# ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------

import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch import Tensor, nn
from torchvision.ops.boxes import nms
from transformers import BertConfig, BertModel, BertPreTrainedModel
from transformers.modeling_outputs import (
    BaseModelOutputWithPoolingAndCrossAttentions,
)


class BertModelWarper(nn.Module):
    def __init__(self, bert_model):
        super().__init__()
        # self.bert = bert_modelc

        self.config = bert_model.config
        self.embeddings = bert_model.embeddings
        self.encoder = bert_model.encoder
        self.pooler = bert_model.pooler

        self.get_extended_attention_mask = (
            bert_model.get_extended_attention_mask
        )
        self.invert_attention_mask = bert_model.invert_attention_mask
        self.get_head_mask = bert_model.get_head_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        """
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict
            if return_dict is not None
            else self.config.use_return_dict
        )

        if self.config.is_decoder:
            use_cache = (
                use_cache if use_cache is not None else self.config.use_cache
            )
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
            batch_size, seq_length = input_shape
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size, seq_length = input_shape
        else:
            raise ValueError(
                "You have to specify either input_ids or inputs_embeds"
            )

        device = (
            input_ids.device if input_ids is not None else inputs_embeds.device
        )

        # past_key_values_length
        past_key_values_length = (
            past_key_values[0][0].shape[2]
            if past_key_values is not None
            else 0
        )

        if attention_mask is None:
            attention_mask = torch.ones(
                ((batch_size, seq_length + past_key_values_length)),
                device=device,
            )
        if token_type_ids is None:
            token_type_ids = torch.zeros(
                input_shape, dtype=torch.long, device=device
            )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = (
            self.get_extended_attention_mask(
                attention_mask, input_shape, device
            )
        )

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            (
                encoder_batch_size,
                encoder_sequence_length,
                _,
            ) = encoder_hidden_states.size()
            encoder_hidden_shape = (
                encoder_batch_size,
                encoder_sequence_length,
            )
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(
                    encoder_hidden_shape, device=device
                )
            encoder_extended_attention_mask = self.invert_attention_mask(
                encoder_attention_mask
            )
        else:
            encoder_extended_attention_mask = None
        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
        #     import ipdb; ipdb.set_trace()

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(
            head_mask, self.config.num_hidden_layers
        )

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = (
            self.pooler(sequence_output) if self.pooler is not None else None
        )

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


class TextEncoderShell(nn.Module):
    def __init__(self, text_encoder):
        super().__init__()
        self.text_encoder = text_encoder
        self.config = self.text_encoder.config

    def forward(self, **kw):
        # feed into text encoder
        return self.text_encoder(**kw)


def generate_masks_with_special_tokens(
    tokenized, special_tokens_list, tokenizer
):
    """Generate attention mask between each pair of special tokens
    Args:
        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
        special_tokens_mask (list): special tokens mask.
    Returns:
        torch.Tensor: attention mask between each special tokens.
    """
    input_ids = tokenized["input_ids"]
    bs, num_token = input_ids.shape
    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
    special_tokens_mask = torch.zeros(
        (bs, num_token), device=input_ids.device
    ).bool()
    for special_token in special_tokens_list:
        special_tokens_mask |= input_ids == special_token

    # idxs: each row is a list of indices of special tokens
    idxs = torch.nonzero(special_tokens_mask)

    # generate attention mask and positional ids
    attention_mask = (
        torch.eye(num_token, device=input_ids.device)
        .bool()
        .unsqueeze(0)
        .repeat(bs, 1, 1)
    )
    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
    previous_col = 0
    for i in range(idxs.shape[0]):
        row, col = idxs[i]
        if (col == 0) or (col == num_token - 1):
            attention_mask[row, col, col] = True
            position_ids[row, col] = 0
        else:
            attention_mask[
                row, previous_col + 1 : col + 1, previous_col + 1 : col + 1
            ] = True
            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                0, col - previous_col, device=input_ids.device
            )

        previous_col = col

    # # padding mask
    # padding_mask = tokenized['attention_mask']
    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()

    return attention_mask, position_ids.to(torch.long)


def generate_masks_with_special_tokens_and_transfer_map(
    tokenized, special_tokens_list, tokenizer
):
    """Generate attention mask between each pair of special tokens
    Args:
        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
        special_tokens_mask (list): special tokens mask.
    Returns:
        torch.Tensor: attention mask between each special tokens.
    """
    input_ids = tokenized["input_ids"]
    bs, num_token = input_ids.shape
    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
    special_tokens_mask = torch.zeros(
        (bs, num_token), device=input_ids.device
    ).bool()
    for special_token in special_tokens_list:
        special_tokens_mask |= input_ids == special_token

    # idxs: each row is a list of indices of special tokens
    idxs = torch.nonzero(special_tokens_mask)

    # generate attention mask and positional ids
    attention_mask = (
        torch.eye(num_token, device=input_ids.device)
        .bool()
        .unsqueeze(0)
        .repeat(bs, 1, 1)
    )
    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
    cate_to_token_mask_list = [[] for _ in range(bs)]
    previous_col = 0
    for i in range(idxs.shape[0]):
        row, col = idxs[i]
        if (col == 0) or (col == num_token - 1):
            attention_mask[row, col, col] = True
            position_ids[row, col] = 0
        else:
            attention_mask[
                row, previous_col + 1 : col + 1, previous_col + 1 : col + 1
            ] = True
            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                0, col - previous_col, device=input_ids.device
            )
            c2t_maski = torch.zeros(
                (num_token), device=input_ids.device
            ).bool()
            c2t_maski[previous_col + 1 : col] = True
            cate_to_token_mask_list[row].append(c2t_maski)
        previous_col = col

    cate_to_token_mask_list = [
        torch.stack(cate_to_token_mask_listi, dim=0)
        for cate_to_token_mask_listi in cate_to_token_mask_list
    ]

    # # padding mask
    # padding_mask = tokenized['attention_mask']
    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()

    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list
