation(data=mlp, act_type='relu', name='activation')
    >>> mlp = FullyConnected(data=mlp, num_hidden=10, name='mlp')
    >>> mlp
    <Symbol mlp>

    Regression Test
    ---------------
    ReLU activation

    >>> test_suites = [
    ...     ('relu', lambda x: numpy.maximum(x, 0)),
    ...     ('sigmoid', lambda x: 1 / (1 + numpy.exp(-x))),
    ...     ('tanh', lambda x: numpy.tanh(x)),
    ...     ('softrelu', lambda x: numpy.log(1 + numpy.exp(x)))
    ... ]
    >>> x = test_utils.random_arrays((2, 3, 4))
    >>> for act_type, numpy_impl in test_suites:
    ...     op = Activation(act_type=act_type, name='act')
    ...     y = test_utils.simple_forward(op, act_data=x)
    ...     y_np = numpy_impl(x)
    ...     print('%s: %s' % (act_type, test_utils.almost_equal(y, y_np)))
    relu: True
    sigmoid: True
    tanh: True
    softrelu: True
    """


class DropoutDoc(SymbolDoc):
    """
    Examples
    --------
    Apply dropout to corrupt input as zero with probability 0.2:

    >>> data = Variable('data')
    >>> data_dp = Dropout(data=data, p=0.2)

    Regression Test
    ---------------
    >>> shape = (100, 100)  # take larger shapes to be more statistical stable
    >>> x = numpy.ones(shape)
    >>> op = Dropout(p=0.5, name='dp')
    >>> # dropout is identity during testing
    >>> y = test_utils.simple_forward(op, dp_data=x, is_train=False)
    >>> test_utils.almost_equal(x, y, threshold=0)
    True
    >>> y = test_utils.simple_forward(op, dp_data=x, is_train=True)
    >>> # expectation is (approximately) unchanged
    >>> numpy.abs(x.mean() - y.mean()) < 0.1
    True
    >>> set(numpy.unique(y)) == set([0, 2])
    True
    """


class EmbeddingDoc(SymbolDoc):
    """
    Examples
    --------
    Assume we want to map the 26 English alphabet letters to 16-dimensional
    vectorial representations.

    >>> vocabulary_size = 26
    >>> embed_dim = 16
    >>> seq_len, batch_size = (10, 64)
    >>> input = Variable('letters')
    >>> op = Embedding(data=input, input_dim=vocabulary_size, output_dim=embed_dim,
    ...                name='embed')
    >>> SymbolDoc.get_output_shape(op, letters=(seq_len, batch_size))
    {'embed_output': (10L, 64L, 16L)}

    Regression Test
    ---------------
    >>> vocab_size, embed_dim = (26, 16)
    >>> batch_size = 12
    >>> word_vecs = test_utils.random_arrays((vocab_size, embed_dim))
    >>> op = Embedding(name='embed', input_dim=vocab_size, output_dim=embed_dim)
    >>> x = numpy.random.choice(vocab_size, batch_size)
    >>> y = test_utils.simple_forward(op, embed_data=x, embed_weight=word_vecs)
    >>> y_np = word_vecs[x]
    >>> test_utils.almost_equal(y, y_np)
    True
    """


class FlattenDoc(SymbolDoc):
    """
    Examples
    --------
    Flatten is usually applied before `FullyConnected`, to reshape the 4D tensor
    produced by convolutional layers to 2D matrix:

    >>> data = Variable('data')  # say this is 4D from some conv/pool
    >>> flatten = Flatten(data=data, name='flat')  # now this is 2D
    >>> SymbolDoc.get_output_shape(flatten, data=(2, 3, 4, 5))
    {'flat_output': (2L, 60L)}

    Regression Test
    ---------------
    >>> test_dims = [(2, 3, 4, 5), (2, 3), (2,)]
    >>> op = Flatten(name='flat')
    >>> for dims in test_dims:
    ...     x = test_utils.random_arrays(dims)
    ...     y = test_utils.simple_forward(op, flat_data=x)
    ...     y_np = x.reshape((dims[0], numpy.prod(dims[1:])))
    ...     print('%s: %s' % (dims, test_utils.almost_equal(y, y_np)))
    (2, 3, 4, 5): True
    (2, 3): True
    (2,): True
    """


class FullyConnectedDoc(SymbolDoc):
    """
    Examples
    --------
    Construct a fully connected operator with target dimension 512.

    >>> data = Variable('data')  # or some constructed NN
    >>> op = FullyConnected(data=data,
    ...                     num_hidden=512,
    ...                     name='FC1')
    >>> op
    <Symbol FC1>
    >>> SymbolDoc.get_output_shape(op, data=(128, 100))
    {'FC1_output': (128L, 512L)}

    A simple 3-layer MLP with ReLU activation:

    >>> net = Variable('data')
    >>> for i, dim in enumerate([128, 64]):
    ...     net = FullyConnected(data=net, num_hidden=dim, name='FC%d' % i)
    ...     net = Activation(data=net, act_type='relu', name='ReLU%d' % i)
    >>> # 10-class predictor (e.g. MNIST)
    >>> net = FullyConnected(data=net, num_hidden=10, name='pred')
    >>> net
    <Symbol pred>

    Regression Test
    ---------------
    >>> dim_in, dim_out = (3, 4)
    >>> x, w, b = test_utils.random_arrays((10, dim_in), (dim_out, dim_in), (dim_out,))
    >>> op = FullyConnected(num_hidden=dim_out, name='FC')
    >>> out = test_utils.simple_forward(op, FC_data=x, FC_weight=w, FC_bias=b)
    >>> # numpy implementation of FullyConnected
    >>> out_np = numpy.dot(x, w.T) + b
    >>> test_utils.almost_equal(out, out_np)
    True
    """





class ConcatDoc(SymbolDoc):
    """
    Examples
    --------
    Concat two (or more) inputs along a specific dimension:

    >>> a = Variable('a')
    >>> b = Variable('b')
    >>> c = Concat(a, b, dim=1, name='my-concat')
    >>> c
    <Symbol my-concat>
    >>> SymbolDoc.get_output_shape(c, a=(128, 10, 3, 3), b=(128, 15, 3, 3))
    {'my-concat_output': (128L, 25L, 3L, 3L)}

    Note the shape should be the same except on the dimension that is being
    concatenated.
    """


class BroadcastPlusDoc(SymbolDoc):
    """
    Examples
    --------

    >>> a = Variable('a')
    >>> b = Variable('b')
    >>> c = broadcast_plus(a, b)

    Normal summation with matching shapes:

    >>> dev = mxnet.context.cpu();
    >>> x = c.bind(dev, args={'a': mxnet.nd.ones((2, 2)), 'b' : mxnet.nd.ones((2, 2))})
    >>> x.forward()
    [<NDArray 2x2 @cpu(0)>]
    >>> print x.outputs[0].asnumpy()
    [[ 2.  2.]
     [ 2.  2.]]

    Broadcasting:

    >>> x = c.bind(dev, args={'a': mxnet.nd.ones((2, 2)), 'b' : mxnet.nd.ones((1, 1))})
    >>> x.forward()
    [<NDArray 2x2 @cpu(0)>]
    >>> print x.outputs[0].asnumpy()
    [[ 2.  2.]
     [ 2.  2.]]

    >>> x = c.bind(dev, args={'a': mxnet.nd.ones((2, 1)), 'b' : mxnet.nd.ones((1, 2))})
    >>> x.forward()
    [<NDArray 2x2 @cpu(0)>]
    >>> print x.outputs[0].asnumpy()
    [[ 2.  2.]
     [ 2.  2.]]

    >>> x = c.bind(dev, args={'a': mxnet.nd.ones((1, 2)), 'b' : mxnet.nd.ones((2, 1))})
    >>> x.forward()
    [<NDArray 2x2 @cpu(0)>]
    >>> print x.outputs[0].asnumpy()
    [[ 2.  2.]
     [ 2.  2.]]
    """
=cut

1;
