"""
 Copyright (c) 2022, salesforce.com, inc.
 All rights reserved.
 SPDX-License-Identifier: BSD-3-Clause
 For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""

import torch
import torch.nn.functional as F
from copy import deepcopy
from torch import nn

from ...common.registry import registry
from ..base_model import MomentumDistilationMixin, SharedQueueMixin
from ..med import XBertEncoder, XBertLMHeadDecoder
from ..vit import VisionTransformerEncoder
from . import tie_encoder_decoder_weights
from .blip import BlipBase
from .blip_outputs import BlipIntermediateOutput, BlipOutput, BlipSimilarity


@registry.register_model('blip_pretrain')
class BlipPretrain(BlipBase, SharedQueueMixin, MomentumDistilationMixin):
    """
    BLIP pretrain model.

    Supported model types:
        - base: BLIP base model before pretraining.
    """

    PRETRAINED_MODEL_CONFIG_DICT = {
        'base': 'configs/models/blip_pretrain_base.yaml',
        # "large": "configs/models/blip_pretrain_large.yaml",
    }

    def __init__(
        self,
        image_encoder,
        text_encoder,
        text_decoder,
        queue_size,
        alpha=0.4,
        embed_dim=256,
        momentum=0.995,
        tie_enc_dec_weights=True,
        max_txt_len=30,
    ):
        super().__init__()

        self.tokenizer = self.init_tokenizer()

        text_encoder.resize_token_embeddings(len(self.tokenizer))
        text_decoder.resize_token_embeddings(len(self.tokenizer))

        if tie_enc_dec_weights:
            tie_encoder_decoder_weights(
                encoder=text_encoder,
                decoder=text_decoder.bert,
                base_model_prefix='',
                skip_key='/attention',
            )

        self.visual_encoder = image_encoder

        self.text_encoder = text_encoder
        self.text_decoder = text_decoder

        # creating projection layers for ITC
        text_width = text_encoder.config.hidden_size
        vision_width = image_encoder.vision_width

        self.vision_proj = nn.Linear(vision_width, embed_dim)
        self.text_proj = nn.Linear(text_width, embed_dim)

        self.itm_head = nn.Linear(text_width, 2)

        # create the momentum encoder
        self.visual_encoder_m = deepcopy(self.visual_encoder)
        self.text_encoder_m = deepcopy(self.text_encoder)

        self.vision_proj_m = deepcopy(self.vision_proj)
        self.text_proj_m = deepcopy(self.text_proj)

        self.model_pairs = [
            [self.visual_encoder, self.visual_encoder_m],
            [self.text_encoder, self.text_encoder_m],
            [self.vision_proj, self.vision_proj_m],
            [self.text_proj, self.text_proj_m],
        ]
        self.copy_params()

        # create the queue
        self.register_buffer('image_queue', torch.randn(embed_dim, queue_size))
        self.register_buffer('text_queue', torch.randn(embed_dim, queue_size))
        self.register_buffer('queue_ptr', torch.zeros(1, dtype=torch.long))

        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)

        self.queue_size = queue_size
        self.momentum = momentum
        self.temp = nn.Parameter(0.07 * torch.ones([]))

        self.alpha = alpha
        self.max_txt_len = max_txt_len

    def _rampup_factor(self, epoch, iters, num_iters_per_epoch):
        return min(1, (epoch * num_iters_per_epoch + iters) / (2 * num_iters_per_epoch))

    def forward(self, samples):
        """
        Args:
            samples (dict): A dictionary containing the following keys:
                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). The input images. Default: H=224, W=224.
                - text_input (list): A list of length batch_size, each element is a string of text/caption.
                - epoch (int): The current epoch.
                - iters (int): The current iteration.
                - num_iters_per_epoch (int): The number of iterations per epoch.

        Returns:
            BlipOutput: A BlipOutput object containing loss and intermediate output. See ``lavis.models.blip_models.blip_outputs.BlipOutput`` for more details.

        Examples:
            >>> import torch
            >>> from lavis.models import load_model
            >>> model = load_model("blip_pretrain", "base")
            >>> images = torch.randn(4, 3, 224, 224)
            >>> text_input = ["caption of image 1", "another caption of image 1", "caption of image 2", "caption of image 3"]
            >>> samples = {"image": images, "text_input": text_input, "epoch": 0, "iters": 0, "num_iters_per_epoch": 100}
            >>> output = model(samples)
            >>> output.keys()
            odict_keys(['sims', 'intermediate_output', 'loss', 'loss_itc', 'loss_itm', 'loss_lm'])

            >>> output.intermediate_output.keys()
            odict_keys(['image_embeds', 'text_embeds', 'image_embeds_m', 'text_embeds_m', 'encoder_output', 'encoder_output_neg', 'itm_logits', 'itm_labels', 'decoder_output', 'decoder_labels'])
            >>> output.intermediate_output.image_embeds.shape
            >>> # shape: (batch_size, num_patches, embed_dim)
            torch.Size([4, 197, 768])
            >>> output.intermediate_output.text_embeds.shape
            >>> # shape: (batch_size, max_txt_len, embed_dim)
            torch.Size([4, 30, 768])
            >>> output.intermediate_output.image_embeds_m.shape
            >>> # shape: (batch_size, num_patches, embed_dim)
            torch.Size([4, 197, 768])
            >>> output.intermediate_output.text_embeds_m.shape
            >>> # shape: (batch_size, max_txt_len, embed_dim)
            torch.Size([4, 30, 768])
            >>> output.intermediate_output.itm_logits.shape
            >>> # shape: (batch_size * 3, 2)
            torch.Size([12, 2])
            >>> output.intermediate_output.itm_labels.shape
            >>> # shape: (batch_size * 3,)
            torch.Size([12])
            >>> output.intermediate_output.encoder_output.last_hidden_state.shape
            >>> # shape: (batch_size, max_txt_len, embed_dim)
            torch.Size([4, 30, 768])
            >>> output.intermediate_output.encoder_output_m.last_hidden_state.shape
            >>> # shape: (batch_size, max_txt_len, embed_dim)
            torch.Size([4, 30, 768])
            >>> output.intermediate_output.decoder_output.logits.shape
            >>> # shape: (batch_size, max_txt_len, vocab_size)
            torch.Size([4, 30, 30524])
            >>> output.intermediate_output.decoder_labels.shape
            >>> # shape: (batch_size, max_txt_len)
            torch.Size([4, 30])
        """

        image = samples['image']
        caption = samples['text_input']

        alpha = self.alpha * self._rampup_factor(
            epoch=samples['epoch'],
            iters=samples['iters'],
            num_iters_per_epoch=samples['num_iters_per_epoch'],
        )

        with torch.no_grad():
            self.temp.clamp_(0.001, 0.5)

        # image embeddings and features
        image_embeds = self.visual_encoder.forward_features(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
        image_feat = F.normalize(self.vision_proj(image_embeds[:, 0, :]), dim=-1)

        text = self.tokenizer(
            caption,
            padding='max_length',
            truncation=True,
            max_length=self.max_txt_len,
            return_tensors='pt',
        ).to(image.device)

        # text embeddings and features
        text_output = self.text_encoder.forward_text(text)
        text_embeds = text_output.last_hidden_state
        text_feat = F.normalize(self.text_proj(text_embeds[:, 0, :]), dim=-1)

        # get momentum features
        with torch.no_grad():
            self._momentum_update()
            image_embeds_m = self.visual_encoder_m(image)
            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:, 0, :]), dim=-1)
            image_feat_all = torch.cat([image_feat_m.t(), self.image_queue.clone().detach()], dim=1)

            text_output_m = self.text_encoder_m.forward_text(text)
            text_embeds_m = text_output_m.last_hidden_state
            text_feat_m = F.normalize(self.text_proj_m(text_embeds_m[:, 0, :]), dim=-1)
            text_feat_all = torch.cat([text_feat_m.t(), self.text_queue.clone().detach()], dim=1)

            sim_i2t_m = image_feat_m @ text_feat_all / self.temp
            sim_t2i_m = text_feat_m @ image_feat_all / self.temp

            sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
            sim_targets.fill_diagonal_(1)

            sim_i2t_targets = (alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets)
            sim_t2i_targets = (alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets)

        sim_i2t = image_feat @ text_feat_all / self.temp
        sim_t2i = text_feat @ image_feat_all / self.temp

        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1) * sim_i2t_targets, dim=1).mean()
        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1) * sim_t2i_targets, dim=1).mean()

        loss_itc = (loss_i2t + loss_t2i) / 2

        self._dequeue_and_enqueue(image_feat_m, text_feat_m)

        # Image-text Matching
        encoder_input_ids = text.input_ids.clone()
        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id

        # forward the positve image-text pair
        bs = image.size(0)
        output_pos = self.text_encoder(
            encoder_input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        with torch.no_grad():
            weights_t2i = F.softmax(sim_t2i[:, :bs], dim=1) + 1e-4
            weights_t2i.fill_diagonal_(0)
            weights_i2t = F.softmax(sim_i2t[:, :bs], dim=1) + 1e-4
            weights_i2t.fill_diagonal_(0)

        # select a negative image for each text
        image_embeds_neg = []
        for b in range(bs):
            neg_idx = torch.multinomial(weights_t2i[b], 1).item()
            image_embeds_neg.append(image_embeds[neg_idx])
        image_embeds_neg = torch.stack(image_embeds_neg, dim=0)

        # select a negative text for each image
        text_ids_neg = []
        text_atts_neg = []
        for b in range(bs):
            neg_idx = torch.multinomial(weights_i2t[b], 1).item()
            text_ids_neg.append(encoder_input_ids[neg_idx])
            text_atts_neg.append(text.attention_mask[neg_idx])

        text_ids_neg = torch.stack(text_ids_neg, dim=0)
        text_atts_neg = torch.stack(text_atts_neg, dim=0)

        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg], dim=0)
        text_atts_all = torch.cat([text.attention_mask, text_atts_neg], dim=0)

        image_embeds_all = torch.cat([image_embeds_neg, image_embeds], dim=0)
        image_atts_all = torch.cat([image_atts, image_atts], dim=0)

        output_neg = self.text_encoder(
            text_ids_all,
            attention_mask=text_atts_all,
            encoder_hidden_states=image_embeds_all,
            encoder_attention_mask=image_atts_all,
            return_dict=True,
        )

        vl_embeddings = torch.cat(
            [
                output_pos.last_hidden_state[:, 0, :],
                output_neg.last_hidden_state[:, 0, :],
            ],
            dim=0,
        )
        itm_logits = self.itm_head(vl_embeddings)

        itm_labels = torch.cat(
            [torch.ones(bs, dtype=torch.long), torch.zeros(2 * bs, dtype=torch.long)],
            dim=0,
        ).to(image.device)
        loss_itm = F.cross_entropy(itm_logits, itm_labels)

        # LM
        decoder_input_ids = text.input_ids.clone()
        decoder_input_ids[:, 0] = self.tokenizer.bos_token_id
        decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100)

        decoder_output = self.text_decoder(
            decoder_input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            labels=decoder_targets,
            return_dict=True,
        )

        loss_lm = decoder_output.loss

        return BlipOutput(
            loss=loss_itc + loss_itm + loss_lm,
            loss_itc=loss_itc,
            loss_itm=loss_itm,
            loss_lm=loss_lm,
            sims=BlipSimilarity(
                sim_i2t=sim_i2t,
                sim_t2i=sim_t2i,
                sim_i2t_m=sim_i2t_m,
                sim_t2i_m=sim_t2i_m,
                sim_i2t_targets=sim_i2t_targets,
                sim_t2i_targets=sim_t2i_targets,
            ),
            intermediate_output=BlipIntermediateOutput(
                image_embeds=image_embeds,
                text_embeds=text_embeds,
                image_embeds_m=image_embeds_m,
                text_embeds_m=text_embeds_m,
                encoder_output=output_pos,
                encoder_output_neg=output_neg,
                itm_logits=itm_logits,
                itm_labels=itm_labels,
                decoder_output=decoder_output,
                decoder_labels=decoder_targets,
            ),
        )

    def reset_queue_ptr(self):
        self.queue_ptr = torch.zeros(1, dtype=torch.long)

    @classmethod
    def from_config(cls, cfg=None):
        # set from_pretrained=True to load weights for 'bert-base-uncased'
        image_encoder = VisionTransformerEncoder.from_config(cfg, from_pretrained=True)
        text_encoder = XBertEncoder.from_config(cfg, from_pretrained=True)
        text_decoder = XBertLMHeadDecoder.from_config(cfg, from_pretrained=True)

        embed_dim = cfg.get('embed_dim', 256)
        momentum = cfg.get('momentum', 0.995)
        alpha = cfg.get('alpha', 0.4)
        max_txt_len = cfg.get('max_txt_len', 30)
        queue_size = cfg.get('queue_size', 57600)

        model = cls(
            image_encoder=image_encoder,
            text_encoder=text_encoder,
            text_decoder=text_decoder,
            embed_dim=embed_dim,
            queue_size=queue_size,
            momentum=momentum,
            alpha=alpha,
            tie_enc_dec_weights=True,
            max_txt_len=max_txt_len,
        )

        # [IMPORTANT] to reset queue pointer to 0.
        # Otherwise when updating last batch in the queue, the batch size and remaining queue length may be un-equal.
        model.reset_queue_ptr()

        return model
