modeling_instellavl.py

#    Modification Copyright© 2025 Advanced Micro Devices, Inc. All rights reserved.
#    Copyright 2023 Haotian Liu
#
#    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.


import torch
import torch.nn as nn

from typing import List, Optional, Tuple, Union

from transformers import (AutoConfig, AutoModelForCausalLM,
                          OlmoConfig, OlmoModel, OlmoForCausalLM)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.generation.utils import GenerateOutput
from abc import ABC, abstractmethod

import re
import os
import math
import random
import shutil
from .mm_utils import get_anyres_image_grid_shape, rank0_print

from .mm_utils import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN

import torch
from einops import rearrange, repeat

try:
    from einops_exts import rearrange_many
except:
    pass



from torch import einsum

from torch import Tensor, device
import torch.utils.checkpoint
from torch.nn import CrossEntropyLoss

from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
    MaskedLMOutput,
)
from transformers.modeling_utils import (
    PreTrainedModel,
    apply_chunking_to_forward,
    find_pruneable_heads_and_indices,
    prune_linear_layer,
)
from transformers.utils import logging
from transformers.models.bert.configuration_bert import BertConfig

logger = logging.get_logger(__name__)

########## Projector ##############
class PoolerProjector(nn.Module):
    def __init__(self, config, vision_cfg):
        super().__init__()
        self._config = config
        self.hw = vision_cfg.image_size // vision_cfg.patch_size

        self.conv_pool = nn.Conv2d(config.mm_hidden_size, config.hidden_size, kernel_size=2, stride=2)

        self.proj = nn.Sequential(
            nn.GELU(),
            nn.Linear(config.hidden_size, config.hidden_size),
        )

    def forward(self, x, *args, **kwargs):
        height = width = self.hw
        assert height * width == x.shape[1]
        x = x.view(x.shape[0], height, width, -1).permute(0, 3, 1, 2)
        x = self.conv_pool(x)
        x = x.flatten(2).transpose(1, 2)
        x = self.proj(x)
        return x

    @property
    def config(self):
        return {"mm_projector_type": "pooler"}

class IdentityMap(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, *args, **kwargs):
        return x

    @property
    def config(self):
        return {"mm_projector_type": "identity"}


class SimpleResBlock(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.pre_norm = nn.LayerNorm(channels)

        self.proj = nn.Sequential(nn.Linear(channels, channels), nn.GELU(), nn.Linear(channels, channels))

    def forward(self, x):
        x = self.pre_norm(x)
        return x + self.proj(x)


def build_vision_projector(config, delay_load=False, **kwargs):
    projector_type = getattr(config, "mm_projector_type", "linear")

    if projector_type == "linear":
        return nn.Linear(config.mm_hidden_size, config.hidden_size)

    if projector_type == "pooler":
        return PoolerProjector(config, kwargs["vision_cfg"])

    mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
    if mlp_gelu_match:
        mlp_depth = int(mlp_gelu_match.group(1))
        modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
        for _ in range(1, mlp_depth):
            modules.append(nn.GELU())
            modules.append(nn.Linear(config.hidden_size, config.hidden_size))
        return nn.Sequential(*modules)

    mlp_gelu_resnet_match = re.match(r"^mlp(\d+)x_res(\d+)x_gelu$", projector_type)
    if mlp_gelu_resnet_match:
        mlp_depth = int(mlp_gelu_resnet_match.group(1))
        res_depth = int(mlp_gelu_resnet_match.group(2))
        modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
        for _ in range(1, mlp_depth):
            modules.append(nn.GELU())
            modules.append(nn.Linear(config.hidden_size, config.hidden_size))
        for _ in range(res_depth):
            modules.append(SimpleResBlock(config.hidden_size))
        return nn.Sequential(*modules)

    if projector_type == "identity":
        return IdentityMap()

    raise ValueError(f"Unknown projector type: {projector_type}")

################ Resampler: Spatial Pool ####################
class SpatialPool(nn.Module):
    def __init__(self, model_args, vision_tower):
        super().__init__()

        self.mode = model_args.mm_spatial_pool_mode
        self.stride = model_args.mm_spatial_pool_stride
        self.out_channels = getattr(model_args, "mm_spatial_pool_out_channels", vision_tower.hidden_size)

        if self.mode == "average":
            self.pool = nn.AvgPool2d(kernel_size=self.stride, stride=self.stride)
        elif self.mode == "max":
            self.pool = nn.MaxPool2d(kernel_size=self.stride, stride=self.stride)
        elif self.mode == "conv":
            self.pool = nn.Conv2d(in_channels=vision_tower.hidden_size, out_channels=self.out_channels, kernel_size=self.stride, stride=self.stride)
        else:
            raise ValueError(f"Unknown pooling mode: {self.pool}.")

    def forward(self, image_features, images, *args, **kwargs):
        ori_W = int(math.sqrt(image_features.shape[1] * images.shape[3] // images.shape[2]))
        ori_H = int(ori_W * images.shape[2] // images.shape[3])

        B, _, F = image_features.shape

        image_features_spatial = image_features.view(B, ori_H, ori_H, F).permute(0, 3, 1, 2)
        image_features_spatial_pool = self.pool(image_features_spatial)

        return image_features_spatial_pool.flatten(2).transpose(1, 2).contiguous()

    @property
    def config(self):
        return {
            "mm_resampler_type": "spatial_pool",
            "mm_spatial_pool_stride": self.stride,
            "mm_spatial_pool_mode": self.mode,
            "mm_spatial_pool_out_channels": self.out_channels,
        }

    @property
    def hidden_size(self):
        return self.out_channels

def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self

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

    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, 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 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")

        self.config = config

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        query_embeds=None,
        past_key_values_length=0,
    ):
        if input_ids is not None:
            seq_length = input_ids.size()[1]
        else:
            seq_length = 0

        if position_ids is None:
            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length].clone()

        if input_ids is not None:
            embeddings = self.word_embeddings(input_ids)
            if self.position_embedding_type == "absolute":
                position_embeddings = self.position_embeddings(position_ids)
                embeddings = embeddings + position_embeddings

            if query_embeds is not None:
                embeddings = torch.cat((query_embeds, embeddings), dim=1)
        else:
            embeddings = query_embeds

        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertSelfAttention(nn.Module):
    def __init__(self, config, is_cross_attention):
        super().__init__()
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            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)
        if is_cross_attention:
            self.key = nn.Linear(config.encoder_width, self.all_head_size)
            self.value = nn.Linear(config.encoder_width, self.all_head_size)
        else:
            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)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    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=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        mixed_query_layer = self.query(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)

        past_key_value = (key_layer, 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))

        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            seq_length = hidden_states.size()[1]
            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # 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)

        if is_cross_attention and self.save_attention:
            self.save_attention_map(attention_probs)
            attention_probs.register_hook(self.save_attn_gradients)

        # 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_dropped = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs_dropped = attention_probs_dropped * head_mask

        context_layer = torch.matmul(attention_probs_dropped, 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)

        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

        outputs = outputs + (past_key_value,)
        return outputs


class BertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        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, is_cross_attention=False):
        super().__init__()
        self.self = BertSelfAttention(config, is_cross_attention)
        self.output = BertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads,
            self.self.num_attention_heads,
            self.self.attention_head_size,
            self.pruned_heads,
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )
        attention_output = self.output(self_outputs[0], hidden_states)

        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class BertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            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().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        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, layer_num):
        super().__init__()
        self.config = config
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = BertAttention(config)
        self.layer_num = layer_num
        if self.config.add_cross_attention and layer_num % self.config.cross_attention_freq == 0:
            self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention)
            self.has_cross_attention = True
        else:
            self.has_cross_attention = False
        self.intermediate = BertIntermediate(config)
        self.output = BertOutput(config)

        self.intermediate_query = BertIntermediate(config)
        self.output_query = BertOutput(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
        query_length=0,
    ):
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
        )
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:-1]

        present_key_value = self_attention_outputs[-1]

        if query_length > 0:
            query_attention_output = attention_output[:, :query_length, :]

            if self.has_cross_attention:
                assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers"
                cross_attention_outputs = self.crossattention(
                    query_attention_output,
                    attention_mask,
                    head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    output_attentions=output_attentions,
                )
                query_attention_output = cross_attention_outputs[0]
                outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights

            layer_output = apply_chunking_to_forward(
                self.feed_forward_chunk_query,
                self.chunk_size_feed_forward,
                self.seq_len_dim,
                query_attention_output,
            )
            if attention_output.shape[1] > query_length:
                layer_output_text = apply_chunking_to_forward(
                    self.feed_forward_chunk,
                    self.chunk_size_feed_forward,
                    self.seq_len_dim,
                    attention_output[:, query_length:, :],
                )
                layer_output = torch.cat([layer_output, layer_output_text], dim=1)
        else:
            layer_output = apply_chunking_to_forward(
                self.feed_forward_chunk,
                self.chunk_size_feed_forward,
                self.seq_len_dim,
                attention_output,
            )
        outputs = (layer_output,) + outputs

        outputs = outputs + (present_key_value,)

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output

    def feed_forward_chunk_query(self, attention_output):
        intermediate_output = self.intermediate_query(attention_output)
        layer_output = self.output_query(intermediate_output, attention_output)
        return layer_output


class BertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([BertLayer(config, i) for i in range(config.num_hidden_layers)])

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
        query_length=0,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None

        next_decoder_cache = () if use_cache else None

        for i in range(self.config.num_hidden_layers):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            if getattr(self.config, "gradient_checkpointing", False) and self.training:

                if use_cache:
                    logger.warn("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions, query_length)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                    query_length,
                )

            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)
                all_cross_attentions = all_cross_attentions + (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    next_decoder_cache,
                    all_hidden_states,
                    all_self_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


class BertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    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().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    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):
        super().__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(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

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


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

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


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

    config_class = BertConfig
    base_model_prefix = "bert"
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_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, nn.LayerNorm):
            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_()


class BertModel(BertPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=False):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
        has_query: bool = False,
    ) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`torch.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.
            device: (:obj:`torch.device`):
                The device of the input to the model.

        Returns:
            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
        """
        # 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.
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]
        elif attention_mask.dim() == 2:
            # Provided a padding mask of dimensions [batch_size, seq_length]
            # - if the model is a decoder, apply a causal mask in addition to the padding mask
            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
            if is_decoder:
                batch_size, seq_length = input_shape

                seq_ids = torch.arange(seq_length, device=device)
                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]

                # add a prefix ones mask to the causal mask
                # causal and attention masks must have same type with pytorch version < 1.3
                causal_mask = causal_mask.to(attention_mask.dtype)

                if causal_mask.shape[1] < attention_mask.shape[1]:
                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
                    if has_query:  # UniLM style attention mask
                        causal_mask = torch.cat(
                            [
                                torch.zeros(
                                    (batch_size, prefix_seq_len, seq_length),
                                    device=device,
                                    dtype=causal_mask.dtype,
                                ),
                                causal_mask,
                            ],
                            axis=1,
                        )
                    causal_mask = torch.cat(
                        [
                            torch.ones(
                                (batch_size, causal_mask.shape[1], prefix_seq_len),
                                device=device,
                                dtype=causal_mask.dtype,
                            ),
                            causal_mask,
                        ],
                        axis=-1,
                    )
                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
            else:
                extended_attention_mask = attention_mask[:, None, None, :]
        else:
            raise ValueError("Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(input_shape, attention_mask.shape))

        # 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=self.dtype)  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        query_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,
        is_decoder=False,
    ):
        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

        # use_cache = use_cache if use_cache is not None else self.config.use_cache

        if input_ids is None:
            assert query_embeds is not None, "You have to specify query_embeds when input_ids is None"

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] - self.config.query_length if past_key_values is not None else 0

        query_length = query_embeds.shape[1] if query_embeds is not None else 0

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            query_embeds=query_embeds,
            past_key_values_length=past_key_values_length,
        )

        input_shape = embedding_output.size()[:-1]
        batch_size, seq_length = input_shape
        device = embedding_output.device

        if attention_mask is None:
            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), 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.
        if is_decoder:
            extended_attention_mask = self.get_extended_attention_mask(
                attention_mask,
                input_ids.shape,
                device,
                is_decoder,
                has_query=(query_embeds is not None),
            )
        else:
            extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device, is_decoder)

        # 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 encoder_hidden_states is not None:
            if type(encoder_hidden_states) == list:
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
            else:
                (
                    encoder_batch_size,
                    encoder_sequence_length,
                    _,
                ) = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)

            if type(encoder_attention_mask) == list:
                encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
            elif 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 = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # 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)

        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,
            query_length=query_length,
        )
        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 BertLMHeadModel(BertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

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

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        query_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=True,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=True,
        reduction="mean",
    ):
        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**.
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
        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`).
        Returns:
        Example::
            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
            >>> import torch
            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
            >>> config = BertConfig.from_pretrained("bert-base-cased")
            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
            >>> outputs = model(**inputs)
            >>> prediction_logits = outputs.logits
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if labels is not None:
            use_cache = False
        if past_key_values is not None:
            query_embeds = None

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            query_embeds=query_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_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,
            is_decoder=is_decoder,
        )

        sequence_output = outputs[0]
        if query_embeds is not None:
            sequence_output = outputs[0][:, query_embeds.shape[1] :, :]

        prediction_scores = self.cls(sequence_output)

        if return_logits:
            return prediction_scores[:, :-1, :].contiguous()

        lm_loss = None
        if labels is not None:
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
            lm_loss = loss_fct(
                shifted_prediction_scores.view(-1, self.config.vocab_size),
                labels.view(-1),
            )
            if reduction == "none":
                lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs):
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_ids.shape)
        query_mask = input_ids.new_ones(query_embeds.shape[:-1])
        attention_mask = torch.cat([query_mask, attention_mask], dim=-1)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "query_embeds": query_embeds,
            "attention_mask": attention_mask,
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
        }

    def _reorder_cache(self, past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
        return reordered_past


class BertForMaskedLM(BertPreTrainedModel):

    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

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

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        query_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=False,
    ):
        r"""
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            query_embeds=query_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            is_decoder=is_decoder,
        )

        if query_embeds is not None:
            sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
        prediction_scores = self.cls(sequence_output)

        if return_logits:
            return prediction_scores

        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()  # -100 index = padding token
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output

        return MaskedLMOutput(
            loss=masked_lm_loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class Qformer(nn.Module):
    def __init__(self, model_args, vision_tower):
        super().__init__()

        self.depth = model_args.mm_qformer_depth
        self.num_latents = model_args.mm_qformer_latents
        self.pretrained = model_args.mm_qformer_pretrained

        self.Qformer, self.query_tokens, self.ln_vision = self.build_Qformer(vision_tower.hidden_size, self.depth, self.num_latents)

        if self.pretrained is not None:
            pretrained_dict = torch.load(self.pretrained, map_location="cpu")["model"]
            pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith("t5_proj")}
            self.load_state_dict(pretrained_dict)

    def build_Qformer(self, vision_width, cross_attention_freq, num_query_token):
        encoder_config = BertConfig.from_pretrained("bert-base-uncased")
        encoder_config.encoder_width = vision_width
        # insert cross-attention layer every other block
        encoder_config.add_cross_attention = True
        encoder_config.cross_attention_freq = cross_attention_freq
        encoder_config.query_length = num_query_token
        Qformer = BertLMHeadModel(config=encoder_config)
        query_tokens = nn.Parameter(torch.zeros(1, num_query_token, encoder_config.hidden_size))
        query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
        Qformer.cls = None
        Qformer.bert.embeddings.word_embeddings = None
        Qformer.bert.embeddings.position_embeddings = None
        for layer in Qformer.bert.encoder.layer:
            layer.output = None
            layer.intermediate = None
        return Qformer, query_tokens, nn.LayerNorm(vision_width)

    def forward(self, image_features, *args, **kwargs):
        x = self.ln_vision(image_features)
        image_atts = torch.ones(x.size()[:-1], dtype=torch.long).to(x.device)

        query_tokens = self.query_tokens.expand(x.shape[0], -1, -1)
        query_output = self.Qformer.bert(
            query_embeds=query_tokens,
            encoder_hidden_states=x,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        return query_output.last_hidden_state

    @property
    def hidden_size(self):
        return 768

    @property
    def config(self):
        return {
            "mm_resampler_type": "qformer",
            "mm_qformer_depth": self.depth,
            "mm_qformer_latents": self.num_latents,
            "mm_qformer_pretrained": self.pretrained,
        }


################### Resampler: Perciever ###################
def exists(val):
    return val is not None


def FeedForward(dim, mult=4):
    inner_dim = int(dim * mult)
    return nn.Sequential(
        nn.LayerNorm(dim),
        nn.Linear(dim, inner_dim, bias=False),
        nn.GELU(),
        nn.Linear(inner_dim, dim, bias=False),
    )


class PerceiverAttention(nn.Module):
    def __init__(self, *, dim, dim_head=64, heads=8):
        super().__init__()
        self.scale = dim_head**-0.5
        self.heads = heads
        inner_dim = dim_head * heads

        self.norm_media = nn.LayerNorm(dim)
        self.norm_latents = nn.LayerNorm(dim)

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
        self.to_out = nn.Linear(inner_dim, dim, bias=False)

    def forward(self, x, latents):
        """
        Args:
            x (torch.Tensor): image features
                shape (b, T, n1, D)
            latent (torch.Tensor): latent features
                shape (b, T, n2, D)
        """
        x = self.norm_media(x)
        latents = self.norm_latents(latents)

        h = self.heads

        q = self.to_q(latents)
        kv_input = torch.cat((x, latents), dim=-2)
        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
        q, k, v = rearrange_many((q, k, v), "b t n (h d) -> b h t n d", h=h)
        q = q * self.scale

        # attention
        sim = einsum("... i d, ... j d  -> ... i j", q, k)
        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
        attn = sim.softmax(dim=-1)

        out = einsum("... i j, ... j d -> ... i d", attn, v)
        out = rearrange(out, "b h t n d -> b t n (h d)", h=h)
        return self.to_out(out)


class PerceiverResamplerModule(nn.Module):
    def __init__(
        self,
        *,
        dim,
        depth=6,
        dim_head=64,
        heads=8,
        num_latents=64,
        max_num_media=None,
        max_num_frames=None,
        ff_mult=4,
    ):
        super().__init__()
        self.latents = nn.Parameter(torch.randn(num_latents, dim))
        self.frame_embs = nn.Parameter(torch.randn(max_num_frames, dim)) if exists(max_num_frames) else None
        self.media_time_embs = nn.Parameter(torch.randn(max_num_media, 1, dim)) if exists(max_num_media) else None

        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(
                nn.ModuleList(
                    [
                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
                        FeedForward(dim=dim, mult=ff_mult) if ff_mult > 0 else nn.Identity(),
                    ]
                )
            )

        self.norm = nn.LayerNorm(dim)

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): image features
                shape (b, T, F, v, D)
        Returns:
            shape (b, T, n, D) where n is self.num_latents
        """
        b, T, F, v = x.shape[:4]

        # frame and media time embeddings
        if exists(self.frame_embs):
            frame_embs = repeat(self.frame_embs[:F], "F d -> b T F v d", b=b, T=T, v=v)
            x = x + frame_embs
        x = rearrange(x, "b T F v d -> b T (F v) d")  # flatten the frame and spatial dimensions
        if exists(self.media_time_embs):
            x = x + self.media_time_embs[:T]

        # blocks
        latents = repeat(self.latents, "n d -> b T n d", b=b, T=T)
        for attn, ff in self.layers:
            latents = attn(x, latents) + latents
            latents = ff(latents) + latents
        return self.norm(latents)


class PerceiverResampler(nn.Module):
    def __init__(self, model_args, vision_tower):
        super().__init__()

        self.depth = model_args.mm_perceiver_depth
        self.num_latents = model_args.mm_perceiver_latents
        self.ff_mult = model_args.mm_perceiver_ff_mult
        self.pretrained = model_args.mm_perceiver_pretrained

        self.perceiver = PerceiverResamplerModule(dim=vision_tower.hidden_size, depth=self.depth, num_latents=self.num_latents, ff_mult=self.ff_mult)

        if self.pretrained is not None:
            self.load_state_dict(torch.load(self.pretrained))

    def forward(self, image_features, *args, **kwargs):
        return self.perceiver(image_features[:, None, None]).squeeze(1)

    @property
    def config(self):
        return {
            "mm_resampler_type": "perceiver",
            "mm_perceiver_depth": self.depth,
            "mm_perceiver_latents": self.num_latents,
            "mm_perceiver_ff_mult": self.ff_mult,
            "mm_perceiver_pretrained": self.pretrained,
        }

######################### Resampler: Masker Drop ######################### 
class MaskedDrop(nn.Module):
    def __init__(self, model_args):
        super().__init__()

        self.mode = model_args.mm_mask_drop_mode
        self.skip_percentage = model_args.mm_mask_drop_skip_percentage
        self.ratio = model_args.mm_mask_drop_ratio
        self.ratio_upper = model_args.mm_mask_drop_ratio_upper
        self.ratio_lower = model_args.mm_mask_drop_ratio_lower

    def forward(self, image_features, *args, **kwargs):

        if not self.training:
            return image_features

        if self.skip_percentage > random.random():
            return image_features

        masked_features = []

        for image_feature in image_features:
            num_tokens = image_feature.shape[0]
            if self.mode == "fixed":
                num_keep = int(num_tokens * self.ratio)
                masked_features.append(self.random_masking(image_feature.unsqueeze(0), num_keep)[0][0])
            elif self.mode == "range":
                num_keep = int(num_tokens * random.uniform(self.ratio_lower, self.ratio_upper))
                masked_features.append(self.random_masking(image_feature.unsqueeze(0), num_keep)[0])
            elif self.mode == "cls_only":
                masked_features.append(image_feature[0:1])
            else:
                raise ValueError(f"Unexpected masked drop mode: {self.mode}")

        if self.mode not in ["range"] and (type(image_features) is not list or self.mode in ["cls_only"]):
            masked_features = torch.stack(masked_features, dim=0)

        return masked_features

    @property
    def config(self):
        return {
            "mm_resampler_type": "masked_drop",
            "mm_mask_drop_mode": self.mode,
            "mm_mask_drop_skip_percentage": self.skip_percentage,
            "mm_mask_drop_ratio": self.ratio,
            "mm_mask_drop_ratio_upper": self.ratio_upper,
            "mm_mask_drop_ratio_lower": self.ratio_lower,
        }

    def random_masking(self, x, len_keep):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim

        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

class IdentityMap(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, *args, **kwargs):
        return x

    @property
    def config(self):
        return {"mm_resampler_type": None}

###################### Resampler - Builder ######################
def build_vision_resampler(model_args, delay_load=False, **kwargs):
    resampler_type = getattr(model_args, "mm_resampler_type", None)
    if resampler_type == "masked_drop":
        return MaskedDrop(model_args)
    elif resampler_type == "spatial_pool":
        return SpatialPool(model_args, **kwargs)
    elif resampler_type == "perceiver":
        return PerceiverResampler(model_args, **kwargs)
    elif resampler_type == "qformer":
        return Qformer(model_args, **kwargs)
    elif resampler_type is None:
        return IdentityMap()

    raise ValueError(f"Unknown resampler type: {resampler_type}")

from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig

######################## Vision Tower ######################
class CLIPVisionTower(nn.Module):
    r"""
    A class to represent the CLIP Vision Tower model.
    
    Attributes :
    ------------
        - is_loaded (bool): A flag indicating whether the model is loaded.
        - vision_tower_name (str): The name of the vision tower model.
        - select_layer (int): The layer to select features from.
        - select_feature (str): The type of feature to select.

    Methods :
    ------------
        - `__init__(vision_tower: str, args: Namespace, delay_load: bool = False)`: Initializes the CLIPVisionTower with the given vision tower name and arguments.
        - `load_model(device_map: Optional[dict] = None)`: Loads the vision tower model and image processor.
        - `feature_select(image_forward_outs: Any) -> torch.Tensor`: Selects features from the image forward outputs based on the specified feature type.
        - `forward(images: Union[torch.Tensor, List[torch.Tensor]]) -> torch.Tensor`: Forward pass for the vision tower model.
        - `dummy_feature() -> torch.Tensor`: Returns a dummy feature tensor.
        - `dtype() -> torch.dtype`: Returns the data type of the vision tower model.
        - `device() -> torch.device`: Returns the device of the vision tower model.
        - `config() -> Any`: Returns the configuration of the vision tower model.
        - `hidden_size() -> int`: Returns the hidden size of the vision tower model.
        - `num_patches_per_side() -> int`: Returns the number of patches per side of the image.
        - `num_patches() -> int`: Returns the total number of patches in the image.
        - `image_size() -> int`: Returns the size of the image.
    """

    def __init__(self, vision_tower, args, delay_load=False):
        super().__init__()

        self.is_loaded = False

        self.vision_tower_name = vision_tower
        self.select_layer = args.mm_vision_select_layer
        self.select_feature = getattr(args, "mm_vision_select_feature", "patch")

        if not delay_load:
            rank0_print(f"Loading vision tower: {vision_tower}")
            self.load_model()
        elif getattr(args, "unfreeze_mm_vision_tower", False):
            # TODO: better detector is needed.
            rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.")
            self.load_model()
        elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
            rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`.")
            self.load_model()
        else:
            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)

    def load_model(self, device_map=None):
        if self.is_loaded:
            rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
            return

        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
        self.vision_tower.requires_grad_(False)

        self.is_loaded = True

    def feature_select(self, image_forward_outs):
        select_feature_type = self.select_feature

        if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
            select_every_k_layer = len(image_forward_outs.hidden_states) // 4
            image_features = torch.cat([image_forward_outs.hidden_states[i] for i in range(select_every_k_layer + self.select_layer, len(image_forward_outs.hidden_states), select_every_k_layer)], dim=-1)
            select_feature_type = select_feature_type.replace("slicefour_", "")
        elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]:
            select_layers = [-2, -5, -8, -11, 6]
            image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1)
            select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
        else:
            image_features = image_forward_outs.hidden_states[self.select_layer]

        if select_feature_type == "patch":
            image_features = image_features[:, 1:]
        elif select_feature_type == "cls_patch":
            image_features = image_features
        else:
            raise ValueError(f"Unexpected select feature: {select_feature_type}")
        return image_features

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
                image_feature = self.feature_select(image_forward_out).to(image.dtype)
                image_features.append(image_feature)
        else:
            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
            image_features = self.feature_select(image_forward_outs).to(images.dtype)

        return image_features

    @property
    def dummy_feature(self):
        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def config(self):
        if self.is_loaded:
            return self.vision_tower.config
        else:
            return self.cfg_only

    @property
    def hidden_size(self):
        _hidden_size = self.config.hidden_size
        if "slicefour" in self.select_feature:
            _hidden_size *= 4
        if "slice_m25811_f6" in self.select_feature:
            _hidden_size *= 5
        return _hidden_size

    @property
    def num_patches_per_side(self):
        return self.config.image_size // self.config.patch_size

    @property
    def num_patches(self):
        _num_patches = (self.config.image_size // self.config.patch_size) ** 2
        if "cls_patch" in self.select_feature:
            _num_patches += 1
        return _num_patches

    @property
    def image_size(self):
        return self.config.image_size

def build_vision_tower(vision_tower_cfg, **kwargs):
    vision_tower = getattr(vision_tower_cfg, "mm_vision_tower", getattr(vision_tower_cfg, "vision_tower", None))
    is_absolute_path_exists = os.path.exists(vision_tower)
    if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion") or "ShareGPT4V" in vision_tower:
        return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)

    raise ValueError(f"Unknown vision tower: {vision_tower}")

class InstellaVLMetaModel:

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

        if hasattr(config, "mm_vision_tower"):
            delay_load = getattr(config, "delay_load", False)
            self.vision_tower = build_vision_tower(config, delay_load=delay_load)
            self.vision_resampler = build_vision_resampler(config, vision_tower=self.vision_tower)
            self.mm_projector = build_vision_projector(config, vision_cfg=self.vision_tower.config)

            if "unpad" in getattr(config, "mm_patch_merge_type", ""):
                self.image_newline = nn.Parameter(torch.empty(config.hidden_size, dtype=self.dtype))

    def get_vision_tower(self):
        vision_tower = getattr(self, "vision_tower", None)
        if type(vision_tower) is list:
            vision_tower = vision_tower[0]
        return vision_tower

    def initialize_vision_modules(self, model_args, fsdp=None):
        vision_tower = model_args.vision_tower
        mm_vision_select_layer = model_args.mm_vision_select_layer
        mm_vision_select_feature = model_args.mm_vision_select_feature
        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
        mm_patch_merge_type = model_args.mm_patch_merge_type

        self.config.mm_vision_tower = vision_tower
        self.config.vision_tower_pretrained = getattr(model_args, "vision_tower_pretrained", "")

        if self.get_vision_tower() is None:
            vision_tower = build_vision_tower(model_args)
            vision_resampler = build_vision_resampler(model_args, vision_tower=vision_tower)
            for k, v in vision_resampler.config.items():
                setattr(self.config, k, v)

            if fsdp is not None and len(fsdp) > 0:
                self.vision_tower = [vision_tower]
                self.vision_resampler = [vision_resampler]
            else:
                self.vision_tower = vision_tower
                self.vision_resampler = vision_resampler
        else:
            if fsdp is not None and len(fsdp) > 0:
                vision_resampler = self.vision_resampler[0]
                vision_tower = self.vision_tower[0]
            else:
                vision_resampler = self.vision_resampler
                vision_tower = self.vision_tower
            vision_tower.load_model()

            # In case it is frozen by LoRA
            for p in self.vision_resampler.parameters():
                p.requires_grad = True

        self.config.use_mm_proj = True
        self.config.mm_projector_type = getattr(model_args, "mm_projector_type", "linear")
        self.config.mm_hidden_size = getattr(vision_resampler, "hidden_size", vision_tower.hidden_size)
        self.config.mm_vision_select_layer = mm_vision_select_layer
        self.config.mm_vision_select_feature = mm_vision_select_feature
        self.config.mm_patch_merge_type = mm_patch_merge_type
        self.config.online_training = model_args.online_training

        if getattr(self, "mm_projector", None) is None:
            self.mm_projector = build_vision_projector(self.config, vision_cfg=vision_tower.config)

            if "unpad" in mm_patch_merge_type:
                embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
                self.image_newline = nn.Parameter(torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std)
        else:
            # In case it is frozen by LoRA
            for p in self.mm_projector.parameters():
                p.requires_grad = True

        if pretrain_mm_mlp_adapter is not None:
            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location="cpu")

            def get_w(weights, keyword):
                return {k.split(keyword + ".")[1]: v for k, v in weights.items() if keyword in k}

            incompatible_keys = self.mm_projector.load_state_dict(get_w(mm_projector_weights, "mm_projector"))
            rank0_print(f"Loaded mm projector weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}")
            incompatible_keys = self.vision_resampler.load_state_dict(get_w(mm_projector_weights, "vision_resampler"), strict=False)
            rank0_print(f"Loaded vision resampler weights from {pretrain_mm_mlp_adapter}. Incompatible keys: {incompatible_keys}") 
        
            if 'tmp-' in pretrain_mm_mlp_adapter:
                pretrain_mm_mlp_adapter_folder = os.path.dirname(pretrain_mm_mlp_adapter)
                shutil.rmtree(pretrain_mm_mlp_adapter_folder, ignore_errors=True)
            


def unpad_image(tensor, original_size):
    """
    Unpads a PyTorch tensor of a padded and resized image.

    Args:
    tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
    original_size (tuple): The original size of the image (height, width).

    Returns:
    torch.Tensor: The unpadded image tensor.
    """
    original_width, original_height = original_size
    current_height, current_width = tensor.shape[1:]

    # Compute aspect ratios
    original_aspect_ratio = original_width / original_height
    current_aspect_ratio = current_width / current_height

    # Determine padding size and direction
    if original_aspect_ratio > current_aspect_ratio:
        # Padding was added to the height
        scale_factor = current_width / original_width
        new_height = int(original_height * scale_factor)
        padding = (current_height - new_height) // 2
        unpadded_tensor = tensor[:, padding : current_height - padding, :]
    else:
        # Padding was added to the width
        scale_factor = current_height / original_height
        new_width = int(original_width * scale_factor)
        padding = (current_width - new_width) // 2
        unpadded_tensor = tensor[:, :, padding : current_width - padding]

    return unpadded_tensor


class InstellaVLMetaForCausalLM(ABC):

    @abstractmethod
    def get_model(self):
        pass

    def get_vision_tower(self):
        return self.get_model().get_vision_tower()

    def get_2dPool(self, image_feature):
        height = width = self.get_vision_tower().num_patches_per_side
        num_frames, num_tokens, num_dim = image_feature.shape
        image_feature = image_feature.view(num_frames, height, width, -1)
        image_feature = image_feature.permute(0, 3, 1, 2).contiguous()
        # image_feature = nn.functional.max_pool2d(image_feature, self.config.mm_spatial_pool_stride)
        if self.config.mm_spatial_pool_mode == "average":
            image_feature = nn.functional.avg_pool2d(image_feature, self.config.mm_spatial_pool_stride)
        elif self.config.mm_spatial_pool_mode == "max":
            image_feature = nn.functional.max_pool2d(image_feature, self.config.mm_spatial_pool_stride)
        elif self.config.mm_spatial_pool_mode == "bilinear":
            height, weight = image_feature.shape[2:]
            scaled_shape = [math.ceil(height / 2), math.ceil(weight / 2)]
            image_feature = nn.functional.interpolate(image_feature, size=scaled_shape, mode='bilinear')

        else:
            raise ValueError(f"Unexpected mm_spatial_pool_mode: {self.config.mm_spatial_pool_mode}")
        image_feature = image_feature.permute(0, 2, 3, 1)
        image_feature = image_feature.view(num_frames, -1, num_dim)
        return image_feature

    def encode_images(self, images):
        image_features = self.get_model().get_vision_tower()(images)
        # image_features = self.get_model().vision_resampler(image_features, images=images)
        image_features = self.get_model().mm_projector(image_features)
        return image_features
    
    def encode_multimodals(self, videos_or_images, video_idx_in_batch, split_sizes=None):
        videos_or_images_features = self.get_model().get_vision_tower()(videos_or_images)
        per_videos_or_images_features = torch.split(videos_or_images_features, split_sizes, dim=0)  # tuple, (dim_1, 576, 4096)
        all_videos_or_images_features = []

        for idx, feat in enumerate(per_videos_or_images_features):
            feat = self.get_model().mm_projector(feat)
            if idx in video_idx_in_batch:
                feat = self.get_2dPool(feat)
            all_videos_or_images_features.append(feat)
        return all_videos_or_images_features

    def add_token_per_grid(self, image_feature):
        resize_h = int(math.sqrt(image_feature.shape[1]))
        num_frames = image_feature.shape[0]
        image_feature = image_feature.view(num_frames, 1, resize_h, resize_h, -1)
        image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
        image_feature = image_feature.flatten(1, 2).flatten(2, 3)
        image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
        image_feature = image_feature.flatten(1, 2).transpose(0, 1)
        return image_feature

    def add_token_per_frame(self, image_feature):
        image_feature = image_feature.permute(2, 0, 1).contiguous()
        image_feature =  torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
        image_feature = image_feature.permute(1, 2, 0).contiguous()
        return image_feature

    def prepare_inputs_labels_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities=["image"], image_sizes=None):
        vision_tower = self.get_vision_tower()
        # rank_print(modalities)
        if vision_tower is None or images is None or input_ids.shape[1] == 1:
            return input_ids, position_ids, attention_mask, past_key_values, None, labels

        if isinstance(modalities, str):
            modalities = [modalities]

        if type(images) is list or images.ndim == 5:
            if type(images) is list:
                images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]

            video_idx_in_batch = []
            for _ in range(len(modalities)):
                if modalities[_] == "video":
                    video_idx_in_batch.append(_)

            # print(video_idx_in_batch)

            images_list = []
            for image in images:
                if image.ndim == 4:
                    images_list.append(image)
                else:
                    images_list.append(image.unsqueeze(0))

            # import pdb;pdb.set_trace()
            concat_images = torch.cat([image for image in images_list], dim=0)
            split_sizes = [image.shape[0] for image in images_list]
            encoded_image_features = self.encode_images(concat_images)
            # import pdb
            # pdb.set_trace()

            # This is a list, each element is [num_images, patch * patch, dim]
            # rank_print(f"Concat images : {concat_images.shape}")
            encoded_image_features = torch.split(encoded_image_features, split_sizes)
            image_features = []
            for idx, image_feat in enumerate(encoded_image_features):
                if idx in video_idx_in_batch:
                    image_features.append(self.get_2dPool(image_feat))
                else:
                    image_features.append(image_feat)
            # image_features = self.encode_multimodals(concat_images, video_idx_in_batch, split_sizes)
            # rank_print(f"Encoded image feats : {[x.shape for x in image_features]}")
            # image_features = torch.split(image_features, split_sizes, dim=0)
            mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
            image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")

            if mm_patch_merge_type == "flat":
                image_features = [x.flatten(0, 1) for x in image_features]

            elif mm_patch_merge_type.startswith("spatial"):
                new_image_features = []
                for image_idx, image_feature in enumerate(image_features):
                    # FIXME: now assume the image is square, and split to 2x2 patches
                    # num_patches = h * w, where h = w = sqrt(num_patches)
                    # currently image_feature is a tensor of shape (4, num_patches, hidden_size)
                    # we want to first unflatten it to (2, 2, h, w, hidden_size)
                    # rank0_print("At least we are reaching here")
                    if image_idx in video_idx_in_batch:  # video operations
                        # rank0_print("Video")
                        if self.config.mm_newline_position == "grid":
                            # Grid-wise
                            image_feature = self.add_token_per_grid(image_feature)
                        
                            new_image_features.append(image_feature)
                        elif self.config.mm_newline_position == "frame":
                            # Frame-wise
                            image_feature = self.add_token_per_frame(image_feature)

                            new_image_features.append(image_feature.flatten(0, 1))
                            
                        elif self.config.mm_newline_position == "one_token":
                            # one-token
                            image_feature = image_feature.flatten(0, 1)
                            if 'unpad' in mm_patch_merge_type:
                                image_feature = torch.cat((
                                    image_feature,
                                    self.model.image_newline[None].to(image_feature.device)
                                ), dim=0)
                            new_image_features.append(image_feature)      
                        elif self.config.mm_newline_position == "no_token":
                            new_image_features.append(image_feature.flatten(0, 1))
                        else:
                            raise ValueError(f"Unexpected mm_newline_position: {self.config.mm_newline_position}")


                    elif image_feature.shape[0] > 1:  # multi patches and multi images operations
                        base_image_feature = image_feature[0]
                        image_feature = image_feature[1:]
                        height = width = self.get_vision_tower().num_patches_per_side

                        assert height * width == base_image_feature.shape[0]

                        if "anyres_max" in image_aspect_ratio:
                            matched_anyres_max_num_patches = re.match(r"anyres_max_(\d+)", image_aspect_ratio)
                            if matched_anyres_max_num_patches:
                                max_num_patches = int(matched_anyres_max_num_patches.group(1))

                        if image_aspect_ratio == "anyres" or "anyres_max" in image_aspect_ratio:
                            if hasattr(self.get_vision_tower(), "image_size"):
                                vision_tower_image_size = self.get_vision_tower().image_size
                            else:
                                raise ValueError("vision_tower_image_size is not found in the vision tower.")
                            try:
                                num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, vision_tower_image_size)
                            except Exception as e:
                                rank0_print(f"Error: {e}")
                                num_patch_width, num_patch_height = 2, 2
                            image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
                        else:
                            image_feature = image_feature.view(2, 2, height, width, -1)

                        if "maxpool2x2" in mm_patch_merge_type:
                            image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
                            image_feature = image_feature.flatten(1, 2).flatten(2, 3)
                            image_feature = nn.functional.max_pool2d(image_feature, 2)
                            image_feature = image_feature.flatten(1, 2).transpose(0, 1)
                        elif "unpad" in mm_patch_merge_type and "anyres_max" in image_aspect_ratio and matched_anyres_max_num_patches:
                            unit = image_feature.shape[2]
                            image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
                            image_feature = image_feature.flatten(1, 2).flatten(2, 3)
                            image_feature = unpad_image(image_feature, image_sizes[image_idx])
                            c, h, w = image_feature.shape
                            times = math.sqrt(h * w / (max_num_patches * unit**2))
                            if times > 1.1:
                                image_feature = image_feature[None]
                                image_feature = nn.functional.interpolate(image_feature, [int(h // times), int(w // times)], mode="bilinear")[0]
                            image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
                            image_feature = image_feature.flatten(1, 2).transpose(0, 1)
                        elif "unpad" in mm_patch_merge_type:
                            image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
                            image_feature = image_feature.flatten(1, 2).flatten(2, 3)
                            image_feature = unpad_image(image_feature, image_sizes[image_idx])
                            image_feature = torch.cat((image_feature, self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)), dim=-1)
                            image_feature = image_feature.flatten(1, 2).transpose(0, 1)
                        else:
                            image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
                            image_feature = image_feature.flatten(0, 3)
                        if "nobase" in mm_patch_merge_type:
                            pass
                        else:
                            image_feature = torch.cat((base_image_feature, image_feature), dim=0)
                    else:  # single image operations
                        image_feature = image_feature[0]
                        if "unpad" in mm_patch_merge_type:
                            image_feature = torch.cat((image_feature, self.model.image_newline[None]), dim=0)

                    new_image_features.append(image_feature)
                image_features = new_image_features
            else:
                raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
        else:
            image_features = self.encode_images(images)

        # TODO: image start / end is not implemented here to support pretraining.
        if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr(self.config, "mm_use_im_start_end", False):
            raise NotImplementedError
        # rank_print(f"Total images : {len(image_features)}")

        # Let's just add dummy tensors if they do not exist,
        # it is a headache to deal with None all the time.
        # But it is not ideal, and if you have a better idea,
        # please open an issue / submit a PR, thanks.
        _labels = labels
        _position_ids = position_ids
        _attention_mask = attention_mask
        if attention_mask is None:
            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
        else:
            attention_mask = attention_mask.bool()
        if position_ids is None:
            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
        if labels is None:
            labels = torch.full_like(input_ids, IGNORE_INDEX)

        # remove the padding using attention_mask -- FIXME
        _input_ids = input_ids
        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]

        new_input_embeds = []
        new_labels = []
        cur_image_idx = 0
        # rank_print("Inserting Images embedding")
        for batch_idx, cur_input_ids in enumerate(input_ids):
            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
            # rank0_print(num_images)
            if num_images == 0:
                try:
                    cur_image_features = image_features[cur_image_idx]
                except IndexError:
                    try:
                        cur_image_features = image_features[cur_image_idx - 1]
                    except IndexError:
                        pass
                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
                new_input_embeds.append(cur_input_embeds)
                new_labels.append(labels[batch_idx])
                cur_image_idx += 1
                continue

            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
            cur_input_ids_noim = []
            cur_labels = labels[batch_idx]
            cur_labels_noim = []
            for i in range(len(image_token_indices) - 1):
                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1 : image_token_indices[i + 1]])
                cur_labels_noim.append(cur_labels[image_token_indices[i] + 1 : image_token_indices[i + 1]])
            split_sizes = [x.shape[0] for x in cur_labels_noim]
            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
            cur_new_input_embeds = []
            cur_new_labels = []

            for i in range(num_images + 1):
                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
                cur_new_labels.append(cur_labels_noim[i])
                if i < num_images:
                    try:
                        cur_image_features = image_features[cur_image_idx]
                    except IndexError:
                        cur_image_features = image_features[cur_image_idx - 1]
                    cur_image_idx += 1
                    cur_new_input_embeds.append(cur_image_features)
                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))

            cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]

            # import pdb; pdb.set_trace()
            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
            cur_new_labels = torch.cat(cur_new_labels)

            new_input_embeds.append(cur_new_input_embeds)
            new_labels.append(cur_new_labels)

        # Truncate sequences to max length as image embeddings can make the sequence longer
        tokenizer_model_max_length = getattr(self.config, "tokenizer_model_max_length", None)
        # rank_print("Finishing Inserting")

        new_input_embeds = [x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)]
        new_labels = [x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)]
        # TODO: Hard code for control loss spike
        # if tokenizer_model_max_length is not None:
        #     new_input_embeds = [x[:4096] if modality != "video" else x[:tokenizer_model_max_length] for x, modality in zip(new_input_embeds, modalities)]
        #     new_labels = [x[:4096] if modality != "video" else x[:tokenizer_model_max_length] for x, modality in zip(new_labels, modalities)]

        # Combine them
        max_len = max(x.shape[0] for x in new_input_embeds)
        batch_size = len(new_input_embeds)

        new_input_embeds_padded = []
        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
        # rank0_print("Prepare pos id")

        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
            cur_len = cur_new_embed.shape[0]
            if getattr(self.config, "tokenizer_padding_side", "right") == "left":
                new_input_embeds_padded.append(torch.cat((torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device), cur_new_embed), dim=0))
                if cur_len > 0:
                    new_labels_padded[i, -cur_len:] = cur_new_labels
                    attention_mask[i, -cur_len:] = True
                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
            else:
                new_input_embeds_padded.append(torch.cat((cur_new_embed, torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0))
                if cur_len > 0:
                    new_labels_padded[i, :cur_len] = cur_new_labels
                    attention_mask[i, :cur_len] = True
                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)

        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
        # rank0_print("tokenizer padding")

        if _labels is None:
            new_labels = None
        else:
            new_labels = new_labels_padded

        if _attention_mask is None:
            attention_mask = None
        else:
            attention_mask = attention_mask.to(dtype=_attention_mask.dtype)

        if _position_ids is None:
            position_ids = None
        if getattr(self.config, "use_pos_skipping", False) and self.training:
            position_ids = torch.arange(new_input_embeds.size(1), device=new_input_embeds.device).unsqueeze(0).to(new_input_embeds.device)
            split_position = random.randint(0, new_input_embeds.size(1))
            left_add = random.randint(0, self.config.pos_skipping_range)
            right_add = random.randint(left_add, self.config.pos_skipping_range)
            position_ids[:, :split_position] += left_add
            position_ids[:, split_position:] += right_add
        # rank0_print("Finish preparing")
        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels

    def initialize_vision_tokenizer(self, model_args, tokenizer):
        if model_args.mm_use_im_patch_token:
            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
            self.resize_token_embeddings(len(tokenizer))

        if model_args.mm_use_im_start_end:
            num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
            self.resize_token_embeddings(len(tokenizer))

            if num_new_tokens > 0:
                input_embeddings = self.get_input_embeddings().weight.data
                output_embeddings = self.get_output_embeddings().weight.data

                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)

                input_embeddings[-num_new_tokens:] = input_embeddings_avg
                output_embeddings[-num_new_tokens:] = output_embeddings_avg

            if model_args.tune_mm_mlp_adapter:
                for p in self.get_input_embeddings().parameters():
                    p.requires_grad = True
                for p in self.get_output_embeddings().parameters():
                    p.requires_grad = False

            if model_args.pretrain_mm_mlp_adapter:
                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location="cpu")
                embed_tokens_weight = mm_projector_weights["model.embed_tokens.weight"]
                assert num_new_tokens == 2
                if input_embeddings.shape == embed_tokens_weight.shape:
                    input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
                elif embed_tokens_weight.shape[0] == num_new_tokens:
                    input_embeddings[-num_new_tokens:] = embed_tokens_weight
                else:
                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
        elif model_args.mm_use_im_patch_token:
            if model_args.tune_mm_mlp_adapter:
                for p in self.get_input_embeddings().parameters():
                    p.requires_grad = False
                for p in self.get_output_embeddings().parameters():
                    p.requires_grad = False

class InstellaVLConfig(OlmoConfig):
    """
    Configuration class for the InstellaVL model.
    Attributes:
        model_type (str): The type of the model, set to "instellavl".
    """

    model_type = "instellavl"


def disable_torch_init():
    r"""
    Disable the redundant torch default initialization to accelerate model creation.
    """
    import torch

    setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
    setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)


class InstellaVLModel(InstellaVLMetaModel, OlmoModel):
    config_class = InstellaVLConfig

    def __init__(self, config: OlmoConfig):
        super(InstellaVLModel, self).__init__(config)


class InstellaVLForCausalLM(OlmoForCausalLM, InstellaVLMetaForCausalLM):
    r"""
    InstellaVLForCausalLM is a class that extends OlmoForCausalLM and InstellaVLMetaForCausalLM to provide
    a language model with multimodal capabilities, specifically for handling images along with text.
    
    1. Attributes:
        - config_class (type): The configuration class to use for this model.
        - model (InstellaVLModel): The underlying model.
        - lm_head (nn.Linear): The linear layer for language modeling head.
    
    2. Methods:
        
        1. `__init__(config: InstellaVLConfig)`:
            Initializes the InstellaVLForCausalLM model with the given configuration.

        2. `get_model() -> InstellaVLModel`:
            Returns the underlying model.

        3. `forward() -> Union[Tuple, CausalLMOutputWithPast]`:
            Performs a forward pass through the model.
            
        4. `generate() -> Union[GenerateOutput, torch.LongTensor]`:
            Generates text based on the input.
            
        5. `prepare_inputs_for_generation(input_ids: torch.LongTensor,) -> dict`:
            Prepares inputs for text generation.
            
    """

    config_class = InstellaVLConfig

    def __init__(self, config: OlmoConfig):
        r"""
        Initializes the InstellaVLForCausalLM model.

        Args:
            - config (OlmoConfig): Configuration object for the model.

        Attributes:
            - model (InstellaVLModel): The main model instance.
            - lm_head (torch.nn.Linear): Linear layer that maps hidden states to vocabulary size.
        """
        super(OlmoForCausalLM, self).__init__(config)
        disable_torch_init()
        config.model_type = "instellavl"
        self.model = InstellaVLModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()


    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[List[List[int]]] = None,
        return_dict: Optional[bool] = None,
        modalities: Optional[List[str]] = ["image"],
        cache_position=None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            - input_ids (torch.LongTensor, optional): Input token IDs.
            - attention_mask (torch.Tensor, optional): Attention mask.
            - position_ids (torch.LongTensor, optional): Position IDs.
            - past_key_values (List[torch.FloatTensor], optional): Past key values for caching.
            - inputs_embeds (torch.FloatTensor, optional): Input embeddings.
            - labels (torch.LongTensor, optional): Labels for language modeling.
            - use_cache (bool, optional): Whether to use cache.
            - output_attentions (bool, optional): Whether to output attentions.
            - output_hidden_states (bool, optional): Whether to output hidden states.
            - images (torch.FloatTensor, optional): Input images.
            - image_sizes (List[List[int]], optional): Sizes of input images.
            - return_dict (bool, optional): Whether to return a dictionary.
            - modalities (List[str], optional): List of modalities.
            - cache_position (optional): Cache position.
        
        Returns:
            Union[Tuple, CausalLMOutputWithPast]: The output of the forward pass.
        """
        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                labels,
                images,
                modalities,
                image_sizes
            )

        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        modalities: Optional[List[str]] = ["image"],
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        r"""
        Args:
            - inputs (torch.Tensor, optional): Input tensor.
            - images (torch.Tensor, optional): Input images.
            - image_sizes (torch.Tensor, optional): Sizes of input images.
            - modalities (List[str], optional): List of modalities.
            - **kwargs: Additional arguments.
        
        Returns:
            Union[GenerateOutput, torch.LongTensor]: The generated text.
        """
        modalities = kwargs.pop("modalities", None) if "modalities" in kwargs and modalities is None else modalities
        position_ids = kwargs.pop("position_ids", None)
        attention_mask = kwargs.pop("attention_mask", None)
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")

        if images is not None:
            (
                inputs,
                position_ids,
                attention_mask,
                _,
                inputs_embeds,
                _
            ) = self.prepare_inputs_labels_for_multimodal(
                inputs,
                position_ids,
                attention_mask,
                None,
                None,
                images,
                image_sizes=image_sizes
            )
        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)
        return super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            **kwargs
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
                                      inputs_embeds=None, **kwargs):
        r"""
        Args:
            - input_ids (torch.LongTensor): Input token IDs.
            - past_key_values (List[torch.FloatTensor], optional): Past key values for caching.
            - inputs_embeds (torch.FloatTensor, optional): Input embeddings.
            - **kwargs: Additional arguments.
        
        Returns:
            dict: Prepared inputs for generation.
        """
        images = kwargs.pop("images", None)
        image_sizes = kwargs.pop("image_sizes", None)
        inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
        )
        if images is not None:
            inputs['images'] = images
        if image_sizes is not None:
            inputs['image_sizes'] = image_sizes
        return inputs

AutoConfig.register("instellavl", InstellaVLConfig)
AutoModelForCausalLM.register(InstellaVLConfig, InstellaVLForCausalLM)