import torch
import torch.nn.functional as F
import numpy as np


device = torch.device("cpu")

class ImageToWordModel():
    def __init__(self, encoder, decoder, word_map):
        """
        :param encoder: encoder model
        :param decoder: decoder model
        :param word_map: word map
        """
        
        self.encoder = encoder
        self.decoder = decoder
        self.word_map = word_map
        self.rev_word_map = {v: k for k, v in word_map.items()}  # ix2word

    def predict(self, img, beam_size=3):
        """
        Reads an image and captions it with beam search.
        :param img: PIL image
        :param beam_size: number of sequences to consider at each decode-step
        :return: caption, weights for visualization
        """

        k = beam_size
        vocab_size = len(self.word_map)

        img = img.resize((256, 256))
        img = np.asarray(img)

    #     img = imresize(img, (256, 256))
        img = img.transpose(2, 0, 1)
        img = img / 255.
        img = torch.FloatTensor(img).to(device)[:3]
        mean = torch.Tensor([0.485, 0.456, 0.406])
        std = torch.Tensor([0.229, 0.224, 0.225])
        image = (img - mean[:,None,None]) / std[:,None,None]

        # Encode
        image = image.unsqueeze(0)  # (1, 3, 256, 256)
        encoder_out = self.encoder(image)  # (1, enc_image_size, enc_image_size, encoder_dim)
        enc_image_size = encoder_out.size(1)
        encoder_dim = encoder_out.size(3)

        # Flatten encoding
        encoder_out = encoder_out.view(1, -1, encoder_dim)  # (1, num_pixels, encoder_dim)
        num_pixels = encoder_out.size(1)

        # We'll treat the problem as having a batch size of k
        encoder_out = encoder_out.expand(k, num_pixels, encoder_dim)  # (k, num_pixels, encoder_dim)

        # Tensor to store top k previous words at each step; now they're just <start>
        k_prev_words = torch.LongTensor([[self.word_map['<start>']]] * k).to(device)  # (k, 1)

        # Tensor to store top k sequences; now they're just <start>
        seqs = k_prev_words  # (k, 1)

        # Tensor to store top k sequences' scores; now they're just 0
        top_k_scores = torch.zeros(k, 1).to(device)  # (k, 1)

        # Tensor to store top k sequences' alphas; now they're just 1s
        seqs_alpha = torch.ones(k, 1, enc_image_size, enc_image_size).to(device)  # (k, 1, enc_image_size, enc_image_size)

        # Lists to store completed sequences, their alphas and scores
        complete_seqs = list()
        complete_seqs_alpha = list()
        complete_seqs_scores = list()

        # Start decoding
        step = 1
        h, c = self.decoder.init_hidden_state(encoder_out)

        # s is a number less than or equal to k, because sequences are removed from this process once they hit <end>
        while True:

            embeddings = self.decoder.embedding(k_prev_words).squeeze(1)  # (s, embed_dim)

            awe, alpha = self.decoder.attention(encoder_out, h)  # (s, encoder_dim), (s, num_pixels)

            alpha = alpha.view(-1, enc_image_size, enc_image_size)  # (s, enc_image_size, enc_image_size)

            gate = self.decoder.sigmoid(self.decoder.f_beta(h))  # gating scalar, (s, encoder_dim)
            awe = gate * awe

            h, c = self.decoder.decode_step(torch.cat([embeddings, awe], dim=1), (h, c))  # (s, decoder_dim)

            scores = self.decoder.fc(h)  # (s, vocab_size)
            scores = F.log_softmax(scores, dim=1)

            # Add
            scores = top_k_scores.expand_as(scores) + scores  # (s, vocab_size)

            # For the first step, all k points will have the same scores (since same k previous words, h, c)
            if step == 1:
                top_k_scores, top_k_words = scores[0].topk(k, 0, True, True)  # (s)
            else:
                # Unroll and find top scores, and their unrolled indices
                top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True)  # (s)

            # Convert unrolled indices to actual indices of scores
            prev_word_inds = (top_k_words / vocab_size).long()  # (s)
            next_word_inds = top_k_words % vocab_size  # (s)

            # Add new words to sequences, alphas
            seqs = torch.cat([seqs[prev_word_inds], next_word_inds.unsqueeze(1)], dim=1)  # (s, step+1)
            seqs_alpha = torch.cat([seqs_alpha[prev_word_inds], alpha[prev_word_inds].unsqueeze(1)],
                                   dim=1)  # (s, step+1, enc_image_size, enc_image_size)

            # Which sequences are incomplete (didn't reach <end>)?
            incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if
                               next_word != self.word_map['<end>']]
            complete_inds = list(set(range(len(next_word_inds))) - set(incomplete_inds))

            # Set aside complete sequences
            if len(complete_inds) > 0:
                complete_seqs.extend(seqs[complete_inds].tolist())
                complete_seqs_alpha.extend(seqs_alpha[complete_inds].tolist())
                complete_seqs_scores.extend(top_k_scores[complete_inds])
            k -= len(complete_inds)  # reduce beam length accordingly

            # Proceed with incomplete sequences
            if k == 0:
                break
            seqs = seqs[incomplete_inds]
            seqs_alpha = seqs_alpha[incomplete_inds]
            h = h[prev_word_inds[incomplete_inds]]
            c = c[prev_word_inds[incomplete_inds]]
            encoder_out = encoder_out[prev_word_inds[incomplete_inds]]
            top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1)
            k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1)

            # Break if things have been going on too long
            if step > 50:
                break
            step += 1

        i = complete_seqs_scores.index(max(complete_seqs_scores))
        seq = complete_seqs[i]
        alphas = complete_seqs_alpha[i]

        return seq, alphas
