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	# Copyright 2023 Haotian Liu
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	from abc import ABC, abstractmethod

	import torch
	import torch.nn as nn

	# from .multimodal_encoder.builder import build_vision_tower
	# from .multimodal_projector.builder import build_vision_projector

	# from .builders import build_vision_tower, build_vision_projector
	# from .constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
	import pdb


	#############################################################################
	# builders
	#############################################################################

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

	import torch
	import torch.nn as nn
	import re


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

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

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


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

	self.proj = nn.Sequential(
	nn.Linear(channels, channels),
	nn.GELU(),
	nn.Linear(channels, channels)
	)
	def forward(self, x):
	x = self.pre_norm(x)
	return x + self.proj(x)


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

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

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

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

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



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

	import os
	from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
	from transformers import AutoModel


	class CLIPVisionTower(nn.Module):
	def __init__(self, vision_tower, args, delay_load=False):
	super().__init__()

	self.is_loaded = False

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

	if not delay_load:
	self.load_model()
	else:
	self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)

	def load_model(self):
	print(f'loading vision model from {self.vision_tower_name}')
	self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
	if 'clip' in self.vision_tower_name.lower():
	self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)

	elif 'internvit' in self.vision_tower_name.lower():
	self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, trust_remote_code=True)
	else:
	raise ValueError(f'Please implement the loading of vision encoder here')

	self.vision_tower.requires_grad_(False)

	self.is_loaded = True

	def feature_select(self, image_forward_outs):
	image_features = image_forward_outs.hidden_states[self.select_layer]
	if self.select_feature == 'patch':
	image_features = image_features[:, 1:]
	elif self.select_feature == 'cls_patch':
	image_features = image_features
	else:
	raise ValueError(f'Unexpected select feature: {self.select_feature}')
	return image_features

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

	return image_features

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

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

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

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

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

	@property
	def num_patches(self):
	return (self.config.image_size // self.config.patch_size) ** 2



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

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


	#############################################################################
	# builders
	#############################################################################



	#############################################################################
	# constants
	#############################################################################

	CONTROLLER_HEART_BEAT_EXPIRATION = 30
	WORKER_HEART_BEAT_INTERVAL = 15

	LOGDIR = "."

	# Model Constants
	IGNORE_INDEX = -100
	IMAGE_TOKEN_INDEX = -200
	DEFAULT_IMAGE_TOKEN = "<image>"
	DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
	DEFAULT_IM_START_TOKEN = "<im_start>"
	DEFAULT_IM_END_TOKEN = "<im_end>"
	IMAGE_PLACEHOLDER = "<image-placeholder>"

	#############################################################################
	# constants
	#############################################################################


	class LlavaMetaModel:

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

	if hasattr(config, "mm_vision_tower"):
	self.vision_tower = build_vision_tower(config, delay_load=True)
	self.mm_projector = build_vision_projector(config)

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

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

	self.config.mm_vision_tower = vision_tower

	if self.get_vision_tower() is None:
	vision_tower = build_vision_tower(model_args)

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

	self.config.use_mm_proj = True
	self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
	self.config.mm_hidden_size = vision_tower.hidden_size
	self.config.mm_vision_select_layer = mm_vision_select_layer
	self.config.mm_vision_select_feature = mm_vision_select_feature

	if getattr(self, 'mm_projector', None) is None:
	self.mm_projector = build_vision_projector(self.config)
	else:
	# In case it is frozen by LoRA
	for p in self.mm_projector.parameters():
	p.requires_grad = True

	if pretrain_mm_mlp_adapter is not None:
	mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
	def get_w(weights, keyword):
	return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}

	self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))


	class LlavaMetaForCausalLM(ABC):

	@abstractmethod
	def get_model(self):
	pass

	@abstractmethod
	def get_tokenizer(self):
	pass

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

	def encode_images(self, images):
	image_features = self.get_model().get_vision_tower()(images)
	image_features = self.get_model().mm_projector(image_features)
	return image_features

	def prepare_inputs_labels_for_multimodal_new(
	self, input_ids: list[torch.tensor], position_ids, attention_mask: list[torch.tensor], past_key_values, labels, images
	):
	vision_tower = self.get_vision_tower()
	if not self.training: # TODO: check this out!!
	# pdb.set_trace()
	if vision_tower is None or images is None or input_ids.shape[1] == 1:
	if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:

	if attention_mask is None:
	# only happen for qwen at inference
	# raise ValueError(f'should not be here except for Qwen!')
	return input_ids, None, attention_mask, past_key_values, None, labels

	target_shape = past_key_values[-1][-1].shape[-2] + 1
	attention_mask = torch.cat((attention_mask, torch.ones(
	(attention_mask.shape[0], target_shape - attention_mask.shape[1]),
	dtype=attention_mask.dtype,
	device=attention_mask.device
	)), dim=1)
	position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
	return input_ids, position_ids, attention_mask, past_key_values, None, labels


	# ####################### this block must be optimized! #######################
	# if type(images) is list or images.ndim == 5:
	# concat_images = torch.cat([image for image in images], dim=0)
	# image_features = self.encode_images(concat_images)
	# split_sizes = [image.shape[0] for image in images]
	# image_features = torch.split(image_features, split_sizes, dim=0)
	# image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
	# else:
	# image_features = self.encode_images(images).to(self.device)
	# ####################### this block must be optimized! #######################

	# ####################### optimized #######################
	if getattr(self, 'cached_image_features', None) is None:
	# this attribute should be cleared in bot.clear_history()
	if type(images) is list or images.ndim == 5:
	concat_images = torch.cat([image for image in images], dim=0)
	image_features = self.encode_images(concat_images)
	split_sizes = [image.shape[0] for image in images]
	image_features = torch.split(image_features, split_sizes, dim=0)
	image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
	else:
	image_features = self.encode_images(images).to(self.device)
	self.cached_image_features = image_features
	image_features = self.cached_image_features
	# ####################### optimized #######################


	# TODO: image start / end is not implemented here to support pretraining.
	if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
	raise NotImplementedError

	# Let's just add dummy tensors if they do not exist,
	# it is a headache to deal with None all the time.
	# But it is not ideal, and if you have a better idea,
	# please open an issue / submit a PR, thanks.
	_labels = labels
	_position_ids = position_ids
	_attention_mask = attention_mask
	if attention_mask is None:
	# attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
	attention_mask = [torch.tensor([1]*l).to(input_ids).bool() for l in map(len, [ip for ip in input_ids])]
	else:
	# attention_mask = attention_mask.bool()
	attention_mask = [att.bool() for att in attention_mask]

	# if position_ids is None:
	# position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)

	if labels is None:
	labels = [torch.tensor([IGNORE_INDEX]*l).to(input_ids) for l in map(len, [ip for ip in input_ids])]
	# labels = torch.full_like(input_ids, IGNORE_INDEX)
	else:
	labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
	# remove the padding using attention_mask -- TODO: double check
	# pdb.set_trace()
	input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]

	new_input_embeds = []
	new_labels = []
	cur_image_idx = 0
	for batch_idx, cur_input_ids in enumerate(input_ids):
	num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
	if num_images == 0:

	############### FIXME ###############
	if cur_image_idx > len(image_features)-1:
	cur_image_idx = len(image_features)-1
	print(f'warning: {input_ids}')
	############### FIXME ###############

	cur_image_features = image_features[cur_image_idx]
	cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
	cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
	new_input_embeds.append(cur_input_embeds)
	new_labels.append(labels[batch_idx])
	cur_image_idx += 1
	continue

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

	# you have 10 images, but you have 11 placeholders
	for i in range(num_images + 1):
	cur_new_input_embeds.append(cur_input_embeds_no_im[i])
	cur_new_labels.append(cur_labels_noim[i])
	if i < num_images:
	############### FIXME ###############
	if cur_image_idx > len(image_features)-1:
	cur_image_idx = len(image_features)-1
	print(f'warning: {input_ids}')
	############### FIXME ###############

	cur_image_features = image_features[cur_image_idx]
	cur_image_idx += 1
	cur_new_input_embeds.append(cur_image_features)
	cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))

	cur_new_input_embeds = torch.cat(cur_new_input_embeds)
	cur_new_labels = torch.cat(cur_new_labels)

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

	# Truncate sequences to max length as image embeddings can make the sequence longer
	tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
	if tokenizer_model_max_length is not None:
	new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
	new_labels = [x[:tokenizer_model_max_length] for x in new_labels]

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

	new_input_embeds_padded = []
	new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
	attention_mask = torch.zeros((batch_size, max_len), dtype=torch.bool, device=attention_mask[0].device)
	position_ids = torch.zeros((batch_size, max_len), dtype=torch.long, device=attention_mask[0].device)

	for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
	cur_len = cur_new_embed.shape[0]
	# print(f'cur_len[{i}]before padding: {cur_len}')
	# if i==0:
	# print(f"{getattr(self.config, 'tokenizer_padding_side', 'right')} {self.get_tokenizer().padding_side}")
	if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": # checked, this is correct
	# if self.get_tokenizer().padding_side == 'left':
	new_input_embeds_padded.append(torch.cat((
	torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
	cur_new_embed
	), dim=0))
	if cur_len > 0:
	new_labels_padded[i, -cur_len:] = cur_new_labels
	attention_mask[i, -cur_len:] = True
	position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
	else:
	new_input_embeds_padded.append(torch.cat((
	cur_new_embed,
	torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
	), dim=0))
	if cur_len > 0:
	new_labels_padded[i, :cur_len] = cur_new_labels
	attention_mask[i, :cur_len] = True
	position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)

	new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)

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

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

	if _position_ids is None:
	position_ids = None

	return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels

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

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

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

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

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

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

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