"""
 * Copyright (c) 2022, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 * By Junnan Li
"""
from .med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer
import transformers

transformers.logging.set_verbosity_error()

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

from .blip import create_vit, init_tokenizer, load_checkpoint


class BLIP_Pretrain(nn.Module):
    def __init__(
        self,
        med_config="configs/bert_config.json",
        image_size=224,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
        embed_dim=256,
        queue_size=57600,
        momentum=0.995,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer, 0
        )

        if vit == "base":
            checkpoint = torch.hub.load_state_dict_from_url(
                url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
                map_location="cpu",
                check_hash=True,
            )
            state_dict = checkpoint["model"]
            msg = self.visual_encoder.load_state_dict(state_dict, strict=False)
        elif vit == "large":
            from timm.models.helpers import load_custom_pretrained
            from timm.models.vision_transformer import default_cfgs

            load_custom_pretrained(
                self.visual_encoder, default_cfgs["vit_large_patch16_224_in21k"]
            )

        self.tokenizer = init_tokenizer()
        encoder_config = BertConfig.from_json_file(med_config)
        encoder_config.encoder_width = vision_width
        self.text_encoder = BertModel.from_pretrained(
            "bert-base-uncased", config=encoder_config, add_pooling_layer=False
        )
        self.text_encoder.resize_token_embeddings(len(self.tokenizer))

        text_width = self.text_encoder.config.hidden_size

        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 momentum encoders
        self.visual_encoder_m, vision_width = create_vit(vit, image_size)
        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
        self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)
        self.text_proj_m = nn.Linear(text_width, embed_dim)

        self.model_pairs = [
            [self.visual_encoder, self.visual_encoder_m],
            [self.vision_proj, self.vision_proj_m],
            [self.text_encoder, self.text_encoder_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([]))

        # create the decoder
        decoder_config = BertConfig.from_json_file(med_config)
        decoder_config.encoder_width = vision_width
        self.text_decoder = BertLMHeadModel.from_pretrained(
            "bert-base-uncased", config=decoder_config
        )
        self.text_decoder.resize_token_embeddings(len(self.tokenizer))
        tie_encoder_decoder_weights(
            self.text_encoder, self.text_decoder.bert, "", "/attention"
        )

    def forward(self, image, caption, alpha):
        with torch.no_grad():
            self.temp.clamp_(0.001, 0.5)

        image_embeds = self.visual_encoder(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=30,
            return_tensors="pt",
        ).to(image.device)
        text_output = self.text_encoder(
            text.input_ids,
            attention_mask=text.attention_mask,
            return_dict=True,
            mode="text",
        )
        text_feat = F.normalize(
            self.text_proj(text_output.last_hidden_state[:, 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(
                text.input_ids,
                attention_mask=text.attention_mask,
                return_dict=True,
                mode="text",
            )
            text_feat_m = F.normalize(
                self.text_proj_m(text_output_m.last_hidden_state[:, 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_ita = (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,
        )
        vl_output = 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(vl_output, 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 loss_ita, loss_itm, loss_lm

    @torch.no_grad()
    def copy_params(self):
        for model_pair in self.model_pairs:
            for param, param_m in zip(
                model_pair[0].parameters(), model_pair[1].parameters()
            ):
                param_m.data.copy_(param.data)  # initialize
                param_m.requires_grad = False  # not update by gradient

    @torch.no_grad()
    def _momentum_update(self):
        for model_pair in self.model_pairs:
            for param, param_m in zip(
                model_pair[0].parameters(), model_pair[1].parameters()
            ):
                param_m.data = param_m.data * self.momentum + param.data * (
                    1.0 - self.momentum
                )

    @torch.no_grad()
    def _dequeue_and_enqueue(self, image_feat, text_feat):
        # gather keys before updating queue
        image_feats = concat_all_gather(image_feat)
        text_feats = concat_all_gather(text_feat)

        batch_size = image_feats.shape[0]

        ptr = int(self.queue_ptr)
        assert self.queue_size % batch_size == 0  # for simplicity

        # replace the keys at ptr (dequeue and enqueue)
        self.image_queue[:, ptr : ptr + batch_size] = image_feats.T
        self.text_queue[:, ptr : ptr + batch_size] = text_feats.T
        ptr = (ptr + batch_size) % self.queue_size  # move pointer

        self.queue_ptr[0] = ptr


def blip_pretrain(**kwargs):
    model = BLIP_Pretrain(**kwargs)
    return model


@torch.no_grad()
def concat_all_gather(tensor):
    """
    Performs all_gather operation on the provided tensors.
    *** Warning ***: torch.distributed.all_gather has no gradient.
    """
    tensors_gather = [
        torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())
    ]
    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)

    output = torch.cat(tensors_gather, dim=0)
    return output


from typing import List


def tie_encoder_decoder_weights(
    encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key: str
):
    uninitialized_encoder_weights: List[str] = []
    if decoder.__class__ != encoder.__class__:
        logger.info(
            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
        )

    def tie_encoder_to_decoder_recursively(
        decoder_pointer: nn.Module,
        encoder_pointer: nn.Module,
        module_name: str,
        uninitialized_encoder_weights: List[str],
        skip_key: str,
        depth=0,
    ):
        assert isinstance(decoder_pointer, nn.Module) and isinstance(
            encoder_pointer, nn.Module
        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
            assert hasattr(encoder_pointer, "weight")
            encoder_pointer.weight = decoder_pointer.weight
            if hasattr(decoder_pointer, "bias"):
                assert hasattr(encoder_pointer, "bias")
                encoder_pointer.bias = decoder_pointer.bias
            print(module_name + " is tied")
            return

        encoder_modules = encoder_pointer._modules
        decoder_modules = decoder_pointer._modules
        if len(decoder_modules) > 0:
            assert (
                len(encoder_modules) > 0
            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"

            all_encoder_weights = set(
                [module_name + "/" + sub_name for sub_name in encoder_modules.keys()]
            )
            encoder_layer_pos = 0
            for name, module in decoder_modules.items():
                if name.isdigit():
                    encoder_name = str(int(name) + encoder_layer_pos)
                    decoder_name = name
                    if not isinstance(
                        decoder_modules[decoder_name],
                        type(encoder_modules[encoder_name]),
                    ) and len(encoder_modules) != len(decoder_modules):
                        # this can happen if the name corresponds to the position in a list module list of layers
                        # in this case the decoder has added a cross-attention that the encoder does not have
                        # thus skip this step and subtract one layer pos from encoder
                        encoder_layer_pos -= 1
                        continue
                elif name not in encoder_modules:
                    continue
                elif depth > 500:
                    raise ValueError(
                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
                    )
                else:
                    decoder_name = encoder_name = name
                tie_encoder_to_decoder_recursively(
                    decoder_modules[decoder_name],
                    encoder_modules[encoder_name],
                    module_name + "/" + name,
                    uninitialized_encoder_weights,
                    skip_key,
                    depth=depth + 1,
                )
                all_encoder_weights.remove(module_name + "/" + encoder_name)

            uninitialized_encoder_weights += list(all_encoder_weights)

    # tie weights recursively
    tie_encoder_to_decoder_recursively(
        decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key
    )
