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


import os
import warnings
import shutil

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from omnicharacter.model import *
from omnicharacter.model.speech_encoder.builder import build_speech_encoder


def load_pretrained_model(model_path, model_base, model_name=None, is_lora=False, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", use_flash_attn=False, **kwargs):
    kwargs = {"device_map": device_map, **kwargs}

    if device != "cuda":
        kwargs['device_map'] = {"": device}

    if load_8bit:
        kwargs['load_in_8bit'] = True
    elif load_4bit:
        kwargs['load_in_4bit'] = True
        kwargs['quantization_config'] = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type='nf4'
        )
    else:
        kwargs['torch_dtype'] = torch.float16

    if use_flash_attn:
        kwargs['attn_implementation'] = 'flash_attention_2'


    # Load LLaVA model
    if is_lora and model_base is None:
        warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
    if is_lora and model_base is not None:
        from llava_her.model.language_model.llava_her_llama import LlavaHerConfig
        lora_cfg_pretrained = LlavaHerConfig.from_pretrained(model_path)
        tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
        print('Loading LLaVA from base model...')
        model = LlavaHerLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
        token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
        if model.lm_head.weight.shape[0] != token_num:
            model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
            model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))

        print('Loading additional LLaVA weights...')
        if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
            non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
        else:
            # this is probably from HF Hub
            from huggingface_hub import hf_hub_download
            def load_from_hf(repo_id, filename, subfolder=None):
                cache_file = hf_hub_download(
                    repo_id=repo_id,
                    filename=filename,
                    subfolder=subfolder)
                return torch.load(cache_file, map_location='cpu')
            non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
        non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
        if any(k.startswith('model.model.') for k in non_lora_trainables):
            non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
        model.load_state_dict(non_lora_trainables, strict=False)

        from peft import PeftModel
        print('Loading LoRA weights...')
        model = PeftModel.from_pretrained(model, model_path)
        print('Merging LoRA weights...')
        model = model.merge_and_unload()
        print('Model is loaded...')
    elif model_base is not None:
        # this may be mm projector only
        print('Loading LLaVA from base model...')

        tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
        cfg_pretrained = AutoConfig.from_pretrained(model_path)
        model = LlavaHerLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)

        mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
        mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
        model.load_state_dict(mm_projector_weights, strict=False)
    else:

        tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
        model = LlavaHerLlamaForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **kwargs
        )

    vision_tower = model.get_vision_tower()
    if not vision_tower.is_loaded:
        vision_tower.load_model(device_map=device_map)
    if device_map != 'auto':
        vision_tower.to(device=device_map, dtype=torch.float16)

    model.get_model().speech_encoder = build_speech_encoder(model.config)
    model.get_model().speech_encoder.to(device="cuda", dtype=torch.float16)
    
    image_processor = vision_tower.image_processor

    if hasattr(model.config, "max_sequence_length"):
        context_len = model.config.max_sequence_length
    else:
        context_len = 2048

    return tokenizer, model, image_processor, context_len


def load_pretrained_qwen_model(model_path, model_base, model_name=None, is_lora=False, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", use_flash_attn=False, **kwargs):
    kwargs = {"device_map": device_map, **kwargs}

    if device != "cuda":
        kwargs['device_map'] = {"": device}

    if load_8bit:
        kwargs['load_in_8bit'] = True
    elif load_4bit:
        kwargs['load_in_4bit'] = True
        kwargs['quantization_config'] = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type='nf4'
        )
    else:
        kwargs['torch_dtype'] = torch.float16

    if use_flash_attn:
        kwargs['attn_implementation'] = 'flash_attention_2'


    # Load LLaVA model
    if is_lora and model_base is None:
        warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
    if is_lora and model_base is not None:
        lora_cfg_pretrained = LlavaHerQwenConfig.from_pretrained(model_path)
        tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
        print('Loading LLaVA from base model...')
        model = LlavaHerQwen2ForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
        token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
        if model.lm_head.weight.shape[0] != token_num:
            model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
            model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))

        print('Loading additional LLaVA weights...')
        if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
            non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
        else:
            # this is probably from HF Hub
            from huggingface_hub import hf_hub_download
            def load_from_hf(repo_id, filename, subfolder=None):
                cache_file = hf_hub_download(
                    repo_id=repo_id,
                    filename=filename,
                    subfolder=subfolder)
                return torch.load(cache_file, map_location='cpu')
            non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
        non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
        if any(k.startswith('model.model.') for k in non_lora_trainables):
            non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
        model.load_state_dict(non_lora_trainables, strict=False)

        from peft import PeftModel
        print('Loading LoRA weights...')
        model = PeftModel.from_pretrained(model, model_path)
        print('Merging LoRA weights...')
        model = model.merge_and_unload()
        print('Model is loaded...')
    elif model_base is not None:
        # this may be mm projector only
        print('Loading LLaVA from base model...')
        tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
        cfg_pretrained = AutoConfig.from_pretrained(model_path)
        model = LlavaHerQwen2ForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)

        mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
        mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
        model.load_state_dict(mm_projector_weights, strict=False)
    else:
        tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
        model = LlavaHerQwen2ForCausalLM.from_pretrained(
            model_path,
            low_cpu_mem_usage=True,
            **kwargs
        )

    vision_tower = model.get_vision_tower()
    if not vision_tower.is_loaded:
        vision_tower.load_model(device_map=device_map)
    if device_map != 'auto':
        vision_tower.to(device=device_map, dtype=torch.float16)

    model.get_model().speech_encoder = build_speech_encoder(model.config)
    model.get_model().speech_encoder.to(device="cuda", dtype=torch.float16)
    
    image_processor = vision_tower.image_processor

    if hasattr(model.config, "max_sequence_length"):
        context_len = model.config.max_sequence_length
    else:
        context_len = 2048

    return tokenizer, model, image_processor, context_len

