"""Define the default Transfer class."""

from __future__ import division

from itertools import product, chain
from collections import defaultdict
from six import iteritems, itervalues

import numpy as np

from openmdao.vectors.vector import INT_DTYPE
from openmdao.vectors.transfer import Transfer
from openmdao.utils.array_utils import convert_neg, _global2local_offsets

_empty_idx_array = np.array([], dtype=INT_DTYPE)


class DefaultTransfer(Transfer):
    """
    Default NumPy transfer.
    """

    @staticmethod
    def _setup_transfers(group, recurse=True):
        """
        Compute all transfers that are owned by our parent group.

        Parameters
        ----------
        group : <Group>
            Parent group.
        recurse : bool
            Whether to call this method in subsystems.
        """
        group._transfers = {}
        iproc = group.comm.rank
        rev = group._mode == 'rev' or group._mode == 'auto'

        def merge(indices_list):
            if len(indices_list) > 0:
                return np.concatenate(indices_list)
            else:
                return _empty_idx_array

        if recurse:
            for subsys in group._subgroups_myproc:
                subsys._setup_transfers(recurse)

        # Pre-compute map from abs_names to the index of the containing subsystem
        abs2isub = {}
        for subsys, isub in zip(group._subsystems_myproc, group._subsystems_myproc_inds):
            for type_ in ['input', 'output']:
                for abs_name in subsys._var_allprocs_abs_names[type_]:
                    abs2isub[abs_name] = isub

        abs2meta = group._var_abs2meta
        allprocs_abs2meta = group._var_allprocs_abs2meta

        transfers = group._transfers
        vectors = group._vectors
        offsets = _global2local_offsets(group._get_var_offsets())

        vec_names = group._lin_rel_vec_name_list if group._use_derivatives else group._vec_names

        for vec_name in vec_names:
            relvars, _ = group._relevant[vec_name]['@all']
            relvars_in = relvars['input']
            relvars_out = relvars['output']

            # Initialize empty lists for the transfer indices
            nsub_allprocs = len(group._subsystems_allprocs)
            xfer_in = []
            xfer_out = []
            fwd_xfer_in = [[] for i in range(nsub_allprocs)]
            fwd_xfer_out = [[] for i in range(nsub_allprocs)]
            if rev:
                rev_xfer_in = [[] for i in range(nsub_allprocs)]
                rev_xfer_out = [[] for i in range(nsub_allprocs)]

            allprocs_abs2idx = group._var_allprocs_abs2idx[vec_name]
            sizes_in = group._var_sizes[vec_name]['input']
            sizes_out = group._var_sizes[vec_name]['output']
            offsets_in = offsets[vec_name]['input']
            offsets_out = offsets[vec_name]['output']

            # Loop through all explicit / implicit connections owned by this system
            for abs_in, abs_out in iteritems(group._conn_abs_in2out):
                if abs_out not in relvars_out or abs_in not in relvars_in:
                    continue

                # Only continue if the input exists on this processor
                if abs_in in abs2meta and abs_in in relvars['input']:

                    # Get meta
                    meta_in = abs2meta[abs_in]
                    meta_out = allprocs_abs2meta[abs_out]

                    idx_in = allprocs_abs2idx[abs_in]
                    idx_out = allprocs_abs2idx[abs_out]

                    # Read in and process src_indices
                    shape_in = meta_in['shape']
                    shape_out = meta_out['shape']
                    global_size_out = meta_out['global_size']
                    src_indices = meta_in['src_indices']
                    if src_indices is None:
                        pass
                    elif src_indices.ndim == 1:
                        src_indices = convert_neg(src_indices, global_size_out)
                    else:
                        if len(shape_out) == 1 or shape_in == src_indices.shape:
                            src_indices = src_indices.flatten()
                            src_indices = convert_neg(src_indices, global_size_out)
                        else:
                            # TODO: this duplicates code found
                            # in System._setup_scaling.
                            entries = [list(range(x)) for x in shape_in]
                            cols = np.vstack(src_indices[i] for i in product(*entries))
                            dimidxs = [convert_neg(cols[:, i], shape_out[i])
                                       for i in range(cols.shape[1])]
                            src_indices = np.ravel_multi_index(dimidxs, shape_out)

                    # 1. Compute the output indices
                    offset = offsets_out[iproc, idx_out]
                    if src_indices is None:
                        output_inds = np.arange(offset, offset + meta_in['size'], dtype=INT_DTYPE)
                    else:
                        output_inds = src_indices + offset

                    # 2. Compute the input indices
                    input_inds = np.arange(offsets_in[iproc, idx_in],
                                           offsets_in[iproc, idx_in] +
                                           sizes_in[iproc, idx_in], dtype=INT_DTYPE)

                    # Now the indices are ready - input_inds, output_inds
                    xfer_in.append(input_inds)
                    xfer_out.append(output_inds)

                    isub = abs2isub[abs_in]
                    fwd_xfer_in[isub].append(input_inds)
                    fwd_xfer_out[isub].append(output_inds)
                    if rev and abs_out in abs2isub:
                        isub = abs2isub[abs_out]
                        rev_xfer_in[isub].append(input_inds)
                        rev_xfer_out[isub].append(output_inds)

            xfer_in = merge(xfer_in)
            xfer_out = merge(xfer_out)
            for isub in range(nsub_allprocs):
                fwd_xfer_in[isub] = merge(fwd_xfer_in[isub])
                fwd_xfer_out[isub] = merge(fwd_xfer_out[isub])
                if rev:
                    rev_xfer_in[isub] = merge(rev_xfer_in[isub])
                    rev_xfer_out[isub] = merge(rev_xfer_out[isub])

            out_vec = vectors['output'][vec_name]

            transfers[vec_name] = {}
            xfer_all = DefaultTransfer(vectors['input'][vec_name], out_vec,
                                       xfer_in, xfer_out, group.comm)
            transfers[vec_name]['fwd', None] = xfer_all
            if rev:
                transfers[vec_name]['rev', None] = xfer_all
            for isub in range(nsub_allprocs):
                transfers[vec_name]['fwd', isub] = DefaultTransfer(
                    vectors['input'][vec_name], vectors['output'][vec_name],
                    fwd_xfer_in[isub], fwd_xfer_out[isub], group.comm)
                if rev:
                    transfers[vec_name]['rev', isub] = DefaultTransfer(
                        vectors['input'][vec_name], vectors['output'][vec_name],
                        rev_xfer_in[isub], rev_xfer_out[isub], group.comm)

        if group._use_derivatives:
            transfers['nonlinear'] = transfers['linear']

    @staticmethod
    def _setup_discrete_transfers(group, recurse=True):
        """
        Compute all transfers that are owned by our parent group.

        Parameters
        ----------
        group : <Group>
            Parent group.
        recurse : bool
            Whether to call this method in subsystems.
        """
        group._discrete_transfers = transfers = defaultdict(list)
        offset = len(group.pathname) + 1 if group.pathname else 0

        for tgt, src in iteritems(group._conn_discrete_in2out):
            src_sys, src_var = src[offset:].split('.', 1)
            tgt_sys, tgt_var = tgt[offset:].split('.', 1)
            xfer = (src_sys, src_var, tgt_sys, tgt_var)
            transfers[tgt_sys].append(xfer)
            transfers[None].append(xfer)

    def _initialize_transfer(self, in_vec, out_vec):
        """
        Set up the transfer; do any necessary pre-computation.

        Optionally implemented by the subclass.

        Parameters
        ----------
        in_vec : <Vector>
            reference to the input vector.
        out_vec : <Vector>
            reference to the output vector.
        """
        pass

    def transfer(self, in_vec, out_vec, mode='fwd'):
        """
        Perform transfer.

        Parameters
        ----------
        in_vec : <Vector>
            pointer to the input vector.
        out_vec : <Vector>
            pointer to the output vector.
        mode : str
            'fwd' or 'rev'.

        """
        if mode == 'fwd':
            # this works whether the vecs have multi columns or not due to broadcasting
            in_vec._data[self._in_inds] = out_vec._data[self._out_inds]

        else:  # rev
            np.add.at(out_vec._data, self._out_inds, in_vec._data[self._in_inds])
