"""
    #################################################################################################################################################################################################
    # 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 torch
from huggingface_hub import hf_hub_download
from sapiens_transformers import (AddedToken, AutoConfig, AutoTokenizer, VideoLlavaConfig, VideoLlavaForConditionalGeneration, VideoLlavaImageProcessor, VideoLlavaProcessor)
EPILOG_TXT = """Example:
    python transformers/src/transformers/models/video_llava/convert_video_llava_weights_to_hf.py --text_model_id lmsys/vicuna-7b-v1.5 --vision_model_id openai/clip-vit-large-patch14 --output_hub_path org/video_llava-7b --old_state_dict_id LanguageBind/Video-LLaVA-7B
Example for creating the old state dict file with Python:
    import torch
    from video_llava.model.language_model.video_llava import VideoLlavaForCausalLM
    kwargs = {"device_map": "auto", "torch_dtype": torch.float16}
    model = VideoLlavaForCausalLM.from_pretrained("LanguageBind/Video-LLaVA-7B-hf", low_cpu_mem_usage=True, **kwargs)
    model.get_vision_tower().load_model()
    torch.save(model.state_dict(), "tmp/hf_models/video_llava-7b/model_state_dict.bin")
"""
KEYS_TO_MODIFY_MAPPING = {'model.video_tower.video_tower': 'video_tower', 'model.image_tower.image_tower': 'image_tower', 'model.mm_projector': 'multi_modal_projector',
'model': 'language_model.model', 'lm_head': 'language_model.lm_head', 'video_tower': 'video_tower.vision_model', 'image_tower': 'image_tower.vision_model',
'multi_modal_projector.0': 'multi_modal_projector.linear_1', 'multi_modal_projector.2': 'multi_modal_projector.linear_2'}
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
    return new_state_dict
def convert_video_llava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id):
    torch.set_default_dtype(torch.float16)
    text_config = AutoConfig.from_pretrained(text_model_id)
    tokenizer = AutoTokenizer.from_pretrained(text_model_id)
    tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
    tokenizer.add_tokens(AddedToken("<video>", special=True, normalized=False), special_tokens=True)
    tokenizer.add_special_tokens({"pad_token": "<pad>"})
    tokenizer.padding_side = "left"
    image_processor = VideoLlavaImageProcessor.from_pretrained(vision_model_id)
    processor = VideoLlavaProcessor(tokenizer=tokenizer, image_processor=image_processor)
    config = VideoLlavaConfig(text_config=text_config)
    config.pad_token_id = 32002
    with torch.device("meta"): model = VideoLlavaForConditionalGeneration(config)
    model_state_dict = set(model.state_dict().keys())
    pad_shape = 64
    state_dict_temp = "pytorch_model-0000{i}-of-00002.bin"
    for shard in range(1, 3):
        state_dict_path = hf_hub_download(old_state_dict_id, state_dict_temp.format(i=shard))
        state_dict = torch.load(state_dict_path, map_location="cpu")
        state_dict = convert_state_dict_to_hf(state_dict)
        model.load_state_dict(state_dict, strict=False, assign=True)
        model_state_dict -= set(state_dict.keys())
    if len(model_state_dict) > 0: raise RuntimeError(f"Missing keys in state dict: {model_state_dict}")
    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)
    model.resize_token_embeddings(config.text_config.vocab_size + 3, pad_shape)
    model.language_model.model.embed_tokens.weight.data[32000:] = torch.stack(tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[32000:].shape[0]))), dim=0)
    model.language_model.lm_head.weight.data[32000:] = torch.stack(tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[32000:].shape[0]))), dim=0)
    model.push_to_hub(output_hub_path)
    processor.push_to_hub(output_hub_path)
def main():
    parser = argparse.ArgumentParser(epilog=EPILOG_TXT, formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument("--text_model_id", help="Hub location of the text model")
    parser.add_argument("--vision_model_id", help="Hub location of the vision model")
    parser.add_argument("--output_hub_path", help="Location on the hub of the converted model")
    parser.add_argument("--old_state_dict_id", help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`")
    args = parser.parse_args()
    convert_video_llava_llama_to_hf(args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id)
if __name__ == "__main__": main()
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
