"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
import glob
import json
from pathlib import Path
import torch
from sapiens_accelerator import init_empty_weights
from huggingface_hub import hf_hub_download, snapshot_download
from safetensors import safe_open
from sapiens_transformers import (AddedToken, AutoConfig, AutoTokenizer, LlavaNextImageProcessor, LlavaNextVideoConfig, LlavaNextVideoForConditionalGeneration, LlavaNextVideoImageProcessor, LlavaNextVideoProcessor)
KEYS_TO_MODIFY_MAPPING = {'model.vision_tower.': '', '.vision_resampler': '', 'model.mm_projector': 'multi_modal_projector', 'model': 'model.model', 'vision_model.model': 'vision_model',
'lm_head': 'language_model.lm_head', 'model.model': 'language_model.model', 'multi_modal_projector.0': 'multi_modal_projector.linear_1', 'multi_modal_projector.2': 'multi_modal_projector.linear_2',
'language_model.model.image_newline': 'image_newline'}
chat_vicuna = (
    "{% for message in messages %}"
    "{% if message['role'] == 'system' %}"
    "{{ message['content'][0]['text'] }}"
    "{% else %}"
    "{{ message['role'].upper() + ': '}}"
    "{% endif %}"
    "{# Render all images first #}"
    "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
    "{{ '<image>\n' }}"
    "{% endfor %}"
    "{# Render all text next #}"
    "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
    "{{ content['text'] + ' '}}"
    "{% endfor %}"
    "{% endfor %}"
    "{% if add_generation_prompt %}"
    "{{ 'ASSISTANT:' }}"
    "{% endif %}"
)
chat_mistral = (
    "{% for message in messages %}"
    "{% if message['role'] == 'user' %}"
    "{{ '[INST] ' }}"
    "{# Render all images first #}"
    "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
    "{{ '<image>\n' }}"
    "{% endfor %}"
    "{# Render all text next #}"
    "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
    "{{ content['text'] }}"
    "{% endfor %}"
    "{{' [/INST]' }}"
    "{% elif message['role'] == 'assistant' %}"
    r"{{ ' ' + message['content'][0]['text'] + '<\s> '}}"
    "{% else %}"
    "{{ raise_exception('Only user and assistant roles are supported!') }}"
    "{% endif %}"
    "{% endfor %}"
)
chat_yi = (
    "{% for message in messages %}"
    "{{'<|im_start|>' + message['role'] + '\n'}}"
    "{# Render all images first #}"
    "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
    "{{ '<image>\n' }}"
    "{% endfor %}"
    "{# Render all text next #}"
    "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
    "{{ content['text'] }}"
    "{% endfor %}"
    "{{'<|im_end|>' + '\n'}}"
    "{% endfor %}"
    "{% if add_generation_prompt %}"
    "{{ '<|im_start|>assistant\n' }}"
    "{% endif %}"
)
model2template = {"lmms-lab/LLaVA-NeXT-Video-7B-32K": chat_mistral, "lmms-lab/LLaVA-NeXT-Video-7B": chat_vicuna, "lmms-lab/LLaVA-NeXT-Video-7B-DPO": chat_vicuna, "lmms-lab/LLaVA-NeXT-Video-34B": chat_yi, "lmms-lab/LLaVA-NeXT-Video-34B-DPO": chat_yi}
def load_original_state_dict(model_id):
    directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
    original_state_dict = {}
    for path in glob.glob(f"{directory_path}/*"):
        if path.endswith(".safetensors"):
            with safe_open(path, framework="pt", device="cpu") as f:
                for key in f.keys(): original_state_dict[key] = f.get_tensor(key)
    return original_state_dict
def convert_state_dict_to_hf(state_dict):
    new_state_dict = {}
    for key, value in state_dict.items():
        if key.endswith(".inv_freq"): continue
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key: key = key.replace(key_to_modify, new_key)
        new_state_dict[key] = value.to(torch.bfloat16)
    return new_state_dict
def convert_llava_to_hf(model_id, pytorch_dump_folder_path, push_to_hub=False):
    filepath = hf_hub_download(repo_id=model_id, filename="config.json", repo_type="model")
    with open(filepath) as f:
        data = json.load(f)
        print(data)
    if model_id == "lmms-lab/LLaVA-NeXT-Video-7B-32K":
        text_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
        video_token_index = 32000
        image_token_index = 32001
        overwrite_text_config = {}
    elif model_id in ["lmms-lab/LLaVA-NeXT-Video-7B", "lmms-lab/LLaVA-NeXT-Video-7B-DPO"]:
        text_model_id = "lmsys/vicuna-7b-v1.5"
        video_token_index = 32000
        image_token_index = 32001
        overwrite_text_config = {"factor": 2.0, "type": "linear"}
    elif model_id in ["lmms-lab/LLaVA-NeXT-Video-34B", "lmms-lab/LLaVA-NeXT-Video-34B-DPO"]:
        text_model_id = "NousResearch/Nous-Hermes-2-Yi-34B"
        video_token_index = 64000
        image_token_index = 64001
        overwrite_text_config = {}
    else: raise ValueError("Incorrect checkpoint referenced. Text model-id not identified!")
    vision_model_id = data["mm_vision_tower"]
    torch.set_default_dtype(torch.bfloat16)
    text_config = AutoConfig.from_pretrained(text_model_id)
    text_config = text_config.to_dict()
    text_config.update(overwrite_text_config)
    tokenizer = AutoTokenizer.from_pretrained(text_model_id, use_fast=True, padding_side="left")
    tokenizer.add_tokens(AddedToken("<video>", special=True, normalized=False), special_tokens=True)
    tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
    image_processor = LlavaNextImageProcessor.from_pretrained(vision_model_id)
    video_processor = LlavaNextVideoImageProcessor.from_pretrained(vision_model_id)
    processor = LlavaNextVideoProcessor(tokenizer=tokenizer, video_processor=video_processor, image_processor=image_processor, chat_template=model2template[model_id])
    config = LlavaNextVideoConfig(text_config=text_config, image_grid_pinpoints=image_processor.image_grid_pinpoints, use_image_newline_parameter=True, video_token_index=video_token_index, image_token_index=image_token_index)
    with init_empty_weights(): model = LlavaNextVideoForConditionalGeneration(config)
    state_dict = load_original_state_dict(model_id)
    state_dict = convert_state_dict_to_hf(state_dict)
    model.load_state_dict(state_dict, assign=True, strict=True)
    pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data
    mu = torch.mean(pre_expansion_embeddings, dim=0).float()
    n = pre_expansion_embeddings.size()[0]
    sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n
    dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma)
    pad_shape = 64
    vocab_size = config.text_config.vocab_size
    num_tokens = vocab_size + 3
    model.resize_token_embeddings(num_tokens, pad_to_multiple_of=pad_shape)
    model.language_model.model.embed_tokens.weight.data[vocab_size:] = torch.stack(tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[vocab_size:].shape[0]))), dim=0)
    model.language_model.lm_head.weight.data[vocab_size:] = torch.stack(tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[vocab_size:].shape[0]))), dim=0)
    if pytorch_dump_folder_path is not None:
        print(f"Saving model and processor for {model_id} to {pytorch_dump_folder_path}")
        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
        model.save_pretrained(pytorch_dump_folder_path)
        processor.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        repo_id = model_id.split("/")[-1]
        print(f"Pushing model to hub repo: {repo_id}")
        model.push_to_hub(f"llava-hf/{repo_id}-hf")
        processor.push_to_hub(f"llava-hf/{repo_id}-hf")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_id", help="Hub location of the model to convert", default="lmms-lab/LLaVA-NeXT-Video-7B", choices=["lmms-lab/LLaVA-NeXT-Video-7B", "lmms-lab/LLaVA-NeXT-Video-7B-DPO", "lmms-lab/LLaVA-NeXT-Video-7B-32K", "lmms-lab/LLaVA-NeXT-Video-34B", "lmms-lab/LLaVA-NeXT-Video-34B-DPO"], required=False)
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the converted model to the HF hub.")
    args = parser.parse_args()
    convert_llava_to_hf(args.model_id, args.pytorch_dump_folder_path, args.push_to_hub)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
