Add Florence-Bridge code and handoff documentation

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from typing import Dict, List, Optional, Sequence, Tuple, Callable

import torch
from flash_attn.flash_attn_interface import flash_attn_varlen_func
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers import Trainer
from transformers.cache_utils import Cache
from transformers.utils.deprecation import deprecate_kwarg
from transformers.processing_utils import Unpack
from transformers.models.qwen2_vl.modeling_qwen2_vl import (
    Qwen2VisionTransformerPretrainedModel,
    Qwen2VLModel,
    apply_multimodal_rotary_pos_emb,
)
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
    Qwen2_5_VisionTransformerPretrainedModel,
    Qwen2_5_VLModel,
)
from transformers.models.qwen3_vl.modeling_qwen3_vl import (
    Qwen3VLVisionModel,
    Qwen3VLModel,
    apply_rotary_pos_emb,
)
from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import (
    Qwen3VLMoeVisionModel,
    Qwen3VLMoeModel,
)
from transformers.utils import logging

logger = logging.get_logger(__name__)


def flash_attention_forward(
    module: torch.nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    dropout: float = 0.0,
    scaling: Optional[float] = None,
    sliding_window: Optional[int] = None,
    softcap: Optional[float] = None,
    **kwargs,
) -> tuple[torch.Tensor, None]:
    if kwargs.get("output_attentions", False) or kwargs.get("head_mask") is not None:
        logger.warning_once(
            "`flash_attention_2` does not support `output_attentions=True` or `head_mask`."
            " Please set your attention to `eager` if you want any of these features."
        )
    
    # This is before the transpose
    seq_len = query.shape[2]

    if any(dim == 0 for dim in query.shape):
        raise ValueError(
            "Tensor query has shape  with a zero dimension.\n"
            "FlashAttention does not support inputs with dim=0.\n"
            "Please check your input shapes or use SDPA instead."
        )
    # FA2 uses non-transposed inputs
    # batch, head, seq_len, dim
    query = query.transpose(1, 2)
    key = key.transpose(1, 2)
    value = value.transpose(1, 2)
    # batch, seqlen, head, dim

    # In PEFT, usually we cast the layer norms in float32 for training stability reasons
    # therefore the input hidden states gets silently casted in float32. Hence, we need
    # cast them back in the correct dtype just to be sure everything works as expected.
    # This might slowdown training & inference so it is recommended to not cast the LayerNorms
    # in fp32. (usually our RMSNorm modules handle it correctly)
    target_dtype = None
    if query.dtype == torch.float32:
        if torch.is_autocast_enabled():
            target_dtype = torch.get_autocast_gpu_dtype()
        # Handle the case where the model is quantized
        elif hasattr(module.config, "_pre_quantization_dtype"):
            target_dtype = module.config._pre_quantization_dtype
        else:
            target_dtype = next(layer for layer in module.modules() if isinstance(layer, torch.nn.Linear)).weight.dtype

    query = query.squeeze(0)
    key = key.squeeze(0)
    value = value.squeeze(0)
    cu_seqlens = attention_mask

    with torch.no_grad():
        max_seqlen = max(
            [
                cu_seqlens[idx + 1] - cu_seqlens[idx]
                for idx in range(cu_seqlens.size(0) - 1)
            ]
        ).item()

    attn_output = flash_attn_varlen_func(
        query,
        key,
        value,
        cu_seqlens_q=cu_seqlens,
        cu_seqlens_k=cu_seqlens,
        max_seqlen_q=max_seqlen,
        max_seqlen_k=max_seqlen,
        causal=True,
    )

    attn_output = attn_output.unsqueeze(0)

    return attn_output, None


@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def qwen2vl_forward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[Cache] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    cache_position: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
    **kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
    bsz, q_len, _ = hidden_states.size()

    query_states = self.q_proj(hidden_states)
    key_states = self.k_proj(hidden_states)
    value_states = self.v_proj(hidden_states)

    query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
    key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
    value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

    cos, sin = position_embeddings
    query_states, key_states = apply_multimodal_rotary_pos_emb(
        query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
    )

    if past_key_values is not None:
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
        key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

    attn_output, attn_weights = flash_attention_forward(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        dropout=0.0 if not self.training else self.attention_dropout,
        scaling=self.scaling,
        sliding_window=self.sliding_window,
        position_ids=position_ids,  # pass positions for FA2
        **kwargs,
    )

    attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
    attn_output = self.o_proj(attn_output)
    return attn_output, attn_weights



@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def qwen3vl_forward(
    self,
    hidden_states: torch.Tensor,
    position_embeddings: tuple[torch.Tensor, torch.Tensor],
    attention_mask: Optional[torch.Tensor],
    past_key_values: Optional[Cache] = None,
    cache_position: Optional[torch.LongTensor] = None,
    **kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
    input_shape = hidden_states.shape[:-1]
    hidden_shape = (*input_shape, -1, self.head_dim)

    query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
    key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
    value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

    cos, sin = position_embeddings
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

    if past_key_values is not None:
        # sin and cos are specific to RoPE models; cache_position needed for the static cache
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
        key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

    attn_output, attn_weights = flash_attention_forward(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        dropout=0.0 if not self.training else self.attention_dropout,
        scaling=self.scaling,
        **kwargs,
    )

    attn_output = attn_output.reshape(*input_shape, -1).contiguous()
    attn_output = self.o_proj(attn_output)
    return attn_output, attn_weights


def return_mask(
    config,
    input_embeds,
    attention_mask,
    cache_position,
    past_key_values,
    position_ids,
    **kwargs
):
    return attention_mask


def replace_qwen2_vl_attention_class():
    import transformers
    import transformers.modeling_flash_attention_utils


    transformers.models.qwen2_vl.modeling_qwen2_vl.Qwen2VLAttention.forward = (
        qwen2vl_forward
    )
    transformers.models.qwen2_vl.modeling_qwen2_vl.create_causal_mask = (
        return_mask
    )
    transformers.models.qwen2_vl.modeling_qwen2_vl.create_sliding_window_causal_mask = (
        return_mask
    )    
    ## qwen2_5_vl
    transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLAttention.forward = (
        qwen2vl_forward
    )
    transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.create_causal_mask = (
        return_mask
    )
    transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.create_sliding_window_causal_mask = (
        return_mask
    )
    ## qwen3vl
    transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextAttention.forward = (
        qwen3vl_forward
    )
    transformers.models.qwen3_vl.modeling_qwen3_vl.create_causal_mask = (
        return_mask
    )
    ## qwen3vl moe
    transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe.Qwen3VLMoeTextAttention.forward = (
        qwen3vl_forward
    )
    transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe.create_causal_mask = (
        return_mask
    )


def print_trainable_parameters_visual(self) -> None:
    """
    Prints the trainable status of all vision components including attention blocks and merger module.
    Outputs the indices of trainable/non-trainable blocks and the merger module status.
    """
    trainable_blocks = []
    non_trainable_blocks = []

    # Check trainable status of vision attention blocks
    for block_idx, block in enumerate(self.blocks):
        is_trainable = all(param.requires_grad for param in block.parameters())
        if is_trainable:
            trainable_blocks.append(block_idx)
        else:
            non_trainable_blocks.append(block_idx)

    # Check trainable status of merger module
    is_merger_trainable = any(param.requires_grad for param in self.merger.parameters())

    # Print results
    print("Vision Module - Attention Blocks:")
    print(
        f"Trainable Block Indices: {trainable_blocks if trainable_blocks else 'None'}"
    )
    print(
        f"Non-Trainable Block Indices: {non_trainable_blocks if non_trainable_blocks else 'None'}"
    )
    print(f"Merger Module Trainable: {is_merger_trainable}")


def print_trainable_parameters(self) -> None:
    """
    Prints the trainable status of all LLM components including embeddings, layers, and normalization.
    Outputs the indices of trainable/non-trainable layers and other module statuses.
    """
    # Check embed_tokens
    is_embed_trainable = any(
        param.requires_grad for param in self.language_model.embed_tokens.parameters()
    )
    print(f"LLM Module - Embed Tokens Trainable: {is_embed_trainable}")

    # Check each decoder layer
    trainable_layers = []
    non_trainable_layers = []

    for layer_idx, layer in enumerate(self.language_model.layers):
        is_trainable = any(param.requires_grad for param in layer.parameters())
        if is_trainable:
            trainable_layers.append(layer_idx)
        else:
            non_trainable_layers.append(layer_idx)

    # Print layer status
    print(
        f"LLM Module - Trainable Layer Indices: {trainable_layers if trainable_layers else 'None'}"
    )
    print(
        f"LLM Module - Non-Trainable Layer Indices: {non_trainable_layers if non_trainable_layers else 'None'}"
    )


def create_optimizer(self):

    opt_model = self.model

    if self.optimizer is None:
        decay_parameters = self.get_decay_parameter_names(opt_model)
        decay_parameters = [name for name in decay_parameters if "bias" not in name]
        if self.args.mm_projector_lr is not None and self.args.mm_projector_lr != 0:
            projector_parameters = [
                name for name, _ in opt_model.named_parameters() if "merger" in name
            ]
            if self.args.vision_tower_lr is not None and self.args.vision_tower_lr != 0:
                vision_tower_parameters = [
                    name for name, _ in opt_model.named_parameters() if "visual" in name
                ]
                optimizer_grouped_parameters = [
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n in decay_parameters
                                and n not in projector_parameters
                                and n not in vision_tower_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": self.args.weight_decay,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n in decay_parameters
                                and n not in projector_parameters
                                and n in vision_tower_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": self.args.weight_decay,
                        "lr": self.args.vision_tower_lr,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n not in decay_parameters
                                and n not in projector_parameters
                                and n not in vision_tower_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": 0.0,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n not in decay_parameters
                                and n not in projector_parameters
                                and n in vision_tower_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": 0.0,
                        "lr": self.args.vision_tower_lr,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n in decay_parameters
                                and n in projector_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": self.args.weight_decay,
                        "lr": self.args.mm_projector_lr,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n not in decay_parameters
                                and n in projector_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": 0.0,
                        "lr": self.args.mm_projector_lr,
                    },
                ]
            else:
                optimizer_grouped_parameters = [
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n in decay_parameters
                                and n not in projector_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": self.args.weight_decay,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n not in decay_parameters
                                and n not in projector_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": 0.0,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n in decay_parameters
                                and n in projector_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": self.args.weight_decay,
                        "lr": self.args.mm_projector_lr,
                    },
                    {
                        "params": [
                            p
                            for n, p in opt_model.named_parameters()
                            if (
                                n not in decay_parameters
                                and n in projector_parameters
                                and p.requires_grad
                            )
                        ],
                        "weight_decay": 0.0,
                        "lr": self.args.mm_projector_lr,
                    },
                ]
        else:
            optimizer_grouped_parameters = [
                {
                    "params": [
                        p
                        for n, p in opt_model.named_parameters()
                        if (n in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [
                        p
                        for n, p in opt_model.named_parameters()
                        if (n not in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": 0.0,
                },
            ]

        optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
            self.args
        )
        self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)

    return self.optimizer


# Apply monkey patches
Trainer.create_optimizer = create_optimizer

Qwen2VisionTransformerPretrainedModel.print_trainable_parameters = (
    print_trainable_parameters_visual
)
Qwen2VLModel.print_trainable_parameters = print_trainable_parameters
Qwen2_5_VisionTransformerPretrainedModel.print_trainable_parameters = (
    print_trainable_parameters_visual
)
Qwen2_5_VLModel.print_trainable_parameters = print_trainable_parameters

Qwen3VLVisionModel.print_trainable_parameters = (
    print_trainable_parameters_visual
)
Qwen3VLModel.print_trainable_parameters = print_trainable_parameters
Qwen3VLMoeVisionModel.print_trainable_parameters = print_trainable_parameters_visual
Qwen3VLMoeModel.print_trainable_parameters = print_trainable_parameters

	from typing import Dict, List, Optional, Sequence, Tuple, Callable

	import torch
	from flash_attn.flash_attn_interface import flash_attn_varlen_func
	from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
	from transformers import Trainer
	from transformers.cache_utils import Cache
	from transformers.utils.deprecation import deprecate_kwarg
	from transformers.processing_utils import Unpack
	from transformers.models.qwen2_vl.modeling_qwen2_vl import (
	Qwen2VisionTransformerPretrainedModel,
	Qwen2VLModel,
	apply_multimodal_rotary_pos_emb,
	)
	from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
	Qwen2_5_VisionTransformerPretrainedModel,
	Qwen2_5_VLModel,
	)
	from transformers.models.qwen3_vl.modeling_qwen3_vl import (
	Qwen3VLVisionModel,
	Qwen3VLModel,
	apply_rotary_pos_emb,
	)
	from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import (
	Qwen3VLMoeVisionModel,
	Qwen3VLMoeModel,
	)
	from transformers.utils import logging

	logger = logging.get_logger(__name__)


	def flash_attention_forward(
	module: torch.nn.Module,
	query: torch.Tensor,
	key: torch.Tensor,
	value: torch.Tensor,
	attention_mask: Optional[torch.Tensor],
	dropout: float = 0.0,
	scaling: Optional[float] = None,
	sliding_window: Optional[int] = None,
	softcap: Optional[float] = None,
	**kwargs,
	) -> tuple[torch.Tensor, None]:
	if kwargs.get("output_attentions", False) or kwargs.get("head_mask") is not None:
	logger.warning_once(
	"`flash_attention_2` does not support `output_attentions=True` or `head_mask`."
	" Please set your attention to `eager` if you want any of these features."
	)

	# This is before the transpose
	seq_len = query.shape[2]

	if any(dim == 0 for dim in query.shape):
	raise ValueError(
	"Tensor query has shape with a zero dimension.\n"
	"FlashAttention does not support inputs with dim=0.\n"
	"Please check your input shapes or use SDPA instead."
	)
	# FA2 uses non-transposed inputs
	# batch, head, seq_len, dim
	query = query.transpose(1, 2)
	key = key.transpose(1, 2)
	value = value.transpose(1, 2)
	# batch, seqlen, head, dim

	# In PEFT, usually we cast the layer norms in float32 for training stability reasons
	# therefore the input hidden states gets silently casted in float32. Hence, we need
	# cast them back in the correct dtype just to be sure everything works as expected.
	# This might slowdown training & inference so it is recommended to not cast the LayerNorms
	# in fp32. (usually our RMSNorm modules handle it correctly)
	target_dtype = None
	if query.dtype == torch.float32:
	if torch.is_autocast_enabled():
	target_dtype = torch.get_autocast_gpu_dtype()
	# Handle the case where the model is quantized
	elif hasattr(module.config, "_pre_quantization_dtype"):
	target_dtype = module.config._pre_quantization_dtype
	else:
	target_dtype = next(layer for layer in module.modules() if isinstance(layer, torch.nn.Linear)).weight.dtype

	query = query.squeeze(0)
	key = key.squeeze(0)
	value = value.squeeze(0)
	cu_seqlens = attention_mask

	with torch.no_grad():
	max_seqlen = max(
	[
	cu_seqlens[idx + 1] - cu_seqlens[idx]
	for idx in range(cu_seqlens.size(0) - 1)
	]
	).item()

	attn_output = flash_attn_varlen_func(
	query,
	key,
	value,
	cu_seqlens_q=cu_seqlens,
	cu_seqlens_k=cu_seqlens,
	max_seqlen_q=max_seqlen,
	max_seqlen_k=max_seqlen,
	causal=True,
	)

	attn_output = attn_output.unsqueeze(0)

	return attn_output, None


	@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
	def qwen2vl_forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[Cache] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	cache_position: Optional[torch.LongTensor] = None,
	position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
	**kwargs: Unpack[FlashAttentionKwargs],
	) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()

	query_states = self.q_proj(hidden_states)
	key_states = self.k_proj(hidden_states)
	value_states = self.v_proj(hidden_states)

	query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
	key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
	value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

	cos, sin = position_embeddings
	query_states, key_states = apply_multimodal_rotary_pos_emb(
	query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
	)

	if past_key_values is not None:
	cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
	key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

	attn_output, attn_weights = flash_attention_forward(
	self,
	query_states,
	key_states,
	value_states,
	attention_mask,
	dropout=0.0 if not self.training else self.attention_dropout,
	scaling=self.scaling,
	sliding_window=self.sliding_window,
	position_ids=position_ids, # pass positions for FA2
	**kwargs,
	)

	attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
	attn_output = self.o_proj(attn_output)
	return attn_output, attn_weights



	@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
	def qwen3vl_forward(
	self,
	hidden_states: torch.Tensor,
	position_embeddings: tuple[torch.Tensor, torch.Tensor],
	attention_mask: Optional[torch.Tensor],
	past_key_values: Optional[Cache] = None,
	cache_position: Optional[torch.LongTensor] = None,
	**kwargs: Unpack[FlashAttentionKwargs],
	) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
	input_shape = hidden_states.shape[:-1]
	hidden_shape = (*input_shape, -1, self.head_dim)

	query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
	key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
	value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

	cos, sin = position_embeddings
	query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

	if past_key_values is not None:
	# sin and cos are specific to RoPE models; cache_position needed for the static cache
	cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
	key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

	attn_output, attn_weights = flash_attention_forward(
	self,
	query_states,
	key_states,
	value_states,
	attention_mask,
	dropout=0.0 if not self.training else self.attention_dropout,
	scaling=self.scaling,
	**kwargs,
	)

	attn_output = attn_output.reshape(*input_shape, -1).contiguous()
	attn_output = self.o_proj(attn_output)
	return attn_output, attn_weights


	def return_mask(
	config,
	input_embeds,
	attention_mask,
	cache_position,
	past_key_values,
	position_ids,
	**kwargs
	):
	return attention_mask


	def replace_qwen2_vl_attention_class():
	import transformers
	import transformers.modeling_flash_attention_utils


	transformers.models.qwen2_vl.modeling_qwen2_vl.Qwen2VLAttention.forward = (
	qwen2vl_forward
	)
	transformers.models.qwen2_vl.modeling_qwen2_vl.create_causal_mask = (
	return_mask
	)
	transformers.models.qwen2_vl.modeling_qwen2_vl.create_sliding_window_causal_mask = (
	return_mask
	)
	## qwen2_5_vl
	transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLAttention.forward = (
	qwen2vl_forward
	)
	transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.create_causal_mask = (
	return_mask
	)
	transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.create_sliding_window_causal_mask = (
	return_mask
	)
	## qwen3vl
	transformers.models.qwen3_vl.modeling_qwen3_vl.Qwen3VLTextAttention.forward = (
	qwen3vl_forward
	)
	transformers.models.qwen3_vl.modeling_qwen3_vl.create_causal_mask = (
	return_mask
	)
	## qwen3vl moe
	transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe.Qwen3VLMoeTextAttention.forward = (
	qwen3vl_forward
	)
	transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe.create_causal_mask = (
	return_mask
	)


	def print_trainable_parameters_visual(self) -> None:
	"""
	Prints the trainable status of all vision components including attention blocks and merger module.
	Outputs the indices of trainable/non-trainable blocks and the merger module status.
	"""
	trainable_blocks = []
	non_trainable_blocks = []

	# Check trainable status of vision attention blocks
	for block_idx, block in enumerate(self.blocks):
	is_trainable = all(param.requires_grad for param in block.parameters())
	if is_trainable:
	trainable_blocks.append(block_idx)
	else:
	non_trainable_blocks.append(block_idx)

	# Check trainable status of merger module
	is_merger_trainable = any(param.requires_grad for param in self.merger.parameters())

	# Print results
	print("Vision Module - Attention Blocks:")
	print(
	f"Trainable Block Indices: {trainable_blocks if trainable_blocks else 'None'}"
	)
	print(
	f"Non-Trainable Block Indices: {non_trainable_blocks if non_trainable_blocks else 'None'}"
	)
	print(f"Merger Module Trainable: {is_merger_trainable}")


	def print_trainable_parameters(self) -> None:
	"""
	Prints the trainable status of all LLM components including embeddings, layers, and normalization.
	Outputs the indices of trainable/non-trainable layers and other module statuses.
	"""
	# Check embed_tokens
	is_embed_trainable = any(
	param.requires_grad for param in self.language_model.embed_tokens.parameters()
	)
	print(f"LLM Module - Embed Tokens Trainable: {is_embed_trainable}")

	# Check each decoder layer
	trainable_layers = []
	non_trainable_layers = []

	for layer_idx, layer in enumerate(self.language_model.layers):
	is_trainable = any(param.requires_grad for param in layer.parameters())
	if is_trainable:
	trainable_layers.append(layer_idx)
	else:
	non_trainable_layers.append(layer_idx)

	# Print layer status
	print(
	f"LLM Module - Trainable Layer Indices: {trainable_layers if trainable_layers else 'None'}"
	)
	print(
	f"LLM Module - Non-Trainable Layer Indices: {non_trainable_layers if non_trainable_layers else 'None'}"
	)


	def create_optimizer(self):

	opt_model = self.model

	if self.optimizer is None:
	decay_parameters = self.get_decay_parameter_names(opt_model)
	decay_parameters = [name for name in decay_parameters if "bias" not in name]
	if self.args.mm_projector_lr is not None and self.args.mm_projector_lr != 0:
	projector_parameters = [
	name for name, _ in opt_model.named_parameters() if "merger" in name
	]
	if self.args.vision_tower_lr is not None and self.args.vision_tower_lr != 0:
	vision_tower_parameters = [
	name for name, _ in opt_model.named_parameters() if "visual" in name
	]
	optimizer_grouped_parameters = [
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n in decay_parameters
	and n not in projector_parameters
	and n not in vision_tower_parameters
	and p.requires_grad
	)
	],
	"weight_decay": self.args.weight_decay,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n in decay_parameters
	and n not in projector_parameters
	and n in vision_tower_parameters
	and p.requires_grad
	)
	],
	"weight_decay": self.args.weight_decay,
	"lr": self.args.vision_tower_lr,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n not in decay_parameters
	and n not in projector_parameters
	and n not in vision_tower_parameters
	and p.requires_grad
	)
	],
	"weight_decay": 0.0,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n not in decay_parameters
	and n not in projector_parameters
	and n in vision_tower_parameters
	and p.requires_grad
	)
	],
	"weight_decay": 0.0,
	"lr": self.args.vision_tower_lr,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n in decay_parameters
	and n in projector_parameters
	and p.requires_grad
	)
	],
	"weight_decay": self.args.weight_decay,
	"lr": self.args.mm_projector_lr,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n not in decay_parameters
	and n in projector_parameters
	and p.requires_grad
	)
	],
	"weight_decay": 0.0,
	"lr": self.args.mm_projector_lr,
	},
	]
	else:
	optimizer_grouped_parameters = [
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n in decay_parameters
	and n not in projector_parameters
	and p.requires_grad
	)
	],
	"weight_decay": self.args.weight_decay,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n not in decay_parameters
	and n not in projector_parameters
	and p.requires_grad
	)
	],
	"weight_decay": 0.0,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n in decay_parameters
	and n in projector_parameters
	and p.requires_grad
	)
	],
	"weight_decay": self.args.weight_decay,
	"lr": self.args.mm_projector_lr,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (
	n not in decay_parameters
	and n in projector_parameters
	and p.requires_grad
	)
	],
	"weight_decay": 0.0,
	"lr": self.args.mm_projector_lr,
	},
	]
	else:
	optimizer_grouped_parameters = [
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (n in decay_parameters and p.requires_grad)
	],
	"weight_decay": self.args.weight_decay,
	},
	{
	"params": [
	p
	for n, p in opt_model.named_parameters()
	if (n not in decay_parameters and p.requires_grad)
	],
	"weight_decay": 0.0,
	},
	]

	optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
	self.args
	)
	self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)

	return self.optimizer


	# Apply monkey patches
	Trainer.create_optimizer = create_optimizer

	Qwen2VisionTransformerPretrainedModel.print_trainable_parameters = (
	print_trainable_parameters_visual
	)
	Qwen2VLModel.print_trainable_parameters = print_trainable_parameters
	Qwen2_5_VisionTransformerPretrainedModel.print_trainable_parameters = (
	print_trainable_parameters_visual
	)
	Qwen2_5_VLModel.print_trainable_parameters = print_trainable_parameters

	Qwen3VLVisionModel.print_trainable_parameters = (
	print_trainable_parameters_visual
	)
	Qwen3VLModel.print_trainable_parameters = print_trainable_parameters
	Qwen3VLMoeVisionModel.print_trainable_parameters = print_trainable_parameters_visual
	Qwen3VLMoeModel.print_trainable_parameters = print_trainable_parameters