"""Class for generating captions from an image-to-text model.
credit: https://github.com/tensorflow/models/blob/master/research/im2txt/im2txt/inference_utils/caption_generator.py

"""


from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import heapq
import math


import numpy as np


class Caption(object):
    """Represents a complete or partial caption."""

    def __init__(self, sentence, state, logprob, score, metadata=None):
        """Initializes the Caption.

        Args:
            sentence: List of word ids in the caption.
            state: Model state after generating the previous word.
            logprob: Log-probability of the caption.
            score: Score of the caption.
            metadata: Optional metadata associated with the partial sentence. If not
               None, a list of strings with the same length as 'sentence'.
        """
        self.sentence = sentence
        self.state = state
        self.logprob = logprob
        self.score = score
        self.metadata = metadata

    def __cmp__(self, other):
        """Compares Captions by score."""
        assert isinstance(other, Caption)
        if self.score == other.score:
            return 0
        elif self.score < other.score:
            return -1
        else:
            return 1

    # For Python 3 compatibility (__cmp__ is deprecated).
    def __lt__(self, other):
        assert isinstance(other, Caption)
        return self.score < other.score

    # Also for Python 3 compatibility.
    def __eq__(self, other):
        assert isinstance(other, Caption)
        return self.score == other.score


class TopN(object):
    """Maintains the top n elements of an incrementally provided set."""

    def __init__(self, n):
        self._n = n
        self._data = []

    def size(self):
        assert self._data is not None
        return len(self._data)

    def push(self, x):
        """Pushes a new element."""
        assert self._data is not None
        if len(self._data) < self._n:
            heapq.heappush(self._data, x)
        else:
            heapq.heappushpop(self._data, x)

    def extract(self, sort=False):
        """Extracts all elements from the TopN. This is a destructive operation.
        The only method that can be called immediately after extract() is reset().

        Args:
            sort: Whether to return the elements in descending sorted order.

        Returns:
            A list of data; the top n elements provided to the set.
        """
        assert self._data is not None
        data = self._data
        self._data = None
        if sort:
            data.sort(reverse=True)
        return data

    def reset(self):
        """Returns the TopN to an empty state."""
        self._data = []


class CaptionGenerator(object):
    """Class to generate captions from an image-to-text model."""

    def __init__(self,
                 model,
                 vocab,
                 beam_size=3,
                 max_caption_length=20,
                 length_normalization_factor=0.0):
        """Initializes the generator.

        Args:
            model: Object encapsulating a trained image-to-text model. Must have
                methods feed_image() and inference_step(). For example, an instance of
                InferenceWrapperBase.
            vocab: A Vocabulary object.
            beam_size: Beam size to use when generating captions.
            max_caption_length: The maximum caption length before stopping the search.
                length_normalization_factor: If != 0, a number x such that captions are
                scored by logprob/length^x, rather than logprob. This changes the
                relative scores of captions depending on their lengths. For example, if
                x > 0 then longer captions will be favored.
        """
        self.vocab = vocab
        self.model = model

        self.beam_size = beam_size
        self.max_caption_length = max_caption_length
        self.length_normalization_factor = length_normalization_factor

    def beam_search(self, sess, encoded_image):
        """Runs beam search caption generation on a single image.

        Args:
            sess: TensorFlow Session object.
            encoded_image: An encoded image string.

        Returns:
            A list of Caption sorted by descending score.
        """
        # Feed in the image to get the initial state.
        initial_state = self.model.feed_image(sess, encoded_image)

        initial_beam = Caption(
            sentence=[self.vocab.start_id],
            state=initial_state[0],
            logprob=0.0,
            score=0.0,
            metadata=[""])
        partial_captions = TopN(self.beam_size)
        partial_captions.push(initial_beam)
        complete_captions = TopN(self.beam_size)

        # Run beam search.
        for _ in range(self.max_caption_length - 1):
            partial_captions_list = partial_captions.extract()
            partial_captions.reset()
            input_feed = np.array([c.sentence[-1]
                                   for c in partial_captions_list])
            state_feed = np.array([c.state for c in partial_captions_list])

            softmax, new_states, metadata = self.model.inference_step(sess,
                                                                      input_feed,
                                                                      state_feed)

            for i, partial_caption in enumerate(partial_captions_list):
                word_probabilities = softmax[i]
                state = new_states[i]
                # For this partial caption, get the beam_size most probable next words.
                words_and_probs = list(enumerate(word_probabilities))
                words_and_probs.sort(key=lambda x: -x[1])
                words_and_probs = words_and_probs[0:self.beam_size]
                # Each next word gives a new partial caption.
                for w, p in words_and_probs:
                    if p < 1e-12:
                        continue  # Avoid log(0).
                    sentence = partial_caption.sentence + [w]
                    logprob = partial_caption.logprob + math.log(p)
                    score = logprob
                    if metadata:
                        metadata_list = partial_caption.metadata + \
                            [metadata[i]]
                    else:
                        metadata_list = None
                    if w == self.vocab.end_id:
                        if self.length_normalization_factor > 0:
                            score /= len(sentence)**self.length_normalization_factor
                        beam = Caption(sentence, state, logprob,
                                       score, metadata_list)
                        complete_captions.push(beam)
                    else:
                        beam = Caption(sentence, state, logprob,
                                       score, metadata_list)
                        partial_captions.push(beam)
            if partial_captions.size() == 0:
                # We have run out of partial candidates; happens when beam_size = 1.
                break

        # If we have no complete captions then fall back to the partial captions.
        # But never output a mixture of complete and partial captions because a
        # partial caption could have a higher score than all the complete captions.
        if not complete_captions.size():
            complete_captions = partial_captions

        return complete_captions.extract(sort=True)
