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# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                            LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from peft import LoraConfig
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
                          CLIPImageProcessor, CLIPVisionModel)

from xtuner.dataset import LLaVADataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory
from xtuner.dataset.samplers import LengthGroupedSampler
from xtuner.engine.hooks import DatasetInfoHook, EvaluateChatHook
from xtuner.engine.runner import TrainLoop
from xtuner.model import LLaVAModel
from xtuner.utils import PROMPT_TEMPLATE

#######################################################################
#                          PART 1  Settings                           #
#######################################################################
# Model
llm_name_or_path = 'meta-llama/Meta-Llama-3-8B-Instruct'
visual_encoder_name_or_path = 'openai/clip-vit-large-patch14-336'
# Specify the pretrained pth
pretrained_pth = './work_dirs/llava_llama3_8b_instruct_clip_vit_large_p14_336_e1_gpu8_pretrain/iter_2181.pth'  # noqa: E501

# Data
data_root = './data/llava_data/'
data_path = data_root + 'LLaVA-Instruct-150K/llava_v1_5_mix665k.json'
image_folder = data_root + 'llava_images'
prompt_template = PROMPT_TEMPLATE.llama3_chat
max_length = int(2048 - (336 / 14)**2)

# Scheduler & Optimizer
batch_size = 8  # per_device
accumulative_counts = 2
dataloader_num_workers = 0
max_epochs = 1
optim_type = AdamW
lr = 2e-5
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1  # grad clip
warmup_ratio = 0.03

# Save
save_steps = 1000
save_total_limit = 2  # Maximum checkpoints to keep (-1 means unlimited)

# Evaluate the generation performance during the training
evaluation_freq = 1000
SYSTEM = ''
evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
evaluation_inputs = ['请描述一下这张照片', 'Please describe this picture']

#######################################################################
#            PART 2  Model & Tokenizer & Image Processor              #
#######################################################################
tokenizer = dict(
    type=AutoTokenizer.from_pretrained,
    pretrained_model_name_or_path=llm_name_or_path,
    trust_remote_code=True,
    padding_side='right')

image_processor = dict(
    type=CLIPImageProcessor.from_pretrained,
    pretrained_model_name_or_path=visual_encoder_name_or_path,
    trust_remote_code=True)

model = dict(
    type=LLaVAModel,
    freeze_llm=False,
    freeze_visual_encoder=True,
    pretrained_pth=pretrained_pth,
    llm=dict(
        type=AutoModelForCausalLM.from_pretrained,
        pretrained_model_name_or_path=llm_name_or_path,
        trust_remote_code=True),
    visual_encoder=dict(
        type=CLIPVisionModel.from_pretrained,
        pretrained_model_name_or_path=visual_encoder_name_or_path),
    visual_encoder_lora=dict(
        type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05, bias='none'))

#######################################################################
#                      PART 3  Dataset & Dataloader                   #
#######################################################################
llava_dataset = dict(
    type=LLaVADataset,
    data_path=data_path,
    image_folder=image_folder,
    tokenizer=tokenizer,
    image_processor=image_processor,
    dataset_map_fn=llava_map_fn,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    max_length=max_length,
    pad_image_to_square=True)

train_dataloader = dict(
    batch_size=batch_size,
    num_workers=dataloader_num_workers,
    dataset=llava_dataset,
    sampler=dict(
        type=LengthGroupedSampler,
        length_property='modality_length',
        per_device_batch_size=batch_size * accumulative_counts),
    collate_fn=dict(type=default_collate_fn))

#######################################################################
#                    PART 4  Scheduler & Optimizer                    #
#######################################################################
# optimizer
optim_wrapper = dict(
    type=AmpOptimWrapper,
    optimizer=dict(
        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
    accumulative_counts=accumulative_counts,
    loss_scale='dynamic',
    dtype='float16')

# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
param_scheduler = [
    dict(
        type=LinearLR,
        start_factor=1e-5,
        by_epoch=True,
        begin=0,
        end=warmup_ratio * max_epochs,
        convert_to_iter_based=True),
    dict(
        type=CosineAnnealingLR,
        eta_min=0.0,
        by_epoch=True,
        begin=warmup_ratio * max_epochs,
        end=max_epochs,
        convert_to_iter_based=True)
]

# train, val, test setting
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

#######################################################################
#                           PART 5  Runtime                           #
#######################################################################
# Log the dialogue periodically during the training process, optional
custom_hooks = [
    dict(type=DatasetInfoHook, tokenizer=tokenizer),
    dict(
        type=EvaluateChatHook,
        tokenizer=tokenizer,
        image_processor=image_processor,
        every_n_iters=evaluation_freq,
        evaluation_inputs=evaluation_inputs,
        evaluation_images=evaluation_images,
        system=SYSTEM,
        prompt_template=prompt_template)
]

# configure default hooks
default_hooks = dict(
    # record the time of every iteration.
    timer=dict(type=IterTimerHook),
    # print log every 10 iterations.
    logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
    # enable the parameter scheduler.
    param_scheduler=dict(type=ParamSchedulerHook),
    # save checkpoint per `save_steps`.
    checkpoint=dict(
        type=CheckpointHook,
        by_epoch=False,
        interval=save_steps,
        max_keep_ckpts=save_total_limit),
    # set sampler seed in distributed evrionment.
    sampler_seed=dict(type=DistSamplerSeedHook),
)

# configure environment
env_cfg = dict(
    # whether to enable cudnn benchmark
    cudnn_benchmark=False,
    # set multi process parameters
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    # set distributed parameters
    dist_cfg=dict(backend='nccl'),
)

# set visualizer
visualizer = None

# set log level
log_level = 'INFO'

# load from which checkpoint
load_from = None

# whether to resume training from the loaded checkpoint
resume = False

# Defaults to use random seed and disable `deterministic`
randomness = dict(seed=None, deterministic=False)

# set log processor
log_processor = dict(by_epoch=False)

	# Copyright (c) OpenMMLab. All rights reserved.
	from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
	LoggerHook, ParamSchedulerHook)
	from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
	from peft import LoraConfig
	from torch.optim import AdamW
	from transformers import (AutoModelForCausalLM, AutoTokenizer,
	CLIPImageProcessor, CLIPVisionModel)

	from xtuner.dataset import LLaVADataset
	from xtuner.dataset.collate_fns import default_collate_fn
	from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory
	from xtuner.dataset.samplers import LengthGroupedSampler
	from xtuner.engine.hooks import DatasetInfoHook, EvaluateChatHook
	from xtuner.engine.runner import TrainLoop
	from xtuner.model import LLaVAModel
	from xtuner.utils import PROMPT_TEMPLATE

	#######################################################################
	# PART 1 Settings #
	#######################################################################
	# Model
	llm_name_or_path = 'meta-llama/Meta-Llama-3-8B-Instruct'
	visual_encoder_name_or_path = 'openai/clip-vit-large-patch14-336'
	# Specify the pretrained pth
	pretrained_pth = './work_dirs/llava_llama3_8b_instruct_clip_vit_large_p14_336_e1_gpu8_pretrain/iter_2181.pth' # noqa: E501

	# Data
	data_root = './data/llava_data/'
	data_path = data_root + 'LLaVA-Instruct-150K/llava_v1_5_mix665k.json'
	image_folder = data_root + 'llava_images'
	prompt_template = PROMPT_TEMPLATE.llama3_chat
	max_length = int(2048 - (336 / 14)**2)

	# Scheduler & Optimizer
	batch_size = 8 # per_device
	accumulative_counts = 2
	dataloader_num_workers = 0
	max_epochs = 1
	optim_type = AdamW
	lr = 2e-5
	betas = (0.9, 0.999)
	weight_decay = 0
	max_norm = 1 # grad clip
	warmup_ratio = 0.03

	# Save
	save_steps = 1000
	save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)

	# Evaluate the generation performance during the training
	evaluation_freq = 1000
	SYSTEM = ''
	evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
	evaluation_inputs = ['请描述一下这张照片', 'Please describe this picture']

	#######################################################################
	# PART 2 Model & Tokenizer & Image Processor #
	#######################################################################
	tokenizer = dict(
	type=AutoTokenizer.from_pretrained,
	pretrained_model_name_or_path=llm_name_or_path,
	trust_remote_code=True,
	padding_side='right')

	image_processor = dict(
	type=CLIPImageProcessor.from_pretrained,
	pretrained_model_name_or_path=visual_encoder_name_or_path,
	trust_remote_code=True)

	model = dict(
	type=LLaVAModel,
	freeze_llm=False,
	freeze_visual_encoder=True,
	pretrained_pth=pretrained_pth,
	llm=dict(
	type=AutoModelForCausalLM.from_pretrained,
	pretrained_model_name_or_path=llm_name_or_path,
	trust_remote_code=True),
	visual_encoder=dict(
	type=CLIPVisionModel.from_pretrained,
	pretrained_model_name_or_path=visual_encoder_name_or_path),
	visual_encoder_lora=dict(
	type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05, bias='none'))

	#######################################################################
	# PART 3 Dataset & Dataloader #
	#######################################################################
	llava_dataset = dict(
	type=LLaVADataset,
	data_path=data_path,
	image_folder=image_folder,
	tokenizer=tokenizer,
	image_processor=image_processor,
	dataset_map_fn=llava_map_fn,
	template_map_fn=dict(
	type=template_map_fn_factory, template=prompt_template),
	max_length=max_length,
	pad_image_to_square=True)

	train_dataloader = dict(
	batch_size=batch_size,
	num_workers=dataloader_num_workers,
	dataset=llava_dataset,
	sampler=dict(
	type=LengthGroupedSampler,
	length_property='modality_length',
	per_device_batch_size=batch_size * accumulative_counts),
	collate_fn=dict(type=default_collate_fn))

	#######################################################################
	# PART 4 Scheduler & Optimizer #
	#######################################################################
	# optimizer
	optim_wrapper = dict(
	type=AmpOptimWrapper,
	optimizer=dict(
	type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
	clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
	accumulative_counts=accumulative_counts,
	loss_scale='dynamic',
	dtype='float16')

	# learning policy
	# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
	param_scheduler = [
	dict(
	type=LinearLR,
	start_factor=1e-5,
	by_epoch=True,
	begin=0,
	end=warmup_ratio * max_epochs,
	convert_to_iter_based=True),
	dict(
	type=CosineAnnealingLR,
	eta_min=0.0,
	by_epoch=True,
	begin=warmup_ratio * max_epochs,
	end=max_epochs,
	convert_to_iter_based=True)
	]

	# train, val, test setting
	train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

	#######################################################################
	# PART 5 Runtime #
	#######################################################################
	# Log the dialogue periodically during the training process, optional
	custom_hooks = [
	dict(type=DatasetInfoHook, tokenizer=tokenizer),
	dict(
	type=EvaluateChatHook,
	tokenizer=tokenizer,
	image_processor=image_processor,
	every_n_iters=evaluation_freq,
	evaluation_inputs=evaluation_inputs,
	evaluation_images=evaluation_images,
	system=SYSTEM,
	prompt_template=prompt_template)
	]

	# configure default hooks
	default_hooks = dict(
	# record the time of every iteration.
	timer=dict(type=IterTimerHook),
	# print log every 10 iterations.
	logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
	# enable the parameter scheduler.
	param_scheduler=dict(type=ParamSchedulerHook),
	# save checkpoint per `save_steps`.
	checkpoint=dict(
	type=CheckpointHook,
	by_epoch=False,
	interval=save_steps,
	max_keep_ckpts=save_total_limit),
	# set sampler seed in distributed evrionment.
	sampler_seed=dict(type=DistSamplerSeedHook),
	)

	# configure environment
	env_cfg = dict(
	# whether to enable cudnn benchmark
	cudnn_benchmark=False,
	# set multi process parameters
	mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
	# set distributed parameters
	dist_cfg=dict(backend='nccl'),
	)

	# set visualizer
	visualizer = None

	# set log level
	log_level = 'INFO'

	# load from which checkpoint
	load_from = None

	# whether to resume training from the loaded checkpoint
	resume = False

	# Defaults to use random seed and disable `deterministic`
	randomness = dict(seed=None, deterministic=False)

	# set log processor
	log_processor = dict(by_epoch=False)