# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

from fairseq.models import FairseqDecoder


class FairseqIncrementalDecoder(FairseqDecoder):
    """Base class for incremental decoders.

    Incremental decoding is a special mode at inference time where the Model
    only receives a single timestep of input corresponding to the immediately
    previous output token (for input feeding) and must produce the next output
    *incrementally*. Thus the model must cache any long-term state that is
    needed about the sequence, e.g., hidden states, convolutional states, etc.

    Compared to the standard :class:`FairseqDecoder` interface, the incremental
    decoder interface allows :func:`forward` functions to take an extra keyword
    argument (*incremental_state*) that can be used to cache state across
    time-steps.

    The :class:`FairseqIncrementalDecoder` interface also defines the
    :func:`reorder_incremental_state` method, which is used during beam search
    to select and reorder the incremental state based on the selection of beams.

    To learn more about how incremental decoding works, refer to `this blog
    <http://www.telesens.co/2019/04/21/understanding-incremental-decoding-in-fairseq/>`_.
    """

    def __init__(self, dictionary):
        super().__init__(dictionary)

    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
        """
        Args:
            prev_output_tokens (LongTensor): previous decoder outputs of shape
                `(batch, tgt_len)`, for input feeding/teacher forcing
            encoder_out (Tensor, optional): output from the encoder, used for
                encoder-side attention
            incremental_state (dict): dictionary used for storing state during
                :ref:`Incremental decoding`

        Returns:
            tuple:
                - the last decoder layer's output of shape `(batch, tgt_len,
                  vocab)`
                - the last decoder layer's attention weights of shape `(batch,
                  tgt_len, src_len)`
        """
        raise NotImplementedError

    def reorder_incremental_state(self, incremental_state, new_order):
        """Reorder incremental state.

        This should be called when the order of the input has changed from the
        previous time step. A typical use case is beam search, where the input
        order changes between time steps based on the selection of beams.
        """
        seen = set()

        def apply_reorder_incremental_state(module):
            if module != self and hasattr(module, 'reorder_incremental_state') \
                    and module not in seen:
                seen.add(module)
                module.reorder_incremental_state(incremental_state, new_order)

        self.apply(apply_reorder_incremental_state)

    def set_beam_size(self, beam_size):
        """Sets the beam size in the decoder and all children."""
        if getattr(self, '_beam_size', -1) != beam_size:
            seen = set()

            def apply_set_beam_size(module):
                if module != self and hasattr(module, 'set_beam_size') \
                        and module not in seen:
                    seen.add(module)
                    module.set_beam_size(beam_size)

            self.apply(apply_set_beam_size)
            self._beam_size = beam_size
