"""
This module provides utility functions for the Scan Op

See scan.py for details on scan
"""
__docformat__ = 'restructedtext en'
__authors__ = ("Razvan Pascanu "
               "Frederic Bastien "
               "James Bergstra "
               "Pascal Lamblin "
               "Arnaud Bergeron")
__copyright__ = "(c) 2010, Universite de Montreal"
__contact__ = "Razvan Pascanu <r.pascanu@gmail>"


import copy
import logging
from itertools import izip

import numpy

import theano
from theano.compile.pfunc import rebuild_collect_shared
from theano import gof
from theano import tensor, scalar
from theano.gof.python25 import all
from theano.tensor.basic import get_constant_value


# Logging function for sending warning or info
_logger = logging.getLogger('theano.scan_utils')


def expand(tensor_var, size):
    """
    Given ``tensor_var``, a Theano tensor of shape (d1, d2, ..), this
    function constructs a rval Theano tensor of shape (d1 + size, d2, ..)
    filled with 0s, except the first d1 entries which are taken from
    ``tensor_var``, namely:
        rval[:d1] = tensor_var

    :param tensor_var: Theano tensor variable
    :param size: int
    """
    # Corner case that I might use in an optimization
    if size == 0:
        return tensor_var
    shapes = [tensor_var.shape[x] for x in xrange(tensor_var.ndim)]
    zeros_shape = [size + shapes[0]] + shapes[1:]
    empty = tensor.zeros(zeros_shape,
                               dtype=tensor_var.dtype)
    return tensor.set_subtensor(empty[:shapes[0]], tensor_var)


def to_list(ls):
    """
    Converts ``ls`` to list if it is a tuple, or wraps ``ls`` into a list if
    it is not a list already
    """
    if isinstance(ls, (list, tuple)):
        return list(ls)
    else:
        return [ls]


class until(object):
    """
    Theano can end on a condition. In order to differentiate this condition
    from the other outputs of scan, this class is used to wrap the condition
    around it.
    """
    def __init__(self, condition):
        self.condition = tensor.as_tensor_variable(condition)
        assert self.condition.ndim == 0


def get_updates_and_outputs(ls):
    """
    Parses the list ``ls`` into outputs and updates. The semantics
    of ``ls`` is defined by the constructive function of scan.
    The elemets of ``ls`` are either a list of expressions representing the
    outputs/states, a dictionary of updates or a condition.
    """
    def is_list_outputs(elem):
        if (isinstance(elem, (list, tuple)) and
            all([isinstance(x, theano.Variable) for x in elem])):
            return True
        if isinstance(elem, theano.Variable):
            return True
        return False

    def is_updates(elem):
        if isinstance(elem, dict):
            return True
        # Dictionaries can be given as lists of tuples
        if (isinstance(elem, (list, tuple)) and
            all([isinstance(x, (list, tuple)) and len(x) == 2
                 for x in elem])):
            return True
        return False

    def is_condition(elem):
        return isinstance(elem, until)

    if is_list_outputs(ls):
        return None, to_list(ls), {}
    if is_updates(ls):
        return None, [], dict(ls)
    if not isinstance(ls, (list, tuple)):
        raise ValueError(('Scan can not parse the return value'
                          ' of your constructive function given to scan'))
    ls = list(ls)
    deprecation_msg = ('The return value of the lambda function'
                    ' has been restricted. you have to always return first the'
                    ' outputs (if any), afterwards the updates (if any) and'
                    ' at the end the condition')
    error_msg = ('Scan can not parse the return value of your constructive '
                 'funtion given to scan')
    if len(ls) == 2:
        if is_list_outputs(ls[0]):
            if is_updates(ls[1]):
                return (None, to_list(ls[0]), dict(ls[1]))
            elif is_condition(ls[1]):
                return (ls[1].condition, to_list(ls[0]), {})
            else:
                raise ValueError(error_msg)
        elif is_updates(ls[0]):
            if is_outputs(ls[1]):
                raise ValueError(deprecation_msg)
            elif is_condition(ls[1]):
                return (ls[1].condition, [], dict(ls[0]))
            else:
                raise ValueError(error_msg)
        else:
            raise ValueError(error_msg)
    elif len(ls) == 3:
        if is_outputs(ls[0]):
            if is_updates(ls[1]):
                if is_condition(ls[2]):
                    return (ls[2].condition, to_list(ls[0]), dict(ls[1]))
                else:
                    raise ValueError(error_msg)
            else:
                raise ValueError(error_msg)
        else:
            raise ValueError(error_msg)


def clone(output, replace=None, strict=True, copy_inputs=True):
    """
    Function that allows replacing subgraphs of a computational
    graph. It returns a copy of the initial subgraph with the corresponding
    substitutions.

    :type output: Theano Variables (or Theano expressions)
    :param outputs: Theano expression that represents the computational
                    graph

    :type replace: dict
    :param replace: dictionary describing which subgraphs should be
                    replaced by what
    """

    inps, outs, other_stuff = rebuild_collect_shared(output,
                                                     [],
                                                     replace,
                                                     [],
                                                     strict,
                                                     copy_inputs)
    return outs


def canonical_arguments(sequences,
                        outputs_info,
                        non_sequences,
                        go_backwards,
                        n_steps):
    """
    This re-writes the arguments obtained from scan into a more friendly
    form for the scan_op.

    Mainly it makes sure that arguments are given as lists of dictionaries,
    and that the different fields of of a dictionary are set to default
    value if the user has not provided any.
    """
    states_info = to_list(outputs_info)
    parameters = [tensor.as_tensor_variable(x) for x in to_list(non_sequences)]

    inputs = []
    if n_steps is not None:
        negative_n_steps = tensor.lt(tensor.as_tensor_variable(n_steps), 0)

    for input in to_list(sequences):
        if not isinstance(input, dict):
            nw_input = tensor.as_tensor_variable(input)
            if go_backwards:
                nw_input = nw_input[::-1]
            if n_steps is not None:
                nw_input = tensor.switch(negative_n_steps, nw_input[::-1],
                                         nw_input)
            inputs.append(tensor.as_tensor_variable(nw_input))
        elif input.get('taps', True) is None:
            nw_input = tensor.as_tensor_variable(input['input'])
            if go_backwards:
                nw_input = nw_input[::-1]
            if n_steps is not None:
                nw_input = tensor.switch(negative_n_steps, nw_input[::-1],
                                         nw_input)
            inputs.append(nw_input)
        elif input.get('taps', None):
            mintap = numpy.min(input['taps'])
            maxtap = numpy.max(input['taps'])
            orig_input = tensor.as_tensor_variable(input['input'])
            if go_backwards:
                orig_input = orig_input[::-1]
            if n_steps is not None:
                orig_input = tensor.switch(negative_n_steps, orig_input[::-1],
                                         orig_input)
            for k in input['taps']:
                # We cut the sequence such that seq[i] to correspond to
                # seq[i-k]
                if maxtap < 0:
                    offset_max = abs(maxtap)
                else:
                    offset_max = 0
                if mintap < 0:
                    offset_min = abs(mintap)
                else:
                    offset_min = 0
                nw_input = orig_input
                if maxtap == mintap and maxtap != 0:
                    if maxtap > 0:
                        nw_input = nw_input[maxtap:]
                    else:
                        nw_input = nw_input[:maxtap]
                else:
                    st = k + offset_min
                    if maxtap > 0:
                        ed = - (maxtap + offset_min - st)
                    else:
                        ed = - (offset_min -st)
                    if ed != 0:
                        nw_input = nw_input[st:ed]
                    else:
                        nw_input = nw_input[st:]
                inputs.append(nw_input)
        else:
            raise ValueError('Provided sequence makes no sense', str(input))

    # Since we've added all sequences now we need to level them up based on
    # n_steps or their different shapes
    if n_steps is None:
        if len(inputs) == 0:
            # No information about the number of steps
            raise ValueError('You need to provide either at least '
                             'one sequence over which scan should loop '
                             'or a number of steps for scan to loop. '
                             'Neither of the two had been provided !')
        T = inputs[0].shape[0]
        for input in inputs[1:]:
            T = tensor.minimum(T, input.shape[0])
    else:
        T = abs(tensor.as_tensor(n_steps))
    # Level up sequences
    inputs = [input[:T] for input in inputs]

    # wrap outputs info in a dictionary if they are not already in one
    for i, state in enumerate(states_info):
        if state is not None and not isinstance(state, dict):
            states_info[i] = dict(initial=tensor.as_tensor_variable(state),
                                  taps=[-1])
        elif isinstance(state, dict):
            if not state.get('initial', None) and state.get('taps', None):
                raise ValueError(('If you are using slices of an output '
                                  'you need to provide a initial state '
                                  'for it'), state)
            elif state.get('initial', None) and not state.get('taps', None):
                # initial state but taps not provided
                if 'taps' in state:
                    # explicitly provided a None for taps
                    _logger.warning(
                        ('Output %s ( index %d) has a initial '
                         'state but taps is explicitly set to None '),
                        getattr(states_info[i]['initial'], 'name', 'None'), i)
                states_info[i]['taps'] = [-1]
                states_info[i]['initial'] = \
                        tensor.as_tensor_variable(state['initial'])
            elif state.get('initial', None):
                states_info[i]['initial'] = \
                        tensor.as_tensor_variable(state['initial'])
        else:
            # if a None is provided as the output info we replace it
            # with an empty dict() to simplify handling
            states_info[i] = dict()
    return inputs, states_info, parameters, T


def infer_shape(outs, inputs, input_shapes):
    '''
    Compute the shape of the outputs given the shape of the inputs
    of a theano graph.

    We do it this way to avoid compiling the inner function just to get
    the shape. Changes to ShapeFeature could require changes in this function.
    '''
    # We use a ShapeFeature because it has all the necessary logic
    # inside.  We don't use the full ShapeFeature interface, but we
    # let it initialize itself with an empty fgraph, otherwise we will
    # need to do it manually
    for inp, inp_shp in izip(inputs, input_shapes):
        if inp_shp is not None and len(inp_shp) != inp.ndim:
            assert len(inp_shp) == inp.ndim

    shape_feature = tensor.opt.ShapeFeature()
    shape_feature.on_attach(theano.gof.FunctionGraph([], []))

    # Initialize shape_of with the input shapes
    for inp, inp_shp in izip(inputs, input_shapes):
        shape_feature.set_shape(inp, inp_shp)

    def local_traverse(out):
        '''
        Go back in the graph, from out, adding computable shapes to shape_of.
        '''

        if out in shape_feature.shape_of:
            # Its shape is already known
            return
        elif out.owner is None:
            # This is an input of the graph
            shape_feature.init_r(out)
        else:
            # Recurse over inputs
            for inp in out.owner.inputs:
                if not inp in shape_feature.shape_of:
                    local_traverse(inp)

            # shape_feature.on_import does not actually use an fgraph
            # It will call infer_shape and set_shape appropriately
            dummy_fgraph = None
            shape_feature.on_import(dummy_fgraph, out.owner)

    ret = []
    for o in outs:
        local_traverse(o)
        ret.append(shape_feature.shape_of[o])
    return ret


def allocate_memory(T, y_info, y):
    """
    Allocates memory for an output of scan.

    :param T: scalar
        Variable representing the number of steps scan will run
    :param y_info: dict
        Dictionary describing the output (more specifically describing shape
        information for the output
    :param y: Tensor variable
        Expression describing the computation resulting in out entry of y.
        It can be used to infer the shape of y
    """
    if 'shape' in y_info:
        return tensor.zeros([T, ] + list(y_info['shape']),
                            dtype=y.dtype)
    else:
        inputs = gof.graph.inputs([y])
        ins_shapes = []
        for inp in inputs:
            in_shape = [inp.shape[k] for k in xrange(inp.ndim)]
            ins_shapes.append(in_shape)
        shape = infer_shape([y], inputs, ins_shapes)[0]
        return tensor.zeros([T, ] + shape, dtype=y.dtype)


class ScanPermutation(gof.Op):
    def __init__(self, mintap=0, inplace=False):
        self.inplace = inplace
        self.mintap = mintap
        if inplace:
            self.destroy_map = {0: [0]}

    def __eq__(self, other):
        return type(self) == type(other) and self.inplace == other.inplace

    def __hash__(self):
        return hash(type(self)) ^ hash(self.inplace)

    def __str__(self):
        if self.inplace:
            return "scan_permutation{inplace}"
        else:
            return "scan_permutation"

    def make_node(self, membuffer, index):
        # index has to be a scalar
        assert index.ndim == 0
        # we neeed at least one dimension
        assert membuffer.ndim > 0
        return gof.Apply(self, [membuffer, index], [membuffer.type()])

    def perform(self, node, inputs, outputs):
        membuffer = inputs[0]
        index = inputs[1] + self.mintap
        out = outputs[0]
        if index % membuffer.shape[0] == 0:
            if self.inplace:
                out[0] = membuffer
            else:
                out[0] = membuffer.copy()
        else:
            pos = index % membuffer.shape[0]
            if outputs[0] is membuffer:
                membuffer = membuffer.copy()
            print pos
            out[0][:membuffer.shape[0] - pos] = membuffer[pos:]
            out[0][membuffer.shape[0] - pos:] = membuffer[:pos]

    def R_op(self, inputs, eval_points):
        if eval_points[0] is None:
            return [None]
        return self.make_node(eval_points[0], inputs[1]).outputs

    def grad(self, inputs, grads):
        pos = inputs[0].shape[0] - (inputs[1] % inputs[0].shape[0])
        return self.make_node(grads[0], pos).outputs
