import argparse
import os
import json

import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel

TEXT = "clip.text"
VISION = "clip.vision"


def k(raw_key: str, arch: str) -> str:
    return raw_key.format(arch=arch)


def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
    if name in (
        "logit_scale",
        "text_model.embeddings.position_ids",
        "vision_model.embeddings.position_ids",
    ):
        return True

    if has_llava and name in ["visual_projection.weight", "vision_model.post_layernorm.weight", "vision_model.post_layernorm.bias"]:
        return True

    if name.startswith("v") and not has_vision:
        return True

    if name.startswith("t") and not has_text:
        return True

    return False


def get_tensor_name(name: str) -> str:
    if "projection" in name:
        return name

    if "mm_projector" in name:
        return name.replace("model.mm_projector", "mm")

    return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")


def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = (
        list(range(ord("!"), ord("~") + 1))
        + list(range(ord("¡"), ord("¬") + 1))
        + list(range(ord("®"), ord("ÿ") + 1))
    )
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


ap = argparse.ArgumentParser(prog="convert_hf_to_gguf.py")
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument("--text-only", action="store_true", required=False,
                help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
                help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
                help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)

# with proper
args = ap.parse_args()


if args.text_only and args.vision_only:
    print("--text-only and --image-only arguments cannot be specified at the same time.")
    exit(1)

if args.use_f32:
    print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")

# output in the same directory as the model if output_dir is None
dir_model = args.model_dir

if args.clip_model_is_vision:
    vocab = None
    tokens = None
else:
    with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
        vocab = json.load(f)
        tokens = [key for key in vocab]

with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
    config = json.load(f)
    if args.clip_model_is_vision:
        v_hparams = config
        t_hparams = None
    else:
        v_hparams = config["vision_config"]
        t_hparams = config["text_config"]

# possible data types
#   ftype == 0 -> float32
#   ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]

ftype = 1
if args.use_f32:
    ftype = 0

if args.clip_model_is_vision:
    model = CLIPVisionModel.from_pretrained(dir_model)
    processor = None
else:
    model = CLIPModel.from_pretrained(dir_model)
    processor = CLIPProcessor.from_pretrained(dir_model)

fname_middle = None
has_text_encoder = True
has_vision_encoder = True
has_llava_projector = False
if args.text_only:
    fname_middle = "text-"
    has_vision_encoder = False
elif args.llava_projector is not None:
    fname_middle = "mmproj-"
    has_text_encoder = False
    has_llava_projector = True
elif args.vision_only:
    fname_middle = "vision-"
    has_text_encoder = False
else:
    fname_middle = ""

output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")

fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_llava_projector", has_llava_projector)
fout.add_file_type(ftype)
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
fout.add_name(model_name)
if args.text_only:
    fout.add_description("text-only CLIP model")
elif args.vision_only and not has_llava_projector:
    fout.add_description("vision-only CLIP model")
elif has_llava_projector:
    fout.add_description("image encoder for LLaVA")
else:
    fout.add_description("two-tower CLIP model")

if has_text_encoder:
    # text_model hparams
    fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
    fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
    fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
    fout.add_token_list(tokens)

if has_vision_encoder:
    # vision_model hparams
    fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
    fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
    fout.add_uint32("clip.vision.projection_dim", v_hparams.get("projection_dim", config["projection_dim"]))
    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
    block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)

    if processor is not None:
        image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
        image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
    else:
        image_mean = args.image_mean if args.image_mean is not None else default_image_mean
        image_std = args.image_std if args.image_std is not None else default_image_std
    fout.add_array("clip.vision.image_mean", image_mean)
    fout.add_array("clip.vision.image_std", image_std)

use_gelu = v_hparams["hidden_act"] == "gelu"
fout.add_bool("clip.use_gelu", use_gelu)


if has_llava_projector:
    model.vision_model.encoder.layers.pop(-1)
    projector = torch.load(args.llava_projector)
    for name, data in projector.items():
        name = get_tensor_name(name)
        if data.ndim == 2:
            data = data.squeeze().numpy().astype(np.float16)
        else:
            data = data.squeeze().numpy().astype(np.float32)

        fout.add_tensor(name, data)

    print("Projector tensors added\n")

state_dict = model.state_dict()
for name, data in state_dict.items():
    if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
        # we don't need this
        print(f"skipping parameter: {name}")
        continue

    name = get_tensor_name(name)
    data = data.squeeze().numpy()

    n_dims = len(data.shape)

    # ftype == 0 -> float32, ftype == 1 -> float16
    ftype_cur = 0
    if n_dims == 4:
        print(f"tensor {name} is always saved in f16")
        data = data.astype(np.float16)
        ftype_cur = 1
    elif ftype == 1:
        if name[-7:] == ".weight" and n_dims == 2:
            print("  Converting to float16")
            data = data.astype(np.float16)
            ftype_cur = 1
        else:
            print("  Converting to float32")
            data = data.astype(np.float32)
            ftype_cur = 0
    else:
        if data.dtype != np.float32:
            print("  Converting to float32")
            data = data.astype(np.float32)
            ftype_cur = 0

    print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
    fout.add_tensor(name, data)


fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()

print("Done. Output file: " + fname_out)