# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BERT model."""

import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open

import torch
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
from torch.nn.utils.weight_norm import weight_norm
from transformers import AutoModel, AutoTokenizer, VisualBertModel
# from .utils import cached_path
import pdb
# from MoleculeSTM.models.vilbert import BertConfig
import matplotlib.pyplot as plt
logger = logging.getLogger(__name__)

PRETRAINED_MODEL_ARCHIVE_MAP = {
    "bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
    "bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
    "bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
    "bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
    "bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
    "bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
    "bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
}


def load_tf_weights_in_bert(model, tf_checkpoint_path):
    """ Load tf checkpoints in a pytorch model
    """
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        print(
            "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    print("Converting TensorFlow checkpoint from {}".format(tf_path))
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        print("Loading TF weight {} with shape {}".format(name, shape))
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        name = name.split("/")
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(n in ["adam_v", "adam_m"] for n in name):
            print("Skipping {}".format("/".join(name)))
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                l = re.split(r"_(\d+)", m_name)
            else:
                l = [m_name]
            if l[0] == "kernel" or l[0] == "gamma":
                pointer = getattr(pointer, "weight")
            elif l[0] == "output_bias" or l[0] == "beta":
                pointer = getattr(pointer, "bias")
            elif l[0] == "output_weights":
                pointer = getattr(pointer, "weight")
            else:
                pointer = getattr(pointer, l[0])
            if len(l) >= 2:
                num = int(l[1])
                pointer = pointer[num]
        if m_name[-11:] == "_embeddings":
            pointer = getattr(pointer, "weight")
        elif m_name == "kernel":
            array = np.transpose(array)
        try:
            assert pointer.shape == array.shape
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        print("Initialize PyTorch weight {}".format(name))
        pointer.data = torch.from_numpy(array)
    return model


def gelu(x):
    """Implementation of the gelu activation function.
        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
        Also see https://arxiv.org/abs/1606.08415
    """
    try:
        return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
    except:
        stop = 1


def swish(x):
    return x * torch.sigmoid(x)


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish}


# try:
#     from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
# except ImportError:
#     logger.info(
#         "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex ."
#     )

# class BertLayerNorm(nn.Module):
#     def __init__(self, hidden_size, eps=1e-12):
#          """Construct a layernorm module in the TF style (epsilon inside the square root).
#         """
#         super(BertLayerNorm, self).__init__()
#         self.weight = nn.Parameter(torch.ones(hidden_size))
#         self.bias = nn.Parameter(torch.zeros(hidden_size))
#         self.variance_epsilon = eps
#
#     def forward(self, x):
#         u = x.mean(-1, keepdim=True)
#         s = (x - u).pow(2).mean(-1, keepdim=True)
#         x = (x - u) / torch.sqrt(s + self.variance_epsilon)
#         return self.weight * x + self.bias

class BertConfig(object):
    """Configuration class to store the configuration of a `BertModel`.
    """

    def __init__(
            self,
            vocab_size_or_config_json_file,
            hidden_size=768,
            num_hidden_layers=12,
            num_attention_heads=12,
            intermediate_size=3072,
            hidden_act="gelu",
            hidden_dropout_prob=0.1,
            attention_probs_dropout_prob=0.1,
            max_position_embeddings=512,
            type_vocab_size=2,
            initializer_range=0.02,
            v_feature_size=2048,
            v_target_size=1601,
            v_hidden_size=768,
            v_num_hidden_layers=3,
            v_num_attention_heads=12,
            v_intermediate_size=3072,
            bi_hidden_size=1024,
            bi_num_attention_heads=16,
            v_attention_probs_dropout_prob=0.1,
            v_hidden_act="gelu",
            v_hidden_dropout_prob=0.1,
            v_initializer_range=0.2,
            v_biattention_id=[0, 1],
            t_biattention_id=[10, 11],
            predict_feature=False,
            fast_mode=False,
            fixed_v_layer=0,
            fixed_t_layer=0,
            in_batch_pairs=False,
            fusion_method="mul",
            intra_gate=False,
            with_coattention=True
    ):

        """Constructs BertConfig.

        Args:
            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
            hidden_dropout_prob: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            max_position_embeddings: The maximum sequence length that this model might
                ever be used with. Typically set this to something large just in case
                (e.g., 512 or 1024 or 2048).
            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                `BertModel`.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
        """
        assert len(v_biattention_id) == len(t_biattention_id)
        assert max(v_biattention_id) < v_num_hidden_layers
        assert max(t_biattention_id) < num_hidden_layers

        if isinstance(vocab_size_or_config_json_file, str) or (
                sys.version_info[0] == 2
                and isinstance(vocab_size_or_config_json_file, unicode)
        ):
            with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
                json_config = json.loads(reader.read())
            for key, value in json_config.items():
                self.__dict__[key] = value
        elif isinstance(vocab_size_or_config_json_file, int):
            self.vocab_size = vocab_size_or_config_json_file
            self.hidden_size = hidden_size
            self.num_hidden_layers = num_hidden_layers
            self.num_attention_heads = num_attention_heads
            self.hidden_act = hidden_act
            self.intermediate_size = intermediate_size
            self.hidden_dropout_prob = hidden_dropout_prob
            self.attention_probs_dropout_prob = attention_probs_dropout_prob
            self.max_position_embeddings = max_position_embeddings
            self.type_vocab_size = type_vocab_size
            self.initializer_range = initializer_range
            self.v_feature_size = v_feature_size
            self.v_hidden_size = v_hidden_size
            self.v_num_hidden_layers = v_num_hidden_layers
            self.v_num_attention_heads = v_num_attention_heads
            self.v_intermediate_size = v_intermediate_size
            self.v_attention_probs_dropout_prob = v_attention_probs_dropout_prob
            self.v_hidden_act = v_hidden_act
            self.v_hidden_dropout_prob = v_hidden_dropout_prob
            self.v_initializer_range = v_initializer_range
            self.v_biattention_id = v_biattention_id
            self.t_biattention_id = t_biattention_id
            self.v_target_size = v_target_size
            self.bi_hidden_size = bi_hidden_size
            self.bi_num_attention_heads = bi_num_attention_heads
            self.predict_feature = predict_feature
            self.fast_mode = fast_mode
            self.fixed_v_layer = fixed_v_layer
            self.fixed_t_layer = fixed_t_layer

            self.in_batch_pairs = in_batch_pairs
            self.fusion_method = fusion_method
            self.intra_gate = intra_gate
            self.with_coattention = with_coattention
        else:
            raise ValueError(
                "First argument must be either a vocabulary size (int)"
                "or the path to a pretrained model config file (str)"
            )

    @classmethod
    def from_dict(cls, json_object):
        """Constructs a `BertConfig` from a Python dictionary of parameters."""
        config = BertConfig(vocab_size_or_config_json_file=-1)
        for key, value in json_object.items():
            config.__dict__[key] = value
        return config

    @classmethod
    def from_json_file(cls, json_file):
        """Constructs a `BertConfig` from a json file of parameters."""
        with open(json_file, "r", encoding="utf-8") as reader:
            text = reader.read()
        return cls.from_dict(json.loads(text))

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class BertEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings.
    """

    def __init__(self, config):
        super(BertEmbeddings, self).__init__()
        self.word_embeddings = nn.Embedding(
            config.vocab_size, config.hidden_size, padding_idx=0
        )
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size
        )
        self.token_type_embeddings = nn.Embedding(
            config.type_vocab_size, config.hidden_size
        )

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        # self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, input_ids, token_type_ids=None):
        seq_length = input_ids.size(1)
        position_ids = torch.arange(
            seq_length, dtype=torch.long, device=input_ids.device
        )
        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_ids)

        words_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = words_embeddings + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertSelfAttention(nn.Module):
    def __init__(self, config):
        super(BertSelfAttention, self).__init__()
        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, hidden_states, attention_mask):
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
        attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)

        return context_layer, attention_probs


class BertSelfOutput(nn.Module):
    def __init__(self, config):
        super(BertSelfOutput, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        # self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertAttention(nn.Module):
    def __init__(self, config):
        super(BertAttention, self).__init__()
        self.self = BertSelfAttention(config)
        self.output = BertSelfOutput(config)

    def forward(self, input_tensor, attention_mask):
        self_output, attention_probs = self.self(input_tensor, attention_mask)
        attention_output = self.output(self_output, input_tensor)
        return attention_output, attention_probs


class BertIntermediate(nn.Module):
    def __init__(self, config):
        super(BertIntermediate, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str) or (
                sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
        ):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class BertOutput(nn.Module):
    def __init__(self, config):
        super(BertOutput, self).__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        # self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertLayer(nn.Module):
    def __init__(self, config):
        super(BertLayer, self).__init__()
        self.attention = BertAttention(config)
        self.intermediate = BertIntermediate(config)
        self.output = BertOutput(config)

    def forward(self, hidden_states, attention_mask):
        attention_output, attention_probs = self.attention(hidden_states, attention_mask)
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output, attention_probs


class BertImageSelfAttention(nn.Module):
    def __init__(self, config):
        super(BertImageSelfAttention, self).__init__()
        if config.v_hidden_size % config.v_num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.v_hidden_size, config.v_num_attention_heads)
            )
        self.num_attention_heads = config.v_num_attention_heads
        self.attention_head_size = int(
            config.v_hidden_size / config.v_num_attention_heads
        )
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.v_hidden_size, self.all_head_size)
        self.key = nn.Linear(config.v_hidden_size, self.all_head_size)
        self.value = nn.Linear(config.v_hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.v_attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, hidden_states, attention_mask):
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
        attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)

        return context_layer, attention_probs


class BertImageSelfOutput(nn.Module):
    def __init__(self, config):
        super(BertImageSelfOutput, self).__init__()
        self.dense = nn.Linear(config.v_hidden_size, config.v_hidden_size)
        # self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.v_hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(config.v_hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertImageAttention(nn.Module):
    def __init__(self, config):
        super(BertImageAttention, self).__init__()
        self.self = BertImageSelfAttention(config)
        self.output = BertImageSelfOutput(config)

    def forward(self, input_tensor, attention_mask):
        self_output, attention_probs = self.self(input_tensor, attention_mask)
        attention_output = self.output(self_output, input_tensor)
        return attention_output, attention_probs


class BertImageIntermediate(nn.Module):
    def __init__(self, config):
        super(BertImageIntermediate, self).__init__()
        self.dense = nn.Linear(config.v_hidden_size, config.v_intermediate_size)
        if isinstance(config.v_hidden_act, str) or (
                sys.version_info[0] == 2 and isinstance(config.v_hidden_act, unicode)
        ):
            self.intermediate_act_fn = ACT2FN[config.v_hidden_act]
        else:
            self.intermediate_act_fn = config.v_hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class BertImageOutput(nn.Module):
    def __init__(self, config):
        super(BertImageOutput, self).__init__()
        self.dense = nn.Linear(config.v_intermediate_size, config.v_hidden_size)
        # self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.v_hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(config.v_hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertImageLayer(nn.Module):
    def __init__(self, config):
        super(BertImageLayer, self).__init__()
        self.attention = BertImageAttention(config)
        self.intermediate = BertImageIntermediate(config)
        self.output = BertImageOutput(config)

    def forward(self, hidden_states, attention_mask):
        attention_output, attention_probs = self.attention(hidden_states, attention_mask)
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output, attention_probs


class BertBiAttention(nn.Module):
    def __init__(self, config):
        super(BertBiAttention, self).__init__()
        if config.bi_hidden_size % config.bi_num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.bi_hidden_size, config.bi_num_attention_heads)
            )

        self.num_attention_heads = config.bi_num_attention_heads
        self.attention_head_size = int(
            config.bi_hidden_size / config.bi_num_attention_heads
        )
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        # self.scale = nn.Linear(1, self.num_attention_heads, bias=False)
        # self.scale_act_fn = ACT2FN['relu']

        self.query1 = nn.Linear(config.v_hidden_size, self.all_head_size)
        self.key1 = nn.Linear(config.v_hidden_size, self.all_head_size)
        self.value1 = nn.Linear(config.v_hidden_size, self.all_head_size)
        # self.logit1 = nn.Linear(config.hidden_size, self.num_attention_heads)

        self.dropout1 = nn.Dropout(config.v_attention_probs_dropout_prob)

        self.query2 = nn.Linear(config.hidden_size, self.all_head_size)
        self.key2 = nn.Linear(config.hidden_size, self.all_head_size)
        self.value2 = nn.Linear(config.hidden_size, self.all_head_size)
        # self.logit2 = nn.Linear(config.hidden_size, self.num_attention_heads)

        self.dropout2 = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(self, input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask=None,
                use_co_attention_mask=False):
        # input_tensor1: image input_tensor2: text
        # for vision input.
        mixed_query_layer1 = self.query1(input_tensor1)
        mixed_key_layer1 = self.key1(input_tensor1)
        mixed_value_layer1 = self.value1(input_tensor1)
        # mixed_logit_layer1 = self.logit1(input_tensor1)

        query_layer1 = self.transpose_for_scores(mixed_query_layer1)
        key_layer1 = self.transpose_for_scores(mixed_key_layer1)
        value_layer1 = self.transpose_for_scores(mixed_value_layer1)
        # logit_layer1 = self.transpose_for_logits(mixed_logit_layer1)

        # for text input:
        mixed_query_layer2 = self.query2(input_tensor2)
        mixed_key_layer2 = self.key2(input_tensor2)
        mixed_value_layer2 = self.value2(input_tensor2)
        # mixed_logit_layer2 = self.logit2(input_tensor2)

        query_layer2 = self.transpose_for_scores(mixed_query_layer2)
        key_layer2 = self.transpose_for_scores(mixed_key_layer2)
        value_layer2 = self.transpose_for_scores(mixed_value_layer2)
        # logit_layer2 = self.transpose_for_logits(mixed_logit_layer2)

        # Take the dot product between "query2" and "key1" to get the raw attention scores for value 1.
        attention_scores1 = torch.matmul(query_layer2, key_layer1.transpose(-1, -2))
        attention_scores1 = attention_scores1 / math.sqrt(self.attention_head_size)
        attention_scores1 = attention_scores1 + attention_mask1

        if use_co_attention_mask:
            attention_scores1 = attention_scores1 + co_attention_mask.permute(0, 1, 3, 2) # text-motif

        # Normalize the attention scores to probabilities.
        attention_probs1 = nn.Softmax(dim=-1)(attention_scores1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs1 = self.dropout1(attention_probs1)

        context_layer1 = torch.matmul(attention_probs1, value_layer1)  # attention * v
        context_layer1 = context_layer1.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape1 = context_layer1.size()[:-2] + (self.all_head_size,)
        context_layer1 = context_layer1.view(*new_context_layer_shape1)

        # Take the dot product between "query1" and "key2" to get the raw attention scores for value 2.
        attention_scores2 = torch.matmul(query_layer1, key_layer2.transpose(-1, -2))
        try:
            attention_scores2 = attention_scores2 / math.sqrt(self.attention_head_size) # motif-text
        except:
            stop = 1
        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)

        # we can comment this line for single flow.
        attention_scores2 = attention_scores2 + attention_mask2
        if use_co_attention_mask:
            attention_scores2 = attention_scores2 + co_attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs2 = nn.Softmax(dim=-1)(attention_scores2)

        # case study visualize
        # b = attention_scores2.squeeze(0)
        # b = b.mean(dim=0)
        # row_entropy = -(b * b.log()).sum(dim=1)
        # #b = b[:7,:20]
        # plt.imshow(b.cpu().detach().numpy(), cmap='viridis',vmin=0, vmax=1)  # 将张量转换为 NumPy 数组，'viridis' 是一个颜色映射
        # plt.colorbar()  # 添加颜色条以显示数值与颜色的对应关系
        # plt.show()

        # case study end

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs2 = self.dropout2(attention_probs2)

        context_layer2 = torch.matmul(attention_probs2, value_layer2)
        try:
            context_layer2 = context_layer2.permute(0, 2, 1, 3).contiguous()
        except:
            stop = 1
        new_context_layer_shape2 = context_layer2.size()[:-2] + (self.all_head_size,)
        context_layer2 = context_layer2.view(*new_context_layer_shape2)

        return context_layer1, context_layer2, (attention_probs1, attention_probs2),


class BertBiOutput(nn.Module):
    def __init__(self, config):
        super(BertBiOutput, self).__init__()

        self.dense1 = nn.Linear(config.bi_hidden_size, config.v_hidden_size)
        # self.LayerNorm1 = BertLayerNorm(config.v_hidden_size, eps=1e-12)
        self.LayerNorm1 = torch.nn.LayerNorm(config.v_hidden_size, eps=1e-12)
        self.dropout1 = nn.Dropout(config.v_hidden_dropout_prob)

        self.q_dense1 = nn.Linear(config.bi_hidden_size, config.v_hidden_size)
        self.q_dropout1 = nn.Dropout(config.v_hidden_dropout_prob)

        self.dense2 = nn.Linear(config.bi_hidden_size, config.hidden_size)
        # self.LayerNorm2 = BertLayerNorm(config.hidden_size, eps=1e-12)
        self.LayerNorm2 = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
        self.dropout2 = nn.Dropout(config.hidden_dropout_prob)

        self.q_dense2 = nn.Linear(config.bi_hidden_size, config.hidden_size)
        self.q_dropout2 = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states1, input_tensor1, hidden_states2, input_tensor2):
        hidden_state1 = self.dense1(hidden_states1)
        hidden_state1 = self.dropout1(hidden_state1)

        hidden_state2 = self.dense2(hidden_states2)
        hidden_state2 = self.dropout2(hidden_state2)

        a = hidden_state1 + input_tensor1
        hidden_states1 = self.LayerNorm1(a)
        a = hidden_state2 + input_tensor2
        hidden_states2 = self.LayerNorm2(a)

        return hidden_states1, hidden_states2


class BertConnectionLayer(nn.Module):
    def __init__(self, config):
        super(BertConnectionLayer, self).__init__()
        self.biattention = BertBiAttention(config)

        self.biOutput = BertBiOutput(config)

        self.v_intermediate = BertImageIntermediate(config)
        self.v_output = BertImageOutput(config)

        self.t_intermediate = BertIntermediate(config)
        self.t_output = BertOutput(config)

        self.m_pooler = BertImagePooler(config)
        self.t_pooler = BertTextPooler(config)

    def forward(self, input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask=None,
                use_co_attention_mask=False):
        bi_output1, bi_output2, co_attention_probs, = self.biattention(
            input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask, use_co_attention_mask
        )
        # add & norm
        attention_output1, attention_output2 = self.biOutput(bi_output2, input_tensor1, bi_output1, input_tensor2)

        intermediate_output1 = self.v_intermediate(attention_output1)
        layer_output1 = self.v_output(intermediate_output1, attention_output1)

        intermediate_output2 = self.t_intermediate(attention_output2)
        layer_output2 = self.t_output(intermediate_output2, attention_output2)

        pooled_output_m = self.m_pooler(layer_output1)
        pooled_output_t = self.t_pooler(layer_output2)

        return layer_output1, layer_output2, pooled_output_m, pooled_output_t, co_attention_probs,


class BertEncoder(nn.Module):
    def __init__(self, config):
        super(BertEncoder, self).__init__()

        # in the bert encoder, we need to extract three things here.
        # text bert layer: BertLayer
        # vision bert layer: BertImageLayer
        # Bi-Attention: Given the output of two bertlayer, perform bi-directional
        # attention and add on two layers.

        self.FAST_MODE = config.fast_mode
        self.with_coattention = config.with_coattention
        self.v_biattention_id = config.v_biattention_id
        self.t_biattention_id = config.t_biattention_id
        self.in_batch_pairs = config.in_batch_pairs
        self.fixed_t_layer = config.fixed_t_layer
        self.fixed_v_layer = config.fixed_v_layer
        layer = BertLayer(config)
        v_layer = BertImageLayer(config)
        connect_layer = BertConnectionLayer(config)

        self.layer = nn.ModuleList(
            [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]
        )
        self.v_layer = nn.ModuleList(
            [copy.deepcopy(v_layer) for _ in range(config.v_num_hidden_layers)]
        )
        self.c_layer = nn.ModuleList(
            [copy.deepcopy(connect_layer) for _ in range(len(config.v_biattention_id))]
        )

    def forward(
            self,
            txt_embedding,
            image_embedding,
            txt_attention_mask,
            image_attention_mask,
            co_attention_mask=None,
            output_all_encoded_layers=True,
            output_all_attention_masks=False,
    ):

        v_start = 0
        t_start = 0
        count = 0
        all_encoder_layers_t = []
        all_encoder_layers_v = []

        all_attention_mask_t = []
        all_attnetion_mask_v = []
        all_attention_mask_c = []

        batch_size, num_words, t_hidden_size = txt_embedding.size()
        _, num_regions, v_hidden_size = image_embedding.size()

        use_co_attention_mask = False
        for v_layer_id, t_layer_id in zip(self.v_biattention_id, self.t_biattention_id):

            v_end = v_layer_id
            t_end = t_layer_id

            assert self.fixed_t_layer <= t_end
            assert self.fixed_v_layer <= v_end

            for idx in range(v_start, self.fixed_v_layer):
                with torch.no_grad():
                    image_embedding, image_attention_probs = self.v_layer[idx](image_embedding, image_attention_mask)
                    v_start = self.fixed_v_layer

                    if output_all_attention_masks:
                        all_attnetion_mask_v.append(image_attention_probs)

            for idx in range(v_start, v_end):
                image_embedding, image_attention_probs = self.v_layer[idx](image_embedding, image_attention_mask)

                if output_all_attention_masks:
                    all_attnetion_mask_v.append(image_attention_probs)

            for idx in range(t_start, self.fixed_t_layer):
                with torch.no_grad():
                    txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
                    t_start = self.fixed_t_layer
                    if output_all_attention_masks:
                        all_attention_mask_t.append(txt_attention_probs)

            for idx in range(t_start, t_end):
                txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
                if output_all_attention_masks:
                    all_attention_mask_t.append(txt_attention_probs)

            if count == 0 and self.in_batch_pairs:
                # new batch size is the batch_size ^2
                image_embedding = image_embedding.unsqueeze(0).expand(batch_size, batch_size, num_regions,
                                                                      v_hidden_size).contiguous().view(
                    batch_size * batch_size, num_regions, v_hidden_size)
                image_attention_mask = image_attention_mask.unsqueeze(0).expand(batch_size, batch_size, 1, 1,
                                                                                num_regions).contiguous().view(
                    batch_size * batch_size, 1, 1, num_regions)

                txt_embedding = txt_embedding.unsqueeze(1).expand(batch_size, batch_size, num_words,
                                                                  t_hidden_size).contiguous().view(
                    batch_size * batch_size, num_words, t_hidden_size)
                txt_attention_mask = txt_attention_mask.unsqueeze(1).expand(batch_size, batch_size, 1, 1,
                                                                            num_words).contiguous().view(
                    batch_size * batch_size, 1, 1, num_words)
                co_attention_mask = co_attention_mask.unsqueeze(1).expand(batch_size, batch_size, 1, num_regions,
                                                                          num_words).contiguous().view(
                    batch_size * batch_size, 1, num_regions, num_words)

            if count == 0 and self.FAST_MODE:
                txt_embedding = txt_embedding.expand(image_embedding.size(0), txt_embedding.size(1),
                                                     txt_embedding.size(2))
                txt_attention_mask = txt_attention_mask.expand(image_embedding.size(0), txt_attention_mask.size(1),
                                                               txt_attention_mask.size(2), txt_attention_mask.size(3))

            if self.with_coattention:
                # do the bi attention.
                image_embedding, txt_embedding, pooled_output_m, pooled_output_t, co_attention_probs, = self.c_layer[
                    count](
                    image_embedding, image_attention_mask, txt_embedding, txt_attention_mask, co_attention_mask,
                    use_co_attention_mask)

                # use_co_attention_mask = False
                if output_all_attention_masks:
                    all_attention_mask_c.append(co_attention_probs)

            v_start = v_end
            t_start = t_end
            count += 1

            if output_all_encoded_layers:
                all_encoder_layers_t.append(txt_embedding)
                all_encoder_layers_v.append(image_embedding)

        for idx in range(v_start, len(self.v_layer)):
            image_embedding, image_attention_probs = self.v_layer[idx](image_embedding, image_attention_mask)

            if output_all_attention_masks:
                all_attnetion_mask_v.append(image_attention_probs)

        for idx in range(t_start, len(self.layer)):
            txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)

            if output_all_attention_masks:
                all_attention_mask_t.append(txt_attention_probs)

        # add the end part to finish.
        if not output_all_encoded_layers:
            all_encoder_layers_t.append(txt_embedding)
            all_encoder_layers_v.append(image_embedding)

        return all_encoder_layers_t, all_encoder_layers_v, (
        all_attention_mask_t, all_attnetion_mask_v, all_attention_mask_c),


class BertTextPooler(nn.Module):
    def __init__(self, config):
        super(BertTextPooler, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
        self.activation = nn.ReLU()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertImagePooler(nn.Module):
    def __init__(self, config):
        super(BertImagePooler, self).__init__()
        self.dense = nn.Linear(config.v_hidden_size, config.bi_hidden_size)
        self.activation = nn.ReLU()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super(BertPredictionHeadTransform, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str) or (
                sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
        ):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        # self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertImgPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super(BertImgPredictionHeadTransform, self).__init__()
        self.dense = nn.Linear(config.v_hidden_size, config.v_hidden_size)
        if isinstance(config.hidden_act, str) or (
                sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
        ):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.v_hidden_act
        # self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
        self.LayerNorm = torch.nn.LayerNorm(config.v_hidden_size, eps=1e-12)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertLMPredictionHead(nn.Module):
    def __init__(self, config, bert_model_embedding_weights):
        super(BertLMPredictionHead, self).__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(
            bert_model_embedding_weights.size(1),
            bert_model_embedding_weights.size(0),
            bias=False,
        )
        self.decoder.weight = bert_model_embedding_weights
        self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states) + self.bias
        return hidden_states


class BertOnlyMLMHead(nn.Module):
    def __init__(self, config, bert_model_embedding_weights):
        super(BertOnlyMLMHead, self).__init__()
        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


class BertOnlyNSPHead(nn.Module):
    def __init__(self, config):
        super(BertOnlyNSPHead, self).__init__()
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, pooled_output):
        seq_relationship_score = self.seq_relationship(pooled_output)
        return seq_relationship_score


class BertPreTrainingHeads(nn.Module):
    def __init__(self, config, bert_model_embedding_weights):
        super(BertPreTrainingHeads, self).__init__()
        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
        self.bi_seq_relationship = nn.Linear(config.bi_hidden_size, 2)
        self.imagePredictions = BertImagePredictionHead(config)
        self.fusion_method = config.fusion_method
        self.dropout = nn.Dropout(0.1)

    def forward(
            self, sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
    ):
        # if self.fusion_method == 'sum':
        #     pooled_output = self.dropout(pooled_output_t + pooled_output_v)
        # elif self.fusion_method == 'mul':
        #     pooled_output = self.dropout(pooled_output_t * pooled_output_v)
        # else:
        #     assert False

        prediction_scores_t = self.predictions(sequence_output_t)
        # seq_relationship_score = self.bi_seq_relationship(pooled_output)
        prediction_scores_v = self.imagePredictions(sequence_output_v)

        return prediction_scores_v, prediction_scores_t  # seq_relationship_score


class BertImagePredictionHead(nn.Module):
    def __init__(self, config):
        super(BertImagePredictionHead, self).__init__()
        self.transform = BertImgPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.v_hidden_size, config.v_target_size)

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class BertPreTrainedModel(nn.Module):
    """ An abstract class to handle weights initialization and
        a simple interface for dowloading and loading pretrained models.
    """

    def __init__(self, config, default_gpu=True, *inputs, **kwargs):
        super(BertPreTrainedModel, self).__init__()

        if not isinstance(config, BertConfig):
            raise ValueError(
                "Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
                "To create a model from a Google pretrained model use "
                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
                    self.__class__.__name__, self.__class__.__name__
                )
            )

        self.config = config

    def init_bert_weights(self, module):
        """ Initialize the weights.
        """
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        # elif isinstance(module, BertLayerNorm):
        #     stop = 1
        #     module.bias.data.zero_()
        #     module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()

    @classmethod
    def from_pretrained(
            cls,
            pretrained_model_name_or_path,
            config,
            default_gpu=True,
            state_dict=None,
            cache_dir=None,
            from_tf=False,
            *inputs,
            **kwargs
    ):
        """
        Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
        Download and cache the pre-trained model file if needed.

        Params:
            pretrained_model_name_or_path: either:
                - a str with the name of a pre-trained model to load selected in the list of:
                    . `bert-base-uncased`
                    . `bert-large-uncased`
                    . `bert-base-cased`
                    . `bert-large-cased`
                    . `bert-base-multilingual-uncased`
                    . `bert-base-multilingual-cased`
                    . `bert-base-chinese`
                - a path or url to a pretrained model archive containing:
                    . `bert_config.json` a configuration file for the model
                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
                - a path or url to a pretrained model archive containing:
                    . `bert_config.json` a configuration file for the model
                    . `model.chkpt` a TensorFlow checkpoint
            from_tf: should we load the weights from a locally saved TensorFlow checkpoint
            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
            *inputs, **kwargs: additional input for the specific Bert class
                (ex: num_labels for BertForSequenceClassification)
        """
        CONFIG_NAME = "bert_config.json"
        WEIGHTS_NAME = "pytorch_model.bin"
        TF_WEIGHTS_NAME = "model.ckpt"

        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
        else:
            archive_file = pretrained_model_name_or_path
        # redirect to the cache, if necessary
        try:
            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
        except EnvironmentError:
            logger.error(
                "Model name '{}' was not found in model name list ({}). "
                "We assumed '{}' was a path or url but couldn't find any file "
                "associated to this path or url.".format(
                    pretrained_model_name_or_path,
                    ", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
                    archive_file,
                )
            )
            return None

        if default_gpu:
            if resolved_archive_file == archive_file:
                logger.info("loading archive file {}".format(archive_file))
            else:
                logger.info(
                    "loading archive file {} from cache at {}".format(
                        archive_file, resolved_archive_file
                    )
                )
        tempdir = None
        if os.path.isdir(resolved_archive_file) or from_tf:
            serialization_dir = resolved_archive_file
        elif resolved_archive_file[-3:] == 'bin':
            serialization_dir = '/'.join(resolved_archive_file.split('/')[:-1])
            WEIGHTS_NAME = resolved_archive_file.split('/')[-1]
        else:
            # Extract archive to temp dir
            tempdir = tempfile.mkdtemp()
            logger.info(
                "extracting archive file {} to temp dir {}".format(
                    resolved_archive_file, tempdir
                )
            )
            with tarfile.open(resolved_archive_file, "r:gz") as archive:
                archive.extractall(tempdir)
            serialization_dir = tempdir
        # Load config
        # config_file = os.path.join(serialization_dir, CONFIG_NAME)
        # config = BertConfig.from_json_file(config_file)
        if default_gpu:
            logger.info("Model config {}".format(config))
        # Instantiate model.
        model = cls(config, *inputs, **kwargs)
        if state_dict is None and not from_tf:
            weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
            state_dict = torch.load(
                weights_path,
                map_location="cpu",
            )
            if 'state_dict' in dir(state_dict):
                state_dict = state_dict.state_dict()

        if tempdir:
            # Clean up temp dir
            shutil.rmtree(tempdir)
        if from_tf:
            # Directly load from a TensorFlow checkpoint
            weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
            return load_tf_weights_in_bert(model, weights_path)
        # Load from a PyTorch state_dict
        old_keys = []
        new_keys = []
        for key in state_dict.keys():
            new_key = None
            if "gamma" in key:
                new_key = key.replace("gamma", "weight")
            if "beta" in key:
                new_key = key.replace("beta", "bias")
            if new_key:
                old_keys.append(key)
                new_keys.append(new_key)
        for old_key, new_key in zip(old_keys, new_keys):
            state_dict[new_key] = state_dict.pop(old_key)

        missing_keys = []
        unexpected_keys = []
        error_msgs = []
        # copy state_dict so _load_from_state_dict can modify it
        metadata = getattr(state_dict, "_metadata", None)
        state_dict = state_dict.copy()
        if metadata is not None:
            state_dict._metadata = metadata

        def load(module, prefix=""):
            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
            module._load_from_state_dict(
                state_dict,
                prefix,
                local_metadata,
                True,
                missing_keys,
                unexpected_keys,
                error_msgs,
            )
            for name, child in module._modules.items():
                if child is not None:
                    load(child, prefix + name + ".")

        start_prefix = ""
        if not hasattr(model, "bert") and any(
                s.startswith("bert.") for s in state_dict.keys()
        ):
            start_prefix = "bert."
        load(model, prefix=start_prefix)
        if len(missing_keys) > 0 and default_gpu:
            logger.info(
                "Weights of {} not initialized from pretrained model: {}".format(
                    model.__class__.__name__, missing_keys
                )
            )
        if len(unexpected_keys) > 0 and default_gpu:
            logger.info(
                "Weights from pretrained model not used in {}: {}".format(
                    model.__class__.__name__, unexpected_keys
                )
            )
        if len(error_msgs) > 0 and default_gpu:
            raise RuntimeError(
                "Error(s) in loading state_dict for {}:\n\t{}".format(
                    model.__class__.__name__, "\n\t".join(error_msgs)
                )
            )
        return model


class BertModel(BertPreTrainedModel):
    """BERT model ("Bidirectional Embedding Representations from a Transformer").

    Params:
        config: a BertConfig class instance with the configuration to build a new model

    Inputs:
        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
            a `sentence B` token (see BERT paper for more details).
        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
            input sequence length in the current batch. It's the mask that we typically use for attention when
            a batch has varying length sentences.
        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.

    Outputs: Tuple of (encoded_layers, pooled_output)
        `encoded_layers`: controled by `output_all_encoded_layers` argument:
            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
                to the last attention block of shape [batch_size, sequence_length, hidden_size],
        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
            classifier pretrained on top of the hidden state associated to the first character of the
            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).

    Example usage:
    ```python
    # Already been converted into WordPiece token ids
    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])

    config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)

    model = modeling.BertModel(config=config)
    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
    ```
    """

    def __init__(self, config):
        super(BertModel, self).__init__(config)

        # initilize word embedding
        # self.embeddings = BertEmbeddings(config)

        # initlize the vision embedding
        self.v_embeddings = BertImageEmbeddings(config)
        self.bertlm = AutoModel.from_pretrained('../data/pretrained_SciBERT')

        self.encoder = BertEncoder(config)
        self.t_pooler = BertTextPooler(config)
        self.v_pooler = BertImagePooler(config)

        self.apply(self.init_bert_weights)

    def forward(
            self,
            input_txt,
            token_type_ids=None,
            # input_imgs,
            # image_loc,
            # embedding_output,
            v_embedding_output=None,
            attention_mask=None,
            image_attention_mask=None,
            m_position_embedding=None,
            co_attention_mask=None,
            output_all_encoded_layers=False,
            output_all_attention_masks=False,
    ):
        if m_position_embedding == None:
            m_position_embedding = torch.zeros_like(image_attention_mask).unsqueeze(dim=2).to(input_txt.device)
        # if attention_mask is None:
        #     attention_mask = torch.ones_like(input_txt)
        if token_type_ids is None:
            token_type_ids = torch.zeros_like(input_txt)
        # if image_attention_mask is None:
        #     image_attention_mask = torch.ones(
        #         input_imgs.size(0), input_imgs.size(1)
        #     ).type_as(input_txt)

        # We create a 3D attention mask from a 2D tensor mask.
        # Sizes are [batch_size, 1, 1, to_seq_length]
        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
        # this attention mask is more simple than the triangular masking of causal attention
        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
        extended_image_attention_mask = image_attention_mask.unsqueeze(1).unsqueeze(2)

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(
            dtype=next(self.parameters()).dtype
        )  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

        extended_image_attention_mask = extended_image_attention_mask.to(
            dtype=next(self.parameters()).dtype
        )  # fp16 compatibility
        extended_image_attention_mask = (1.0 - extended_image_attention_mask) * -10000.0

        if co_attention_mask is None:
            co_attention_mask = torch.zeros(attention_mask.size(0), image_attention_mask.size(1),
                                            attention_mask.size(1)).type_as(extended_image_attention_mask)

        extended_co_attention_mask = co_attention_mask.unsqueeze(1)

        # extended_co_attention_mask = co_attention_mask.unsqueeze(-1)
        extended_co_attention_mask = extended_co_attention_mask * 5.0
        extended_co_attention_mask = extended_co_attention_mask.to(
            dtype=next(self.parameters()).dtype
        )  # fp16 compatibility
        # 得到text embedding
        # embedding_output = self.embeddings(input_txt, token_type_ids)

        embedding_output = self.bertlm(input_ids=input_txt, attention_mask=attention_mask)['last_hidden_state']
        # 得到molecule Embedding
        v_embedding_output = self.v_embeddings(v_embedding_output, m_position_embedding)

        encoded_layers_t, encoded_layers_v, all_attention_mask = self.encoder(
            embedding_output,
            v_embedding_output,
            extended_attention_mask,
            extended_image_attention_mask,
            extended_co_attention_mask,
            output_all_encoded_layers=output_all_encoded_layers,
            output_all_attention_masks=output_all_attention_masks,
        )

        sequence_output_t = encoded_layers_t[-1]
        sequence_output_v = encoded_layers_v[-1]

        # pooled_output_t = self.t_pooler(sequence_output_t)
        # pooled_output_v = self.v_pooler(sequence_output_v)
        pooled_output_t = sequence_output_t[:, 0]
        pooled_output_v = sequence_output_v[:, 0]

        if not output_all_encoded_layers:
            encoded_layers_t = encoded_layers_t[-1]
            encoded_layers_v = encoded_layers_v[-1]

        return encoded_layers_t, encoded_layers_v, pooled_output_t, pooled_output_v, all_attention_mask


class BertImageEmbeddings(nn.Module):
    """Construct the embeddings from image, spatial location (omit now) and token_type embeddings.
    """

    def __init__(self, config):
        super(BertImageEmbeddings, self).__init__()

        self.image_embeddings = nn.Linear(config.v_hidden_size, config.v_hidden_size)
        self.image_location_embeddings = nn.Linear(1, config.v_hidden_size)
        self.LayerNorm = torch.nn.LayerNorm(config.v_hidden_size, eps=1e-12)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, input_ids, input_loc):
        # img_embeddings = self.image_embeddings(input_ids)
        img_embeddings = input_ids
        loc_embeddings = self.image_location_embeddings(input_loc)
        embeddings = self.LayerNorm(img_embeddings + loc_embeddings)
        embeddings = self.dropout(embeddings)

        return embeddings


class BertForMultiModalPreTraining(BertPreTrainedModel):
    """BERT model with multi modal pre-training heads.
    """

    def __init__(self, config):
        super(BertForMultiModalPreTraining, self).__init__(config)

        self.bert = BertModel(config)
        self.cls = BertPreTrainingHeads(
            config, self.bert.bertlm.embeddings.word_embeddings.weight
        )

        self.apply(self.init_bert_weights)
        self.predict_feature = config.predict_feature
        self.loss_fct = CrossEntropyLoss(ignore_index=-1)

        print("model's option for predict_feature is ", config.predict_feature)

        if self.predict_feature:
            self.vis_criterion = nn.MSELoss(reduction="none")
        else:
            self.vis_criterion = nn.KLDivLoss(reduction="none")

    def forward(
            self,
            input_ids,
            # image_feat,
            # image_loc,
            # token_type_ids=None,
            # t_embedding,
            m_embedding,
            attention_mask=None,
            image_attention_mask=None,
            m_position_embedding=None,
            masked_lm_labels=None,
            image_label=None,
            image_target=None,
            next_sentence_label=None,
            output_all_attention_masks=False
    ):

        # in this model, we first embed the images.
        sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, all_attention_mask = self.bert(
            input_ids,
            None,
            m_embedding,
            attention_mask,
            image_attention_mask,
            m_position_embedding=m_position_embedding,
            output_all_encoded_layers=False,
            output_all_attention_masks=output_all_attention_masks
        )

        prediction_scores_m, prediction_scores_t = self.cls(
            sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
        )

        if masked_lm_labels is not None and next_sentence_label is not None and image_target is not None:

            prediction_scores_m = prediction_scores_m[:, 1:]
            if self.predict_feature:
                img_loss = self.vis_criterion(prediction_scores_m, image_target)
                masked_img_loss = torch.sum(
                    img_loss * (image_label == 1).unsqueeze(2).float()
                ) / max(torch.sum((image_label == 1).unsqueeze(2).expand_as(img_loss)), 1)

            else:
                img_loss = self.vis_criterion(
                    F.log_softmax(prediction_scores_m, dim=2), image_target
                )
                masked_img_loss = torch.sum(
                    img_loss * (image_label == 1).unsqueeze(2).float()
                ) / max(torch.sum((image_label == 1)), 0)

            # masked_img_loss = torch.sum(img_loss) / (img_loss.shape[0] * img_loss.shape[1])
            masked_lm_loss = self.loss_fct(
                prediction_scores_t.view(-1, self.config.vocab_size),
                masked_lm_labels.view(-1),
            )
            # next_sentence_loss = self.loss_fct(
            #     seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
            # )
            # total_loss = masked_lm_loss + next_sentence_loss + masked_img_loss
            return masked_lm_loss.unsqueeze(0), masked_img_loss.unsqueeze(0),  # next_sentence_loss.unsqueeze(0)
        else:
            return prediction_scores_t, prediction_scores_m, all_attention_mask, pooled_output_t, pooled_output_v, sequence_output_t, sequence_output_v

class VILBertForVLTasks(BertPreTrainedModel):
    def __init__(self, config, num_labels, dropout_prob=0.1, default_gpu=True):
        super(VILBertForVLTasks, self).__init__(config)
        self.num_labels = num_labels
        self.bert = BertModel(config)
        self.dropout = nn.Dropout(dropout_prob)
        self.cls = BertPreTrainingHeads(
            config, self.bert.embeddings.word_embeddings.weight
        )
        self.vil_prediction = SimpleClassifier(config.bi_hidden_size, config.bi_hidden_size * 2, num_labels, 0.5)
        # self.vil_prediction = nn.Linear(config.bi_hidden_size, num_labels)
        self.vil_logit = nn.Linear(config.bi_hidden_size, 1)
        self.vision_logit = nn.Linear(config.v_hidden_size, 1)
        self.linguisic_logit = nn.Linear(config.hidden_size, 1)
        self.fusion_method = config.fusion_method
        self.apply(self.init_bert_weights)

    def forward(
            self,
            input_txt,
            input_imgs,
            image_loc,
            token_type_ids=None,
            attention_mask=None,
            image_attention_mask=None,
            co_attention_mask=None,
            output_all_encoded_layers=False,
    ):
        sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, _ = self.bert(
            input_txt,
            input_imgs,
            image_loc,
            token_type_ids,
            attention_mask,
            image_attention_mask,
            co_attention_mask,
            output_all_encoded_layers=False,
        )

        vil_prediction = 0
        vil_logit = 0
        vil_binary_prediction = 0
        vision_prediction = 0
        vision_logit = 0
        linguisic_prediction = 0
        linguisic_logit = 0

        linguisic_prediction, vision_prediction, vil_binary_prediction = self.cls(
            sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
        )

        if self.fusion_method == 'sum':
            pooled_output = self.dropout(pooled_output_t + pooled_output_v)
        elif self.fusion_method == 'mul':
            pooled_output = self.dropout(pooled_output_t * pooled_output_v)
        else:
            assert False

        vil_prediction = self.vil_prediction(pooled_output)
        vil_logit = self.vil_logit(pooled_output)
        vision_logit = self.vision_logit(self.dropout(sequence_output_v)) + (
                    (1.0 - image_attention_mask) * -10000.0).unsqueeze(2).to(dtype=next(self.parameters()).dtype)
        linguisic_logit = self.linguisic_logit(self.dropout(sequence_output_t))

        return vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit


class SimpleClassifier(nn.Module):
    def __init__(self, in_dim, hid_dim, out_dim, dropout):
        super(SimpleClassifier, self).__init__()
        layers = [
            weight_norm(nn.Linear(in_dim, hid_dim), dim=None),
            nn.ReLU(),
            nn.Dropout(dropout, inplace=True),
            weight_norm(nn.Linear(hid_dim, out_dim), dim=None)
        ]
        self.main = nn.Sequential(*layers)

    def forward(self, x):
        logits = self.main(x)
        return logits