"""
DRAFT: TheanoObject

N.B. the gotcha with this design is listed in the documentation of
`TheanoObject`.

"""
from __future__ import print_function
import theano
from theano import tensor
import numpy


def theano_type(x):
    """
    Return a theano Type instance suitable for containing value `x`.

    """
    if type(x) is int:
        return tensor.lscalar
    else:
        raise NotImplementedError()


class symbolic_fn_callable(object):
    """
    This is the class whose instance you get when you access a symbolic function
    in a `TheanoObject`.

    When you call a symbolic function (`symbolic_fn`) of a TheanoObject,
    the `__call__` of this class handles your request.

    You can also access the symbolic outputs and updates of a symbolic function
    through this class.

    Examples
    --------

    class T(TheanoObject):
        @symbolic_fn
        def add(self, x):
            ...
            add_outputs = ...
            add_updates = ...
            return RVal(add_outputs, add_updates)
            
    t = T()
    t.add.outputs(5)      # returns `add_outputs` from when `x=theano_type(5)`
    t.add.updates(5)      # returns `add_updates` from when `x=theano_type(5)`

    t.add.theano_function(5)  # returns the `Function` compiled when
                              # `x=theano_type(5)`
    
    t.add(5)              # runs the `Function` compiled when `x=theano_type(5)`
                          # with arguments `(5,)`

    """
    def __init__(self, fn, mode):
        self.fn = fn
        self.mode = mode

    def on(self, o_self):
        """
        Silly method to work with symbolic_fn.__get__.

        """
        self.o_self = o_self
        return self

    def run_symbolic(self, *args, **kwargs):
        return self.o_self._get_method_impl(self.fn, self.o_self, args, kwargs, mode=self.mode)

    def __call__(self, *args, **kwargs):
        return self.run_symbolic(*args, **kwargs)['theano_function'](*args, **kwargs)

    def theano_function(self, *args, **kwargs):
        return self.run_symbolic(*args, **kwargs)['theano_function']

    def outputs(self, *args, **kwargs):
        return self.run_symbolic(*args, **kwargs)['outputs']

    def updates(self, *args, **kwargs):
        return self.run_symbolic(*args, **kwargs)['updates']


class symbolic_fn(object):
    """
    A property-like class for decorating symbolic functions in `TheanoObject`.

    """
    def __init__(self, fn, mode=None):
        self.fn = fn
        self.callable = symbolic_fn_callable(fn, mode)

    def __get__(self, o_self, o_cls):
        return self.callable.on(o_self)

    def __set__(self, o_self, new_val):
        pass
        # return NotImplemented


def symbolic_fn_opts(**kwargs):
    """
    Return a decorator for symbolic_functions in a `TheanoObject`.

    `kwargs` passed here are passed to `theano.function` via `symbolic_fn`.

    """
    def deco(f):
        return symbolic_fn(f, **kwargs)
    return deco


class RVal(object):
    """
    A Return-Value object for a `symbolic_fn`.

    """
    outputs = []
    """
    The method will compute values for the variables in this list.

    """
    updates = {}
    """The method will update module variables in this dictionary.

    For items ``(k,v)`` in this dictionary, ``k`` must be a `symbolic_member`
    of some module.
    On each call to this compiled function, the value of ``k`` will be replaced
    with the computed value of the Variable ``v``.

    """
    def __init__(self, outputs, updates=None):
        if updates is None:
            updates = {}
        self.outputs = outputs
        assert type(updates) is dict
        self.updates = updates


class TheanoObject(object):
    """
    Base for Theano-supported classes.

    This class provides support for symbolic_fn class attributes.
    These will be compiled on demand so that they can be used just like normal
    (non-symbolic) methods.

    The symbolic functions in a TheanoObject can share member variables that
    have been created using the `symbolic_member` method.

    Notes
    -----
    Other variables (ones not created using ``self.symbolic_member``) referred
    to in the body of a symbolic function will *not* be shared between symbolic
    functions, or between symbolic functions and this class. These other
    variables will be locked away in the closure of a symbolic function when
    that function is compiled.

    .. warning:: It is not recommended for code to interleave
    (a) changes to non-symbolic instance variables with
    (b) calls to symbolic functions that use those instance variables.
    A symbolic function may be compiled multiple times because it must be
    compiled for each set of argument types.
    Each time the function is compiled, the values of non-symbolic variables
    will be locked into the compiled function. Subsequent changes to those
    non-symbolic instance variables will not have any effect on the behaviour
    of the already-compiled symbolic function.

    :todo: Is there an efficient way of recognizing when a compiled symbolic
    function is stale, wrt the current values of the class's instance variables?

    - One option is to re-evaluate symbolic functions symbolically and see if
    the graph can be completely merged with the original graph. This is not
    fast enough to do all the time by default though.

    """
    def __init__(self):
        self.module_method_cache = {}

    def _get_method_impl(self, fn, o_self, args, kwargs, mode):
        """
        Retrieve information about the symbolic function (`fn`) in TheanoObject
        instance `o_self`, being evaluated on arguments `args` and `kwargs`.

        Returns
        -------
        dict with entries 'theano_function', 'outputs', 'updates'
            The theano function compiled for these arguments, the symbolic
            outputs of that function, and the symbolic updates performed by
            that function.

        Notes
        -----
        This function caches return values in self.`module_method_cache`.

        :todo: This may at some point become a class-level cache rather than an
        instance-level cache.

        """
        if kwargs:
            raise NotImplementedError()

        cache = self.module_method_cache

        args_types = tuple(theano_type(arg) for arg in args)
        key = (fn, args_types)

        if key not in cache:
            inputs = [a() for a in args_types]
            print('compiling', fn, 'for inputs', inputs)
            rval = fn(o_self, *inputs)

            print('compiling to compute outputs', rval.outputs)

            if isinstance(rval.outputs, (tuple, list)):
                all_required_inputs = theano.gof.graph.inputs(rval.outputs)
            else:
                all_required_inputs = theano.gof.graph.inputs([rval.outputs])

            # construct In instances for the symbolic_member instances that can automatically be
            # included here.
            module_inputs = [theano.compile.io.In(
                    variable=v,
                    value=v._theanoclass_container,
                    mutable=(v in rval.updates),
                    update=rval.updates.get(v, None))
                for v in all_required_inputs \
                        if hasattr(v, '_theanoclass_container') and not (v in inputs)]

            cache[key] = dict(theano_function=theano.function(inputs+module_inputs, rval.outputs),
                    updates=rval.updates,
                    outputs=rval.outputs,
                    mode=mode)

        return cache[key]

    def symbolic_member(self, ival, name=None):
        """
        Create a Variable instance to hold value `ival`.

        This function also immediately creates a Container object for ival.
        When the returned Variable is used as input to a `TheanoObject`
        `symbolic_fn`, (but does not appear as an argument to that symbolic_fn),
        then this Container will be used to retrieve (and store) values for the
        Variable.

        This Variable's Container's contents can be retrieved by its `get()`
        method.
        This Variable's Container's contents can be written using its
        `set(newval)` method.

        """
        if type(ival) is not int:
            raise NotImplementedError()

        v = tensor.lscalar(name)
        v._theanoclass_container = \
                theano.gof.Container(v,
                        storage=[theano._asarray(ival, dtype='int64')],
                        readonly=False)
        assert not hasattr(v, 'set')
        assert not hasattr(v, 'get')
        v.get = lambda : v._theanoclass_container.data
        def setval_in_v(newval):
            v._theanoclass_container.data = newval
        v.set = setval_in_v
        return v




