"""
    #################################################################################################################################################################################################
    # 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 gc
import glob
import json
from pathlib import Path
import requests
import torch
from sapiens_accelerator import init_empty_weights
from huggingface_hub import hf_hub_download, snapshot_download
from PIL import Image
from safetensors import safe_open
from sapiens_transformers import (AddedToken, AutoConfig, AutoTokenizer, LlavaNextConfig, LlavaNextForConditionalGeneration, LlavaNextImageProcessor, LlavaNextProcessor)
KEYS_TO_MODIFY_MAPPING = {'model.vision_tower.': '', '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'}
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.float16)
    return new_state_dict
def load_image():
    url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
    image = Image.open(requests.get(url, stream=True).raw)
    return image
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 == "liuhaotian/llava-v1.6-mistral-7b":
        text_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
        image_token_index = 32000
    elif model_id == "liuhaotian/llava-v1.6-vicuna-7b":
        text_model_id = "lmsys/vicuna-7b-v1.5"
        image_token_index = 32000
    elif model_id == "liuhaotian/llava-v1.6-vicuna-13b":
        text_model_id = "lmsys/vicuna-13b-v1.5"
        image_token_index = 32000
    elif model_id == "liuhaotian/llava-v1.6-34b":
        text_model_id = "NousResearch/Nous-Hermes-2-Yi-34B"
        image_token_index = 64000
    elif model_id == "lmms-lab/llama3-llava-next-8b":
        text_model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
        image_token_index = 128256
    elif model_id == "lmms-lab/llava-next-72b":
        text_model_id = "Qwen/Qwen1.5-72B-Chat"
        image_token_index = 151646
    elif model_id == "lmms-lab/llava-next-110b":
        text_model_id = "Qwen/Qwen1.5-110B-Chat"
        image_token_index = 151646
    vision_model_id = data["mm_vision_tower"]
    torch.set_default_dtype(torch.float16)
    text_config = AutoConfig.from_pretrained(text_model_id)
    use_fast = False if model_id == "liuhaotian/llava-v1.6-34b" else True
    tokenizer = AutoTokenizer.from_pretrained(text_model_id, use_fast=use_fast)
    tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True)
    if model_id in ("liuhaotian/llava-v1.6-mistral-7b", "lmms-lab/llama3-llava-next-8b"): tokenizer.add_special_tokens({"pad_token": "<pad>"})
    image_processor = LlavaNextImageProcessor.from_pretrained(vision_model_id)
    processor = LlavaNextProcessor(tokenizer=tokenizer, image_processor=image_processor)
    config = LlavaNextConfig(text_config=text_config.to_dict(), image_grid_pinpoints=image_processor.image_grid_pinpoints, use_image_newline_parameter=True, image_token_index=image_token_index)
    with init_empty_weights(): model = LlavaNextForConditionalGeneration(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)
    model.eval()
    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)
    if model_id not in ["lmms-lab/llava-next-72b", "lmms-lab/llava-next-110b"]:
        pad_shape = 64
        vocab_size = config.text_config.vocab_size
        if model_id == "liuhaotian/llava-v1.6-34b": num_tokens = vocab_size + 3
        else: num_tokens = vocab_size + 2
        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)
    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)
    del state_dict
    gc.collect()
    model = LlavaNextForConditionalGeneration.from_pretrained(pytorch_dump_folder_path, device_map="auto")
    processor = LlavaNextProcessor.from_pretrained(pytorch_dump_folder_path)
    device = model.device
    image = load_image()
    if model_id == "liuhaotian/llava-v1.6-mistral-7b": prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
    elif model_id in ["liuhaotian/llava-v1.6-vicuna-7b", "liuhaotian/llava-v1.6-vicuna-13b"]: prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
    elif model_id == "liuhaotian/llava-v1.6-34b": prompt = "<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|><|im_start|>assistant\n"
    elif model_id == "lmms-lab/llama3-llava-next-8b": prompt = "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.<|eot_id|><|start_header_id|><|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat is shown in this image?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    elif model_id in ["lmms-lab/llava-next-72b", "lmms-lab/llava-next-110b"]: prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\nWhat is shown in this image?<|im_end|>\n<|im_start|>assistant\n"
    inputs = processor(images=image, text=prompt, return_tensors="pt")
    filepath = hf_hub_download(repo_id="nielsr/test-image", filename="llava_1_6_pixel_values.pt", repo_type="dataset")
    original_pixel_values = torch.load(filepath, map_location="cpu")
    assert torch.allclose(original_pixel_values, inputs.pixel_values.half())
    if model_id == "liuhaotian/llava-v1.6-mistral-7b":
        filepath = hf_hub_download(repo_id="nielsr/test-image", filename="llava_1_6_input_ids.pt", repo_type="dataset")
        original_input_ids = torch.load(filepath, map_location="cpu")
        original_input_ids[original_input_ids == -200] = image_token_index
        assert original_input_ids[0].tolist() == inputs.input_ids[0].tolist()
    elif model_id == "liuhaotian/llava-v1.6-34b":
        filepath = hf_hub_download(repo_id="nielsr/test-image", filename="llava_1_6_34b_input_ids.pt", repo_type="dataset")
        original_input_ids = torch.load(filepath, map_location="cpu")
        original_input_ids[original_input_ids == -200] = image_token_index
        assert original_input_ids[0].tolist() == inputs.input_ids[0].tolist()
    image_sizes = torch.tensor([[899, 1024]])
    assert image_sizes[0].tolist() == inputs.image_sizes[0].tolist()
    print("Single forward pass")
    with torch.inference_mode():
        inputs = inputs.to(device)
        outputs = model(**inputs)
        print("Shape of logits:", outputs.logits.shape)
        print("First values of logits:", outputs.logits[0, :3, :3])
        if model_id == "liuhaotian/llava-v1.6-mistral-7b": expected_slice = torch.tensor([[-4.8555, -4.6992, -0.1996], [-10.5703, -10.7344, -2.7246], [-7.0391, -7.3672, -0.2634]], dtype=torch.float32, device=device)
        elif model_id == "liuhaotian/llava-v1.6-vicuna-7b": expected_slice = torch.tensor([[1.4883, 0.9976, -0.6992], [-9.7031, -5.7031, -1.5557], [-5.1328, -5.5586, 8.8281]], dtype=torch.float32, device=device)
        elif model_id == "liuhaotian/llava-v1.6-vicuna-13b": expected_slice = torch.tensor([[-0.9614, 7.3125, 0.2106], [-7.2695, -8.5469, 3.6211], [-6.3750, -8.1875, 5.4688]], dtype=torch.float32, device=device)
        elif model_id == "liuhaotian/llava-v1.6-34b": expected_slice = torch.tensor([[-9.0859, -9.1406, 5.9453], [-5.9570, -5.9766, 2.2754], [-5.7305, -5.7539, 4.0000]], dtype=torch.float32, device=device)
        elif model_id == "lmms-lab/llama3-llava-next-8b": expected_slice = torch.tensor([[-3.9648, 1.1396, 3.3145], [-5.3594, -1.5654, -1.9619], [-12.3750, -10.6797, -9.3125]], dtype=torch.float32, device=device)
        elif model_id == "lmms-lab/llava-next-72b": expected_slice = torch.tensor([[3.7148, 3.9277, 3.4395], [-0.4341, 1.1387, 6.5117], [3.2324, 3.4688, 4.1133]], dtype=torch.float32, device=device)
        elif model_id == "lmms-lab/llava-next-110b": expected_slice = torch.tensor([[-2.5449, -1.6738, -2.0371], [1.0811, 3.4961, 5.0312], [1.7803, 2.5137, 2.4277]], dtype=torch.float32, device=device)
        else: raise ValueError(f"Model {model_id} not supported")
        assert torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)
        print("Logits are ok!")
    output_ids = model.generate(**inputs, max_new_tokens=100, use_cache=True)
    generated_text = processor.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
    print("Generated text:", repr(generated_text))
    if model_id == "liuhaotian/llava-v1.6-mistral-7b": expected_text = '[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.\n\nIn this particular radar chart, there are several axes labeled with different metrics or benchmarks, such as "MMM-Vet," "MMM-Bench," "LLaVA-Bench," "SLED-Bench," "'
    elif model_id == "liuhaotian/llava-v1.6-vicuna-7b": expected_text = """A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human\'s questions. USER:  \nWhat is shown in this image? ASSISTANT: The image appears to be a graphical representation of a benchmarking study comparing the performance of various models or systems. It\'s a scatter plot with a circular layout, where each point represents a different model or system, and the axes represent different metrics or dimensions of comparison.\n\nThe metrics are likely related to machine learning or artificial intelligence performance, as indicated by the terms like "BLIP-2," "Instruct BLIP," "POE," "QWA," "V"""
    elif model_id == "liuhaotian/llava-v1.6-vicuna-13b": expected_text = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER:  \nWhat is shown in this image? ASSISTANT: The image appears to be a radar chart, also known as a spider chart or star chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.\n\nIn this particular radar chart, there are several variables represented:\n\n- MM-Vet\n- LLa-Va-Bench\n- SEED-Bench\n- MM"
    elif model_id == "liuhaotian/llava-v1.6-34b": expected_text = "<|im_start|> system\nAnswer the questions. <|im_start|> user\n\nWhat is shown in this image? <|im_start|> assistant\nThe image appears to be a radar chart, also known as a spider chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point.\n\nIn this particular chart, there are several datasets represented by different colors and labeled with various acronyms such as MM-Vet, LLaVA-Bench, SEED-Bench, MM-Bench-CN, MM-"
    elif model_id == "lmms-lab/llama3-llava-next-8b": expected_text = 'system\n\nYou are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.user\n\n\nWhat is shown in this image?assistant\n\n\nThe image shows a radar chart, also known as a spider chart or a web chart, which is a type of graph used to display multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the values are plotted along each axis and connected to form a polygon.\n\nIn this particular radar chart, there are several axes labeled with different variables, such as "MM-Vet," "LL'
    elif model_id == "lmms-lab/llava-next-72b": expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image displays a radar chart, also known as a spider chart or a star chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. Each axis represents a different variable, and the value of each variable is represented by the distance from the center of the chart to the point where the axis intersects with the line representing that variable's value.\n\nIn this particular chart, there are several axes"
    elif model_id == "lmms-lab/llava-next-110b": expected_text = "system\nYou are a helpful assistant.\nuser\n\nWhat is shown in this image?\nassistant\nThe image shows a radar chart comparing the performance of different models on various visual question answering (VQA) benchmarks. Each colored line represents a different model, and the distance from the center of the chart indicates the score or performance level of the model on a particular benchmark. The benchmarks are labeled around the edges of the chart, and include VQA v2, GQA, VizWiz, TextVQA, MMBench-CN, MME, and others. The chart allows for a"
    else: raise ValueError(f"Model {model_id} not supported")
    assert generated_text == expected_text
    print("Generated text is ok!")
    print("Batched generation...")
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    cats_image = Image.open(requests.get(url, stream=True).raw)
    inputs = processor(images=[image, cats_image], text=[prompt, prompt], padding=True, return_tensors="pt").to(device)
    for k, v in inputs.items(): print(k, v.shape)
    print("Image sizes:", inputs.image_sizes)
    inputs.image_sizes[1] = inputs.image_sizes[0]
    print("Batched generation...")
    output_ids = model.generate(**inputs, max_new_tokens=20, use_cache=True)
    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    print(outputs)
    if push_to_hub:
        checkpoint_name = model_id.split("/")[-1]
        print(f"Pushing to repo llava-hf/{checkpoint_name}-hf")
        model.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
        processor.push_to_hub(f"llava-hf/{checkpoint_name}-hf")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_id", help="Hub location of the model to convert", default="liuhaotian/llava-v1.6-mistral-7b", choices=["liuhaotian/llava-v1.6-mistral-7b",
    "liuhaotian/llava-v1.6-vicuna-7b", "liuhaotian/llava-v1.6-vicuna-13b", "liuhaotian/llava-v1.6-34b", "lmms-lab/llama3-llava-next-8b", "lmms-lab/llava-next-72b", "lmms-lab/llava-next-110b"], required=False)
    parser.add_argument("--pytorch_dump_folder_path", type=str, required=True, 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.                                                                 #
    #################################################################################################################################################################################################
"""
