modeling_phi4mm.py

# coding=utf-8
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""" PyTorch Phi-4-MM model."""
import math
import warnings
from typing import List, Optional, Tuple, Union

import numpy as np

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import _flash_attention_forward
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig

# from .configuration_phi4mm import Phi4MMConfig
from .processing_phi4mm import InputMode
# from .vision_siglip_navit import get_siglip_vision_model
from .speech_conformer_encoder import ConformerEncoder


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "TBA"
_CONFIG_FOR_DOC = "Qwen2MMConfig"

# Special token ids
_IMAGE_SPECIAL_TOKEN_ID = 1516444  # '<|endoftext10|>', or we can better name it (in `tokenizer_config.json`)
_AUDIO_SPECIAL_TOKEN_ID = 151644  # '<|endoftext11|>'
_COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE = [-9999, -1]  # For backward compatibility
_COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE = [float('-inf'), -10000]  # For backward compatibility


# class Phi4MMImageEmbedding(nn.Module):
#     """Image embedding."""

#     def __init__(self, config: PretrainedConfig, **kwargs) -> None:
#         super().__init__()

#         # n_embed or hidden_size
#         hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size
#         if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'):
#             embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop
#             self.drop = nn.Dropout(embd_drop)
#         else:
#             self.drop = None

#         logger.info(f"create image tower {config.img_processor}")
#         enable_gradient_checkpointing = kwargs.get('enable_gradient_checkpointing', False)

#         # Load SigLIP model
#         self.img_processor = get_siglip_vision_model(
#             _flash_attn_2_enabled=config._attn_implementation == 'flash_attention_2'
#         )

#         pe_weight = self.img_processor.embeddings.position_embedding.weight
#         L, D = pe_weight.size()
#         H = int(math.sqrt(L))
#         assert H**2 == L
#         if H % 2 != 0: #and kwargs.get('image_token_compression_cls', None) is None:
#             self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1))
#             H += 1
#         image_dim_out = D
#         # ((448/14)//2)**2
#         self.num_img_tokens = (H//2)**2
#         self.base_feat_height_target = H

#         if enable_gradient_checkpointing:
#             self.img_processor.encoder.gradient_checkpointing = True

#         self.image_dim_out = image_dim_out
#         self.img_sizes = None
#         self.image_attention_mask = None

#         # global_gn and sub_gn for hd transform, serves as line separator
#         self.use_hd_transform = kwargs.get('use_hd_transform', False)
#         self.with_learnable_separator = kwargs.get('with_learnable_separator', False)
#         self.hd_transform_order = kwargs.get('hd_transform_order', 'glb_sub')
#         self.freeze_img_processor = kwargs.get('freeze_img_processor', False)
#         self.crop_size = kwargs.get('crop_size', 336)
#         logger.info(f'freeze_img_processor = {self.freeze_img_processor}')

#         # image token compression
#         self.image_token_compression_cls = kwargs.get('image_token_compression_cls', None)
#         if self.image_token_compression_cls == 'avg_pool_2d':
#             self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2)
#             self.base_feat_height_reduction = 1
#             self.base_feat_height_target = self.base_feat_height_target // 2
#         elif self.image_token_compression_cls is None:
#             self.image_token_compression = None
#             self.base_feat_height_reduction = 2
#         else:
#             raise NotImplementedError(f'image_token_compression_cls = {self.image_token_compression_cls}, not implemented')

#         # with_hd_transform and with_learnable_separator should have same value
#         assert self.use_hd_transform == self.with_learnable_separator, 'use_hd_transform and with_learnable_separator should have same value'
#         if self.with_learnable_separator:
#             assert self.use_hd_transform, 'learnable separator is only for hd transform'
#             # 1024 * 4, merge spatial to channel dimension
#             self.glb_GN = nn.Parameter(torch.zeros([1, 1, self.image_dim_out * self.base_feat_height_reduction**2]))
#             self.sub_GN = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out * self.base_feat_height_reduction**2]))
#             logger.info(f'learnable separator enabled for hd transform, hd_transform_order = {self.hd_transform_order}')

#         projection_cls = kwargs.get('projection_cls', 'linear')
#         if projection_cls == 'linear':
#             self.img_projection = nn.Linear(image_dim_out, hidden_size)
#         elif projection_cls == 'mlp' and self.use_hd_transform:
#             dim_projection = hidden_size
#             depth = 2
#             layers = [nn.Linear(image_dim_out * self.base_feat_height_reduction**2, dim_projection)]
#             for _ in range(1, depth):
#                 layers.extend([nn.GELU(),
#                                 nn.Linear(dim_projection, dim_projection)])
#             self.img_projection = nn.Sequential(*layers)
#         elif projection_cls == 'mlp':
#             # follow llava-v1.5's implementation
#             # (do not use image_projection and image_proj_norm)
#             dim_projection = hidden_size
#             depth = 2
#             layers = [nn.Linear(image_dim_out, dim_projection)]
#             for _ in range(1, depth):
#                 layers.extend([nn.GELU(),
#                                 nn.Linear(dim_projection, dim_projection)])
#             self.img_projection = nn.Sequential(*layers)
#         else:
#             raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented')

#         self.vocab_size = config.vocab_size
#         self.img_features = None

#         if isinstance(config.img_processor, dict):
#             self.layer_idx = config.img_processor.get('layer_idx', -2)
#             self.type_feature = config.img_processor.get('type_feature', 'patch')
#         else:
#             self.layer_idx = -2
#             self.type_feature = 'patch'

#     def set_img_features(self, img_features: torch.FloatTensor) -> None:
#         self.img_features = img_features

#     def set_img_sizes(self, img_sizes: torch.LongTensor) -> None:
#         self.img_sizes = img_sizes

#     def set_img_attn_mask(self, image_attention_mask: torch.FloatTensor) -> None:
#         self.image_attention_mask = image_attention_mask

#     def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor:
#         LAYER_IDX = self.layer_idx
#         TYPE_FEATURE = self.type_feature

#         if self.freeze_img_processor:
#             with torch.no_grad():
#                 if attention_mask is not None:
#                     img_processor_output = self.img_processor(img_embeds, output_hidden_states=True, patch_attention_mask=attention_mask)
#                 else:
#                     img_processor_output = self.img_processor(img_embeds, output_hidden_states=True)
#                 img_feature = img_processor_output.hidden_states[LAYER_IDX]
#         else:
#             if attention_mask is not None:
#                 img_processor_output = self.img_processor(img_embeds, output_hidden_states=True, patch_attention_mask=attention_mask)
#             else:
#                 img_processor_output = self.img_processor(img_embeds, output_hidden_states=True)
#             img_feature = img_processor_output.hidden_states[LAYER_IDX]

#         if TYPE_FEATURE == "patch":
#             patch_feature = img_feature
#             if self.image_token_compression is not None:
#                 # reshape to 2D tensor
#                 width = int(math.sqrt(patch_feature.size(1)))
#                 patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1))
#                 # convert to NCHW
#                 patch_feature = patch_feature.permute(0, 3, 1, 2)
#                 if getattr(self, 'img_processor_padding', None) is not None:
#                     patch_feature = self.img_processor_padding(patch_feature)
#                 patch_feature = self.image_token_compression(patch_feature)
#                 # convert to NHWC
#                 patch_feature = patch_feature.permute(0, 2, 3, 1)
#                 patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1))
#             elif getattr(self, 'img_processor_padding', None) is not None:
#                 width = int(math.sqrt(patch_feature.size(1)))
#                 patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1))
#                 # convert to NCHW
#                 patch_feature = patch_feature.permute(0, 3, 1, 2)
#                 patch_feature = self.img_processor_padding(patch_feature)
#                 # convert to NHWC
#                 patch_feature = patch_feature.permute(0, 2, 3, 1)
#                 patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1))
#             return patch_feature

#         if TYPE_FEATURE == "cls_patch":
#             if self.image_token_compression is not None:
#                 # reshape to 2D tensor
#                 patch_feature = img_feature[:, 1:]
#                 cls_feature = img_feature[:, 0]
#                 width = math.sqrt(patch_feature.size(1))
#                 patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1))
#                 patch_feature = self.image_token_compression(patch_feature)
#                 patch_feature = patch_feature.view(-1, patch_feature.size(-2) * patch_feature.size(-1))
#                 img_feature = torch.cat([cls_feature, patch_feature], dim=1)
#             return img_feature

#         logger.info(f'processed img feature size = {img_feature.size()}')
#         raise NotImplementedError

#     def spatiotemporal_pool(self, x, num_img_tokens, batch_size=1, T=1):

#         if self.image_pos_embed is not None:
#             x = x.view(batch_size * T, -1, x.shape[-1])
#             num_tokens = x.shape[-2]
#             h, w = int(num_tokens ** 0.5), int(num_tokens ** 0.5)
#             assert h * w == num_tokens, 'only support square feature maps for now'
#             x = x.view(batch_size * T, h, w, x.shape[-1])
#             pos_embed = self.image_pos_embed(x)
#             x = x + pos_embed
#             x = x.view(batch_size, T * h * w, x.shape[-1])

#         if self.visual_temporal_embed is not None:
#             visual_temporal_embed = self.visual_temporal_embed(x.view(batch_size, T, -1, x.shape[-1])[:, :, 0])
#             x = x.view(batch_size, T, -1, x.shape[-1]) + visual_temporal_embed.view(1, T, 1, x.shape[-1])

#         new_x = []
#         # [bsz, T * H' * W', C] -> [bsz, T, C]
#         spatial_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=2)
#         new_x.append(spatial_avg_pool_x)

#         # [bsz, T * H' * W', C] -> [bsz, H'*W', C]
#         temporal_avg_pool_x = x.view(batch_size, T, -1, x.shape[-1]).mean(dim=1)
#         new_x.append(temporal_avg_pool_x)

#         x = torch.cat(new_x, dim=1).view(-1, self.image_dim_out)
#         num_img_tokens += T
#         return x, num_img_tokens

#     def forward(self, input_ids: torch.LongTensor, input_embeds: torch.FloatTensor, image_sizes=None, **kwargs) -> torch.FloatTensor:

#         if isinstance(input_ids, tuple):
#             # # pipeline parallel
#             input_ids, input_embeds = input_ids

#         img_embeds = input_embeds
#         if image_sizes is None and 'image_sizes' in kwargs:
#             image_sizes = kwargs['image_sizes']
#         img_sizes = image_sizes

#         if self.img_features is not None:
#             img_embeds = self.img_features.clone()
#             self.img_features = None

#         if self.img_sizes is not None:
#             img_sizes = self.img_sizes

#         dtype = self.img_processor.embeddings.patch_embedding.weight.dtype
#         if img_embeds is not None:
#             # convert to bf16
#             img_embeds = img_embeds.to(dtype)

#         if self.image_attention_mask is not None:
#             image_attention_mask = self.image_attention_mask.clone()
#             self.image_attention_mask = None
#         elif 'image_attention_mask' in kwargs:
#             image_attention_mask = kwargs['image_attention_mask']
#         else:
#             image_attention_mask = None
#         input_shape = input_ids.size()
#         input_ids = input_ids.view(-1, input_shape[-1])

#         with torch.no_grad():
#             positions = torch.nonzero(input_ids == _IMAGE_SPECIAL_TOKEN_ID, as_tuple=False)
#             positions_tuple = torch.nonzero(input_ids == _IMAGE_SPECIAL_TOKEN_ID, as_tuple=True)

#         # logger.info(f'position size: {positions.size()} ...')
#         fake_image_forward = False
#         select = False
#         hd_transform = False

#         if isinstance(self.img_projection, nn.Sequential):
#             target_device = self.img_projection[0].bias.device
#             target_dtype = self.img_projection[0].bias.dtype
#         else:  # It's a single nn.Linear layer
#             target_device = self.img_projection.bias.device
#             target_dtype = self.img_projection.bias.dtype

#         num_img_tokens = self.num_img_tokens
#         if len(positions.tolist()) > 0:
#             if self.use_hd_transform and img_sizes is not None and len(img_sizes):
#                 hd_transform = True
#                 assert img_embeds.ndim == 5, f'(branch 1) img_embeds size: {img_embeds.size()}, expect 5D tensor for hd transform'
#                 # img_embeds: (num_images, max_num_crops, 3, H, W)
#                 # img_sizes: (num_images, 2).view(1, -1)

#                 bs = img_embeds.shape[0]
#                 # Nx(HW)xC
#                 if image_attention_mask is not None and len(image_attention_mask) > 0:
#                     img_features = self.get_img_features(img_embeds.flatten(0, 1), attention_mask=image_attention_mask.type(torch.BoolTensor).flatten(0,1).to(target_device))
#                 else:
#                     img_features = self.get_img_features(img_embeds.flatten(0, 1))

#                 base_feat_height_target = self.base_feat_height_target
#                 base_resolution = self.crop_size
#                 base_feat_height_reduction = self.base_feat_height_reduction

#                 base_feat_height = base_feat_width = int(np.sqrt(img_features.shape[1]))

#                 assert base_feat_height == base_feat_height_target and base_feat_width == base_feat_height_target, f'base_feat_height: {base_feat_height}, base_feat_width: {base_feat_width}, expect {base_feat_height_target} features for hd transform'

#                 # bs x max_num_crops x (24x24) x C
#                 img_features = img_features.view(bs, -1, base_feat_height * base_feat_width, self.image_dim_out)
#                 C = self.image_dim_out
#                 H = base_feat_height

#                 output_imgs = []
#                 output_len = []
#                 # training is tensor, inference is list
#                 if isinstance(img_sizes, torch.Tensor):
#                     img_sizes = img_sizes.view(-1, 2)
#                 for _bs in range(bs):
#                     h, w = img_sizes[_bs]
#                     h = h // base_resolution
#                     w = w // base_resolution
#                     B_ = h * w

#                     # 1 x (24x24) x 1024
#                     global_img_feature = img_features[_bs, :1]

#                     # 1 x 12 x 12 x 4096
#                     glb_img = global_img_feature.reshape(1,H,H,C).reshape(1,H//base_feat_height_reduction,base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction,C).contiguous().permute(0,1,3,2,4,5).reshape(1,H//base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction*base_feat_height_reduction*C).contiguous()
#                     temp_glb_GN = self.sub_GN.repeat(1, H//base_feat_height_reduction, 1, 1)

#                     # 1 x 156 x 4096
#                     glb_img = torch.cat([glb_img, temp_glb_GN], dim=2).reshape(1,-1,base_feat_height_reduction*base_feat_height_reduction*C)

#                     # (max_num_crops-1) x (12x12) x C
#                     sub_img = img_features[_bs, 1:]
#                     # 16x574x1024
#                     # get rid of padding sub_img
#                     sub_img = sub_img[:B_]

#                     # (num_crops, 12, 2, 12, 2, 1024) -> (num_crops, 12, 12, 2, 2, 1024) -> (num_crops, 12*12, 4*1024)
#                     sub_img = sub_img.reshape(B_,H,H,C).reshape(B_,H//base_feat_height_reduction,base_feat_height_reduction,H//base_feat_height_reduction,base_feat_height_reduction,C).contiguous().permute(0,1,3,2,4,5).reshape(B_,-1,base_feat_height_reduction*base_feat_height_reduction*C).contiguous()
#                     sub_img = sub_img.reshape(1, h, w, base_feat_height // base_feat_height_reduction, base_feat_width // base_feat_height_reduction, -1).permute(0,1,3,2,4,5).reshape(1,h*base_feat_height//base_feat_height_reduction,w*base_feat_width//base_feat_height_reduction,base_feat_height_reduction*base_feat_height_reduction*C)

#                     if image_attention_mask is not None and len(image_attention_mask) > 0:
#                         reshaped_image_attention_mask = image_attention_mask[_bs,1:B_+1,0::2,0::2].reshape(1, h, w, base_feat_height // base_feat_height_reduction, base_feat_width // base_feat_height_reduction).permute(0,1,3,2,4).reshape(1,h*base_feat_height//base_feat_height_reduction,w*base_feat_width//base_feat_height_reduction)
#                         useful_height = int(reshaped_image_attention_mask[0,:,0].sum().item())
#                         useful_width = int(reshaped_image_attention_mask[0,0,:].sum().item())
#                         sub_img = sub_img[:,:useful_height, :useful_width]
#                         temp_sub_GN = self.sub_GN.repeat(1, useful_height, 1, 1)
#                         temp_len = int(image_attention_mask[_bs,:B_+1,0::2,0::2].sum().item()) + (useful_height+1) + base_feat_height//base_feat_height_reduction
#                     else:
#                         temp_sub_GN = self.sub_GN.repeat(1, h*base_feat_height//base_feat_height_reduction, 1, 1)
#                         temp_len = int((h*w+1)*self.num_img_tokens+ 1 + (h+1)*base_feat_height//base_feat_height_reduction)

#                     sub_img = torch.cat([sub_img, temp_sub_GN], dim=2).reshape(1,-1,base_feat_height_reduction*base_feat_height_reduction*C)
#                     # (1, num_img_tokens, 1024*4)

#                     # glb + sub
#                     if self.hd_transform_order == 'glb_sub':
#                         output_imgs.append(torch.cat([glb_img, self.glb_GN, sub_img], dim=1))
#                     elif self.hd_transform_order == 'sub_glb':
#                         output_imgs.append(torch.cat([sub_img, self.glb_GN, glb_img], dim=1))
#                     else:
#                         raise NotImplementedError(f'hd_transform_order = {self.hd_transform_order}, not implemented')

#                     #temp_len = int((h*w+1)*144 + 1 + (h+1)*12)
#                     assert temp_len == output_imgs[-1].shape[1], f'temp_len: {temp_len}, output_imgs[-1].shape[1]: {output_imgs[-1].shape[1]}'
#                     output_len.append(temp_len)

#                 num_img_tokens = output_len
#                 img_set_tensor = []
#                 for _output_img in output_imgs:
#                     img_feature_proj = self.img_projection(_output_img.to(target_device).to(target_dtype))
#                     img_set_tensor.append(img_feature_proj)
#                 #logger.info(f'img_embeds size: {img_embeds.size()}, image sizes: {img_sizes} loading time {datetime.now() - start_time}')
#                 #assert sum(num_img_tokens) == len(g_values), f'(branch 1) sum(num_img_tokens): {sum(num_img_tokens)}, g_values size: {len(g_values)}, g_values {g_values}'

#             else:
#                 raise NotImplementedError
#             select = True
#         else:
#             # # create a fake image tensor
#             # # TODO: need define image size for different vision model
#             if self.training:
#                 img_embeds = torch.zeros(1, 3, self.crop_size, self.crop_size, dtype=target_dtype, device=input_ids.device)

#                 tt = (
#                     self.get_img_features(img_embeds)
#                     .to(target_device)
#                     .to(target_dtype)
#                     .reshape(-1, 1024)
#                 )
#                 if self.use_hd_transform:
#                     img_set_tensor = self.img_projection(tt.reshape(-1, self.image_dim_out*self.base_feat_height_reduction**2) * self.glb_GN[0] * self.sub_GN[0, 0])
#                 else:
#                     img_set_tensor = self.img_projection(tt)  # adapted visual features.
#                 fake_image_forward = True

#         # we use the token embedding layer from the huggingface model, this is REQUIRED to make sure we are using the loaded weights.
#         hidden_states = kwargs['wte'](input_ids)

#         if select:
#             if hd_transform:
#                 # new implementation without in-place operation
#                 # Ref: https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py#L233
#                 # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put.html
#                 # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html#torch.Tensor.index_put_
#                 # img_set_tensor: a list of tensors, each tensor has shape (1, N_tokens, C)
#                 assert all([_img_set_tensor.shape[0] == 1 for _img_set_tensor in img_set_tensor]), 'img_set_tensor should have shape (1, N_tokens, C)'
#                 # Shape: (merged_N_tokens, C)
#                 merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0)
#                 merged_img_set_tensor = merged_img_set_tensor.to(hidden_states.dtype).to(hidden_states.device)
#                 # Temporarily disable autocast to avoid issue on bf16 tensors
#                 # Ref: https://github.com/pytorch/pytorch/issues/132715
#                 with torch.autocast(device_type=hidden_states.device.type, enabled=False):
#                     new_hidden_states = hidden_states.index_put(
#                         indices=positions_tuple,
#                         values=merged_img_set_tensor,
#                         accumulate=False
#                     )
#                 hidden_states = new_hidden_states
#             else:
#                 raise NotImplementedError

#         if fake_image_forward and self.training:
#             hidden_states = hidden_states + (0 * img_set_tensor[0].to(hidden_states.dtype).to(hidden_states.device)).sum()

#         if self.drop is not None:
#             hidden_states = self.drop(hidden_states)

#         return hidden_states


class Phi4MMAudioEmbedding(nn.Module):
    """Audio embedding."""

    def __init__(self, config: PretrainedConfig, **kwargs) -> None:
        super().__init__()
        self.config = config
        # n_embed or hidden_size for text LM
        hidden_size = config.n_embd if hasattr(config, 'n_embd') else config.hidden_size

        if hasattr(config, 'embd_pdrop') or hasattr(config, 'embed_pdrop'):
            embd_drop = config.embd_pdrop if hasattr(config, 'embd_pdrop') else config.embed_pdrop
            self.drop = nn.Dropout(embd_drop)
        else:
            self.drop = None

        audio_dim_out = None # Set this variable according to the actual audio processor
        logger.info(f"create audio processor {config.audio_processor}")
        self.layer_idx = -2

        if isinstance(config.audio_processor, dict) and config.audio_processor.get('name', None) == "cascades":
            encoder_config = config.audio_processor.get("config", None)
            assert encoder_config is not None
            self.encoder = ConformerEncoder(**encoder_config)

            # fake initialization, create encoder_embedding layer only so that
            # in decoding, all parameters can be loaded in from_pretrained_function
            # in training, we do post init after from_pretrained function to make sure the correct initialization
            self.encoder.post_init({})

            audio_dim_out = encoder_config["attention_dim"]
            n_mels = encoder_config["input_size"]
        else:
            raise NotImplementedError

        assert audio_dim_out is not None, "Remember to set values for audio_dim_out"
        self.audio_dim_out = audio_dim_out
        self.audio_dim_in = n_mels

        self.freeze_audio_processor = kwargs.get('freeze_audio_processor', False)
        logger.info(f'freeze_audio_processor = {self.freeze_audio_processor}')

        self.downsample_rate = kwargs.get('downsample_rate', 1)

        enable_gradient_checkpointing = kwargs.get('enable_gradient_checkpointing', False)
        if enable_gradient_checkpointing:
            self.encoder.gradient_checkpointing_enable()
            logger.info(f'gradient checkpointing enabled for audio processor')

        projection_cls = kwargs.get('projection_cls', 'linear')
        if projection_cls == 'linear':
            self.audio_projection = nn.Linear(audio_dim_out, hidden_size)
        elif projection_cls == 'mlp':
            # follow llava-v1.5's implementation
            # (do not use image_projection and image_proj_norm)
            dim_projection = hidden_size
            depth = 2
            self.linear_downsample_rate = self.downsample_rate

            layers_for_speech = [nn.Linear(audio_dim_out * self.linear_downsample_rate, dim_projection)]
            for _ in range(1, depth):
                layers_for_speech.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)])
            audio_projection_for_speech = nn.Sequential(*layers_for_speech)

            layers_for_vision = [nn.Linear(audio_dim_out * self.linear_downsample_rate, dim_projection)]
            for _ in range(1, depth):
                layers_for_vision.extend([nn.GELU(), nn.Linear(dim_projection, dim_projection)])
            # audio_projection_for_vision = nn.Sequential(*layers_for_vision)

            self.audio_projection = nn.ModuleDict({
                'speech': audio_projection_for_speech #,
                # 'vision': audio_projection_for_vision
            })
        else:
            raise NotImplementedError(f'projection_cls = {projection_cls}, not implemented')

        self.vocab_size = config.vocab_size
        self.input_embeds = None
        self.audio_embed_sizes = None

    def post_init(self, audio_config):
        # execute after the from_pretrained() initialization of the phi4mm model
        if audio_config.get('name', None) == "cascades":
            init_model_config = audio_config.get("init_model", {})
            self.encoder.post_init(init_model_config)
            # remove the init model in config so it is not saved in the config.
            # This might affect the model loading in resuming training and decoding.
            if "init_model" in audio_config:
                audio_config.pop("init_model")

    def set_audio_embeds(self, input_embeds: torch.FloatTensor) -> None:
        self.input_embeds = input_embeds

    def set_audio_embed_sizes(self, audio_embed_sizes: torch.LongTensor) -> None:
        self.audio_embed_sizes = audio_embed_sizes

    def get_audio_features(self, input_embeds: torch.FloatTensor, audio_attention_mask: torch.Tensor, audio_projection_mode: str='speech'):

        if self.freeze_audio_processor:
            with torch.no_grad():
                audio_features, masks = self.encoder(input_embeds, audio_attention_mask)
        else:
            audio_features, masks = self.encoder(input_embeds, audio_attention_mask)

        if isinstance(self.audio_projection, nn.Sequential):
            audio_set_tensor = self.audio_projection(audio_features)
        elif isinstance(self.audio_projection, nn.ModuleDict):
            audio_set_tensor = self.audio_projection[audio_projection_mode](audio_features)
        else:
            raise NotImplementedError

        return audio_set_tensor

    def forward(self, input_ids: torch.LongTensor, input_embeds: torch.FloatTensor, audio_embed_sizes=None, audio_attention_mask=None, audio_projection_mode='speech', **kwargs) -> torch.FloatTensor:
        '''
        arguments:
            input_ids: input text ids (B, U)
            input_embeds: audio features (B, T, D)  B: num audios in a sequence
        '''
        
        if self.input_embeds is not None:
            input_embeds = self.input_embeds.clone()
        if self.audio_embed_sizes is not None:
            audio_embed_sizes = self.audio_embed_sizes.clone()

        input_shape = input_ids.size()
        input_ids = input_ids.view(-1, input_shape[-1])
        MAX_INPUT_ID = int(1e9)

        with torch.no_grad():
            positions = torch.nonzero(input_ids == _AUDIO_SPECIAL_TOKEN_ID, as_tuple=False)
            positions_tuple = torch.nonzero(input_ids == _AUDIO_SPECIAL_TOKEN_ID, as_tuple=True)

        if isinstance(self.audio_projection, nn.Sequential):
            target_device = self.audio_projection[0].bias.device
            target_dtype = self.audio_projection[0].bias.dtype
        elif isinstance(self.audio_projection, nn.ModuleDict):
            target_device = self.audio_projection[audio_projection_mode][0].bias.device
            target_dtype = self.audio_projection[audio_projection_mode][0].bias.dtype
        else:  # It's a single nn.Linear layer
            target_device = self.audio_projection.bias.device
            target_dtype = self.audio_projection.bias.dtype

        if input_embeds is not None:
            input_embeds = input_embeds.to(target_device).to(target_dtype)
            
        
        
        if len(positions.tolist()) > 0:
            audio_set_tensor = self.get_audio_features(input_embeds, audio_attention_mask, audio_projection_mode)
        else:
            # # create an audio tensor
            # To do: not sure if this is required for text only input
            if self.training:
                audio_embeds = torch.zeros(1, 500, self.audio_dim_in).to(target_device).to(target_dtype)
                audio_attention_mask = audio_embeds.new_ones(audio_embeds.size()[:2]).long()
                audio_set_tensor = self.get_audio_features(audio_embeds, audio_attention_mask, audio_projection_mode)
        
        # print(kwargs['wte'])
        # print(input_ids)
        # print(kwargs['wte'](input_ids))
        # print(audio_embed_sizes)
        # print(len(positions.tolist()))
        # print(audio_set_tensor)
        # print(pppp)
        
        hidden_states = kwargs['wte'](input_ids)
        
        
        if len(positions.tolist()) > 0:

            assert audio_embed_sizes.sum().item() == len(positions), \
                f"please ensure the encoder outputs have the same length as defined in input_ids! \n audio_embed_sizes.sum().item(): {audio_embed_sizes.sum().item()} \n len(positions): {len(positions)} \n audio_embed_sizes: {audio_embed_sizes} \n positions: {positions} \n input_ids.shape \n {input_ids.shape}"

            # new implementation without in-place operation
            # Ref: https://huggingface.co/microsoft/Phi-3.5-vision-instruct/blob/4a0d683eba9f1d0cbfb6151705d1ee73c25a80ca/modeling_phi3_v.py#L233
            # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put.html
            # Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.index_put_.html#torch.Tensor.index_put_
            # audio_set_tensor: shape (N_audios, N_padded_tokens, C)
            # Shape: (merged_N_tokens, C)
            

                 
            merged_audio_set_tensor = torch.cat([
                audio_set_tensor[i, :audio_embed_sizes[i], :]
                for i in range(len(audio_embed_sizes))
            ], dim=0)
            merged_audio_set_tensor = merged_audio_set_tensor.to(hidden_states.dtype).to(hidden_states.device)
            # Temporarily disable autocast to avoid issue on bf16 tensors
            # Ref: https://github.com/pytorch/pytorch/issues/132715
            with torch.autocast(device_type=hidden_states.device.type, enabled=False):
                new_hidden_states = hidden_states.index_put(
                    indices=positions_tuple,
                    values=merged_audio_set_tensor,
                    accumulate=False
                )
            hidden_states = new_hidden_states
        else:
            if self.training:
                hidden_states  = hidden_states + (0 * audio_set_tensor[:,0].to(hidden_states.dtype).to(hidden_states.device)).sum()

        if self.drop is not None:
            hidden_states = self.drop(hidden_states)

        return hidden_states



class Phi4MMImageAudioEmbedding(nn.Module):
    """Image-audio embedding."""

    def __init__(self, config: PretrainedConfig, **kwargs) -> None:
        super().__init__()

        self.vocab_size = config.vocab_size

        # self.image_input_id = kwargs.get('image_input_id', -1)
        self.audio_input_id = kwargs.get('audio_input_id', -10000)
        # assert self.image_input_id != self.audio_input_id, 'image_input_id and audio_input_id should be different'

        
        # self.image_embd_layer_kwargs = kwargs['image_embd_layer']
        # self.image_embed = Phi4MMImageEmbedding(config, **self.image_embd_layer_kwargs)
        self.audio_embd_layer_kwargs = kwargs['audio_embd_layer']
        self.audio_embed = Phi4MMAudioEmbedding(config, **self.audio_embd_layer_kwargs)

        # self.input_image_embeds = None
        # self.image_sizes = None
        # self.image_attention_mask = None
        self.input_audio_embeds = None
        self.audio_embed_sizes = None

    def post_init(self, audio_config):
        # post init for audio embedding
        # ref: model.model.embed_tokens_extend.post_init(audio_config) in phyagi/getters/model.py
        self.audio_embed.post_init(audio_config)

    # def set_input_image_embeds(self, input_image_embeds: torch.FloatTensor) -> None:
    #     self.input_image_embeds = input_image_embeds

    # def set_image_sizes(self, image_sizes: torch.LongTensor) -> None:
    #     self.image_sizes = image_sizes

    # def set_img_attn_mask(self, image_attention_mask: torch.FloatTensor) -> None:
    #     self.image_attention_mask = image_attention_mask

    def set_input_audio_embeds(self, input_audio_embeds: torch.FloatTensor) -> None:
        self.input_audio_embeds = input_audio_embeds

    def set_audio_embed_sizes(self, audio_embed_sizes: torch.LongTensor) -> None:
        self.audio_embed_sizes = audio_embed_sizes

    def forward(
        self,
        input_ids: torch.LongTensor,
        input_embeds,
        input_image_embeds: Optional[torch.FloatTensor]=None,
        input_audio_embeds: Optional[torch.FloatTensor]=None,
        image_sizes=None,
        image_attention_mask=None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        audio_projection_mode='speech',
        wte=None,
    ) -> torch.FloatTensor:
        
        MAX_INPUT_ID = int(1e9)
        assert -MAX_INPUT_ID < self.audio_input_id #< self.image_input_id

        # override image and audio embeddings and sizes from object itself
        # this is for inference
        # ref: phyagi/eval/utils/text_generation_vision_audio_pipeline.py
        # if self.input_image_embeds is not None:
        #     assert input_image_embeds is None
        #     input_image_embeds = self.input_image_embeds.clone()
        #     # NOTE weijian: set input_image_embeds to None after first call in for eval stage
        #     #               during evaluation, it will call model's forward() multiple times
        #     #               the first time input_ids contains the prompt (including <|image_{}|>) and input_embeds exists
        #     #               from the second time, the input_ids will only contain the generated text
        #     #               thus, the input_image_embeds is no longer needed
        #     self.input_image_embeds = None

        # if self.image_sizes is not None:
        #     assert image_sizes is None
        #     image_sizes = self.image_sizes

        if self.input_audio_embeds is not None:
            assert input_audio_embeds is None
            input_audio_embeds = self.input_audio_embeds.clone()
            self.input_audio_embeds = None

        if self.audio_embed_sizes is not None:
            assert audio_embed_sizes is None
            audio_embed_sizes = self.audio_embed_sizes.clone()

        # if self.image_attention_mask is not None:
        #     assert image_attention_mask is None
        #     image_attention_mask = self.image_attention_mask.clone()
        #     self.image_attention_mask = None

        input_shape = input_ids.size()
        input_ids = input_ids.view(-1, input_shape[-1])

        # backward compatibility
        with torch.no_grad():
            new_input_ids = input_ids.clone()
            # new_input_ids[(input_ids >= _COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE[0]) &
            #             (input_ids <= _COMPATIBLE_IMAGE_SPECIAL_TOKEN_ID_RANGE[1])] = _IMAGE_SPECIAL_TOKEN_ID
            new_input_ids[(input_ids >= _COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE[0]) &
                        (input_ids <= _COMPATIBLE_AUDIO_SPECIAL_TOKEN_ID_RANGE[1])] = _AUDIO_SPECIAL_TOKEN_ID
            input_ids = new_input_ids

        # with torch.no_grad():
        #     image_position_mask = input_ids == _IMAGE_SPECIAL_TOKEN_ID
        #     non_image_position_mask = ~image_position_mask

        assert input_embeds is None
        # if self.training:
        #     assert input_image_embeds is not None or input_audio_embeds is not None
        if self.training:
            assert input_audio_embeds is not None

        # if input_image_embeds is not None:
        #     image_hidden_states = self.image_embed(
        #         input_ids=input_ids,
        #         input_embeds=input_image_embeds,
        #         image_sizes=image_sizes,
        #         wte=wte,
        #         image_attention_mask=image_attention_mask
        #     )
        
        
        if input_audio_embeds is not None:
            audio_hidden_states = self.audio_embed(
                input_ids=input_ids,
                input_embeds=input_audio_embeds,
                audio_embed_sizes=audio_embed_sizes,
                audio_attention_mask=audio_attention_mask,
                wte=wte,
                audio_projection_mode=audio_projection_mode,
            )

        # merge image and audio hidden states
        # NOTE weijian: for non-image-audio tokens, here we use audio hidden states
        #               actually, in the debug code above, the non-image-audio tokens from image_hidden_states and audio_hidden_states should be the same
        # if input_image_embeds is not None and input_audio_embeds is not None:
        #     dtype = image_hidden_states.dtype
        #     hidden_states = image_hidden_states * image_position_mask.to(dtype).unsqueeze(-1) + audio_hidden_states * non_image_position_mask.to(dtype).unsqueeze(-1)
        # elif input_image_embeds is not None:
        #     hidden_states = image_hidden_states
        # elif input_audio_embeds is not None:
        if input_audio_embeds is not None:
            hidden_states = audio_hidden_states
        else:
            assert wte is not None
            hidden_states = wte(input_ids)

        return hidden_states


########################################################################################################################

#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/qwen2/modular_qwen2.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_qwen2.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from typing import Callable, List, Optional, Tuple, Union

import torch
from torch import nn

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
)
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import (
    LossKwargs,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from transformers.utils.deprecation import deprecate_kwarg
from .configuration_qwen2mm import Qwen2MMConfig


####################################################################


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "meta-qwen2/Qwen2-2-7b-hf"
_CONFIG_FOR_DOC = "Qwen2MMConfig"


class Qwen2MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class Qwen2Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Qwen2MMConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True
        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = 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_value 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_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        sliding_window = None
        if (
            self.config.use_sliding_window
            and getattr(self.config, "sliding_window", None) is not None
            and self.layer_idx >= self.config.max_window_layers
        ):
            sliding_window = self.config.sliding_window

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
                logger.warning_once(
                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
                )
            else:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            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=sliding_window,  # main diff with Llama
            **kwargs,
        )

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


class Qwen2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        Qwen2RMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class Qwen2DecoderLayer(nn.Module):
    def __init__(self, config: Qwen2MMConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx)
        self.mlp = Qwen2MLP(config)
        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        if config.sliding_window and config._attn_implementation != "flash_attention_2":
            logger.warning_once(
                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
                "unexpected results may be encountered."
            )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[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.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)
        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs


class Qwen2RotaryEmbedding(nn.Module):
    def __init__(self, config: Qwen2MMConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            self.original_inv_freq = self.original_inv_freq.to(device)
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


QWEN2_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`Qwen2MMConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
    QWEN2_START_DOCSTRING,
)
class Qwen2PreTrainedModel(PreTrainedModel):
    config_class = Qwen2MMConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Qwen2DecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True
    _supports_attention_backend = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


QWEN2_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

            Two formats are allowed:
            - a [`~cache_utils.Cache`] instance, see our
            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
            cache format.

            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
            the complete sequence length.
"""


@add_start_docstrings(
    "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
    QWEN2_START_DOCSTRING,
)
class Qwen2MMModel(Qwen2PreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]

    Args:
        config: Qwen2MMConfig
    """

    def __init__(self, config: Qwen2MMConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        
######QWEN#################
        self.embed_tokens_extend = None
        if isinstance(config.embd_layer, dict):
            embedding_config = {
                'embedding_cls': config.embd_layer['embedding_cls'],
                **config.embd_layer
            }
            self.embed_tokens_extend = Phi4MMImageAudioEmbedding(config, **embedding_config)
        self._attn_implementation = config._attn_implementation
############################
        
        self.layers = nn.ModuleList(
            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
        self.gradient_checkpointing = False

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

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
    def forward(
        # self,
        # input_ids: torch.LongTensor = None,
        # attention_mask: Optional[torch.Tensor] = None,
        # position_ids: Optional[torch.LongTensor] = None,
        # past_key_values: Optional[Cache] = None,
        # inputs_embeds: Optional[torch.FloatTensor] = None,
        # use_cache: Optional[bool] = None,
        # output_attentions: Optional[bool] = None,
        # output_hidden_states: Optional[bool] = None,
        # return_dict: Optional[bool] = None,
        # cache_position: Optional[torch.LongTensor] = None,
########QWEN############
        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,
        input_image_embeds: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[torch.LongTensor] = None,
        image_attention_mask=None,
        input_audio_embeds: Optional[torch.FloatTensor] = None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        audio_projection_mode=None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
##########################
        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        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
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False

        # if inputs_embeds is None:
        #     inputs_embeds = self.embed_tokens(input_ids)

############QWEN###########
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens_extend(
                input_ids=input_ids,
                input_embeds=inputs_embeds,
                input_image_embeds=input_image_embeds,
                input_audio_embeds=input_audio_embeds,
                image_sizes=image_sizes,
                image_attention_mask=image_attention_mask,
                audio_embed_sizes=audio_embed_sizes,
                audio_attention_mask=audio_attention_mask,
                audio_projection_mode=audio_projection_mode,
                wte=self.embed_tokens,
            )
###########################
        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                    **flash_attn_kwargs,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        output = BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )
        return output if return_dict else output.to_tuple()

    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and past_key_values is not None:
                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
                if is_padding_right:
                    raise ValueError(
                        "You are attempting to perform batched generation with padding_side='right'"
                        " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                    )
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)
        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if (
            self.config._attn_implementation == "sdpa"
            and not (using_static_cache or using_sliding_window_cache)
            and not output_attentions
        ):
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                sliding_window=self.config.sliding_window,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        # SlidingWindowCache or StaticCache
        if using_sliding_window_cache or using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        # DynamicCache or no cache
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
            config=self.config,
            past_key_values=past_key_values,
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type in ["cuda", "xpu"]
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        config: Qwen2MMConfig,
        past_key_values: Cache,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            device (`torch.device`):
                The device to plcae the 4D attention mask on.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
            config (`Qwen2MMConfig`):
                The model's configuration class
            past_key_values (`Cache`):
                The cache class that is being used currently to generate
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            if config.sliding_window is not None:
                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
                # the check is needed to verify is current checkpoint was trained with sliding window or not
                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
                    sliding_attend_mask = torch.arange(target_length, device=device) <= (
                        cache_position.reshape(-1, 1) - config.sliding_window
                    )
                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
            causal_mask *= diagonal_attend_mask
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                if attention_mask.shape[-1] > target_length:
                    attention_mask = attention_mask[:, :target_length]
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )
        return causal_mask


class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...


class Qwen2MMForCausalLM(Qwen2PreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = Qwen2MMModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
    @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        # self,
        # input_ids: torch.LongTensor = None,
        # attention_mask: Optional[torch.Tensor] = None,
        # position_ids: Optional[torch.LongTensor] = None,
        # past_key_values: Optional[Union[Cache, 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,
        # return_dict: Optional[bool] = None,
        # cache_position: Optional[torch.LongTensor] = None,
        # logits_to_keep: Union[int, torch.Tensor] = 0,
    ######QWEN###############
        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,
        input_image_embeds: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[torch.LongTensor] = None,
        image_attention_mask=None,
        input_audio_embeds: Optional[torch.FloatTensor] = None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        input_mode=None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        num_logits_to_keep: int = 0,
 ####################################
        
        **kwargs: Unpack[KwargsForCausalLM],
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (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]`.

            num_logits_to_keep (`int` or `torch.Tensor`, *optional*):
                If an `int`, compute logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
                This is useful when using packed tensor format (single dimension for batch and sequence length).

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, Qwen2ForCausalLM

        >>> model = Qwen2ForCausalLM.from_pretrained("meta-qwen2/Qwen2-2-7b-hf")
        >>> tokenizer = AutoTokenizer.from_pretrained("meta-qwen2/Qwen2-2-7b-hf")

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""
        
        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

###########QWEN##########
        if isinstance(input_mode, torch.Tensor):
            # len(input_mode) == num_beams in beam search, and all elements of input_mode should have the same value
            input_mode = input_mode[0].item()
        input_mode = InputMode(input_mode)

        if input_mode in [InputMode.VISION_SPEECH, InputMode.VISION]:
            # self.set_lora_adapter('vision')
            audio_projection_mode = 'vision'
        elif input_mode == InputMode.SPEECH:
            # self.set_lora_adapter('speech')
            audio_projection_mode = 'speech'
        elif input_mode == InputMode.LANGUAGE:
            # self.unset_lora_adapter()
            audio_projection_mode = 'speech'
        else:
            raise ValueError(f"Invalid input_mode: {input_mode}")

##################################

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            input_image_embeds=input_image_embeds,
            image_sizes=image_sizes,
            image_attention_mask=image_attention_mask,
            input_audio_embeds=input_audio_embeds,
            audio_embed_sizes=audio_embed_sizes,
            audio_attention_mask=audio_attention_mask,
            audio_projection_mode=audio_projection_mode,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            **kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-num_logits_to_keep, None) if isinstance(num_logits_to_keep, int) else num_logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
        
    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        input_image_embeds=None,
        image_sizes=None,
        image_attention_mask=None,
        input_audio_embeds=None,
        audio_embed_sizes=None,
        audio_attention_mask=None,
        input_mode=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        num_logits_to_keep=None,
        **kwargs
    ):
        # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the
        # process

        # When the first time input length reached long and short factor switching point, enforce re-compute cache
        # It will cause downside of slower at this single token position, however, better than current failure.
        if (
            past_key_values
            and self.config.rope_scaling
            and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1
        ):
            past_length = cache_position[0]
            if past_length <= self.config.original_max_position_embeddings:
                past_key_values = None

        model_inputs = super().prepare_inputs_for_generation(
            input_ids=input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            input_image_embeds=input_image_embeds,
            image_sizes=image_sizes,
            image_attention_mask=image_attention_mask,
            input_audio_embeds=input_audio_embeds,
            audio_embed_sizes=audio_embed_sizes,
            audio_attention_mask=audio_attention_mask,
            input_mode=input_mode,
            cache_position=cache_position,
            position_ids=position_ids,
            use_cache=use_cache,
            num_logits_to_keep=num_logits_to_keep,
            **kwargs,
        )
        return model_inputs

#######################################################################################################



AutoConfig.register("qwen2-mm", Qwen2MMConfig)
AutoModelForCausalLM.register(Qwen2MMConfig, Qwen2MMForCausalLM)
Qwen2MMConfig.register_for_auto_class()
Qwen2MMForCausalLM.register_for_auto_class("AutoModelForCausalLM")