# Copyright 2024 The HuggingFace Inc. team.
# SPDX-License-Identifier: Apache-2.0

"""Convert SigLIP checkpoints from the original repository.

URL: https://github.com/google-research/big_vision/tree/main
"""

import argparse
import collections
from pathlib import Path

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from numpy import load
from PIL import Image
from transformers import (
    SiglipConfig,
    SiglipImageProcessor,
    SiglipModel,
    SiglipProcessor,
    SiglipTokenizer,
)
from transformers.utils import logging

logging.set_verbosity_info()
logger = logging.get_logger(__name__)


model_name_to_checkpoint = {
    # base checkpoints
    "siglip-base-patch16-224": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_224_63724782.npz",
    "siglip-base-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_256_60500360.npz",
    "siglip-base-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_384_68578854.npz",
    "siglip-base-patch16-512": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_512_68580893.npz",
    # large checkpoints
    "siglip-large-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_256_60552751.npz",
    "siglip-large-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_384_63634585.npz",
    # multilingual checkpoint
    "siglip-base-patch16-256-i18n": "/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz",
    # so400m checkpoints
    "siglip-so400m-patch14-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_so400m_384_58765454.npz",
}

model_name_to_image_size = {
    "siglip-base-patch16-224": 224,
    "siglip-base-patch16-256": 256,
    "siglip-base-patch16-384": 384,
    "siglip-base-patch16-512": 512,
    "siglip-large-patch16-256": 256,
    "siglip-large-patch16-384": 384,
    "siglip-base-patch16-256-i18n": 256,
    "siglip-so400m-patch14-384": 384,
}


def get_siglip_config(model_name):
    config = SiglipConfig()

    vocab_size = 250000 if "i18n" in model_name else 32000
    image_size = model_name_to_image_size[model_name]
    patch_size = 16 if "patch16" in model_name else 14

    # size of the architecture
    config.vision_config.image_size = image_size
    config.vision_config.patch_size = patch_size
    config.text_config.vocab_size = vocab_size

    if "base" in model_name:
        pass
    elif "large" in model_name:
        config.text_config.hidden_size = 1024
        config.text_config.intermediate_size = 4096
        config.text_config.num_hidden_layers = 24
        config.text_config.num_attention_heads = 16
        config.vision_config.hidden_size = 1024
        config.vision_config.intermediate_size = 4096
        config.vision_config.num_hidden_layers = 24
        config.vision_config.num_attention_heads = 16
    elif "so400m" in model_name:
        config.text_config.hidden_size = 1152
        config.text_config.intermediate_size = 4304
        config.text_config.num_hidden_layers = 27
        config.text_config.num_attention_heads = 16
        config.vision_config.hidden_size = 1152
        config.vision_config.intermediate_size = 4304
        config.vision_config.num_hidden_layers = 27
        config.vision_config.num_attention_heads = 16
    else:
        raise ValueError("Model not supported")

    return config


def create_rename_keys(config):
    rename_keys = []
    # fmt: off

    # vision encoder

    rename_keys.append(("params/img/embedding/kernel", "vision_model.embeddings.patch_embedding.weight"))
    rename_keys.append(("params/img/embedding/bias", "vision_model.embeddings.patch_embedding.bias"))
    rename_keys.append(("params/img/pos_embedding", "vision_model.embeddings.position_embedding.weight"))

    for i in range(config.vision_config.num_hidden_layers):
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/scale", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/scale", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"vision_model.encoder.layers.{i}.self_attn.k_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"vision_model.encoder.layers.{i}.self_attn.k_proj.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"vision_model.encoder.layers.{i}.self_attn.v_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"vision_model.encoder.layers.{i}.self_attn.v_proj.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"vision_model.encoder.layers.{i}.self_attn.q_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"vision_model.encoder.layers.{i}.self_attn.q_proj.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"vision_model.encoder.layers.{i}.self_attn.out_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"vision_model.encoder.layers.{i}.self_attn.out_proj.bias"))

    rename_keys.append(("params/img/Transformer/encoder_norm/scale", "vision_model.post_layernorm.weight"))
    rename_keys.append(("params/img/Transformer/encoder_norm/bias", "vision_model.post_layernorm.bias"))

    rename_keys.append(("params/img/MAPHead_0/probe", "vision_model.head.probe"))
    rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/scale", "vision_model.head.layernorm.weight"))
    rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/bias", "vision_model.head.layernorm.bias"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/kernel", "vision_model.head.mlp.fc1.weight"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/bias", "vision_model.head.mlp.fc1.bias"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/kernel", "vision_model.head.mlp.fc2.weight"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/bias", "vision_model.head.mlp.fc2.bias"))
    rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/kernel", "vision_model.head.attention.out_proj.weight"))
    rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/bias", "vision_model.head.attention.out_proj.bias"))

    # text encoder

    rename_keys.append(("params/txt/Embed_0/embedding", "text_model.embeddings.token_embedding.weight"))
    rename_keys.append(("params/txt/pos_embedding", "text_model.embeddings.position_embedding.weight"))

    for i in range(config.text_config.num_hidden_layers):
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/scale", f"text_model.encoder.layers.{i}.layer_norm1.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/bias", f"text_model.encoder.layers.{i}.layer_norm1.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/scale", f"text_model.encoder.layers.{i}.layer_norm2.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/bias", f"text_model.encoder.layers.{i}.layer_norm2.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"text_model.encoder.layers.{i}.mlp.fc1.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"text_model.encoder.layers.{i}.mlp.fc1.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"text_model.encoder.layers.{i}.mlp.fc2.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"text_model.encoder.layers.{i}.mlp.fc2.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"text_model.encoder.layers.{i}.self_attn.k_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"text_model.encoder.layers.{i}.self_attn.k_proj.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"text_model.encoder.layers.{i}.self_attn.v_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"text_model.encoder.layers.{i}.self_attn.v_proj.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"text_model.encoder.layers.{i}.self_attn.q_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"text_model.encoder.layers.{i}.self_attn.q_proj.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"text_model.encoder.layers.{i}.self_attn.out_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"text_model.encoder.layers.{i}.self_attn.out_proj.bias"))

    rename_keys.append(("params/txt/Encoder_0/encoder_norm/scale", "text_model.final_layer_norm.weight"))
    rename_keys.append(("params/txt/Encoder_0/encoder_norm/bias", "text_model.final_layer_norm.bias"))
    rename_keys.append(("params/txt/head/kernel", "text_model.head.weight"))
    rename_keys.append(("params/txt/head/bias", "text_model.head.bias"))

    # learned temperature and bias
    rename_keys.append(("params/t", "logit_scale"))
    rename_keys.append(("params/b", "logit_bias"))

    # fmt: on
    return rename_keys


def rename_key(dct, old, new, config):
    val = dct.pop(old)

    if (
        "out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new
    ) and "vision" in new:
        val = val.reshape(-1, config.vision_config.hidden_size)
    if (
        "out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new
    ) and "text" in new:
        val = val.reshape(-1, config.text_config.hidden_size)

    if "patch_embedding.weight" in new:
        val = val.transpose(3, 2, 0, 1)
    elif (
        new.endswith("weight")
        and "position_embedding" not in new
        and "token_embedding" not in new
    ):
        val = val.T

    if "position_embedding" in new and "vision" in new:
        val = val.reshape(-1, config.vision_config.hidden_size)
    if "position_embedding" in new and "text" in new:
        val = val.reshape(-1, config.text_config.hidden_size)

    if new.endswith("bias"):
        val = val.reshape(-1)

    dct[new] = torch.from_numpy(val)


def read_in_q_k_v_head(state_dict, config):
    # read in individual input projection layers
    key_proj_weight = (
        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/kernel")
        .reshape(-1, config.vision_config.hidden_size)
        .T
    )
    key_proj_bias = state_dict.pop(
        "params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/bias"
    ).reshape(-1)
    value_proj_weight = (
        state_dict.pop(
            "params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/kernel"
        )
        .reshape(-1, config.vision_config.hidden_size)
        .T
    )
    value_proj_bias = state_dict.pop(
        "params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/bias"
    ).reshape(-1)
    query_proj_weight = (
        state_dict.pop(
            "params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/kernel"
        )
        .reshape(-1, config.vision_config.hidden_size)
        .T
    )
    query_proj_bias = state_dict.pop(
        "params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/bias"
    ).reshape(-1)

    # next, add them to the state dict as a single matrix + vector
    state_dict["vision_model.head.attention.in_proj_weight"] = torch.from_numpy(
        np.concatenate([query_proj_weight, key_proj_weight, value_proj_weight], axis=0)
    )
    state_dict["vision_model.head.attention.in_proj_bias"] = torch.from_numpy(
        np.concatenate([query_proj_bias, key_proj_bias, value_proj_bias], axis=0)
    )


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


def flatten_nested_dict(params, parent_key="", sep="/"):
    items = []

    for k, v in params.items():
        new_key = parent_key + sep + k if parent_key else k

        if isinstance(v, collections.abc.MutableMapping):
            items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
        else:
            items.append((new_key, v))
    return dict(items)


@torch.no_grad()
def convert_siglip_checkpoint(
    model_name, pytorch_dump_folder_path, verify_logits=True, push_to_hub=False
):
    """
    Copy/paste/tweak model's weights to our SigLIP structure.
    """

    # define default SigLIP configuration
    config = get_siglip_config(model_name)

    # get checkpoint
    checkpoint = model_name_to_checkpoint[model_name]

    # get vocab file
    if "i18n" in model_name:
        vocab_file = (
            "/Users/nielsrogge/Documents/SigLIP/multilingual_vocab/sentencepiece.model"
        )
    else:
        vocab_file = (
            "/Users/nielsrogge/Documents/SigLIP/english_vocab/sentencepiece.model"
        )

    # load original state dict
    data = load(checkpoint)
    state_dict = flatten_nested_dict(data)

    # remove and rename some keys
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest, config)

    # qkv matrices of attention pooling head need special treatment
    read_in_q_k_v_head(state_dict, config)

    # load HuggingFace model
    model = SiglipModel(config).eval()
    model.load_state_dict(state_dict)

    # create processor
    # important: make tokenizer not return attention_mask since original one doesn't require it
    image_size = config.vision_config.image_size
    size = {"height": image_size, "width": image_size}
    image_processor = SiglipImageProcessor(size=size)
    tokenizer = SiglipTokenizer(vocab_file=vocab_file, model_input_names=["input_ids"])
    processor = SiglipProcessor(image_processor=image_processor, tokenizer=tokenizer)

    # verify on dummy images and texts
    url_1 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-ipod.jpg"
    image_1 = Image.open(requests.get(url_1, stream=True).raw).convert("RGB")
    url_2 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-blank.jpg"
    image_2 = Image.open(requests.get(url_2, stream=True).raw).convert("RGB")
    texts = ["an apple", "a picture of an apple"]

    inputs = processor(
        images=[image_1, image_2], text=texts, return_tensors="pt", padding="max_length"
    )

    # verify input_ids against original ones
    if image_size == 224:
        filename = "siglip_pixel_values.pt"
    elif image_size == 256:
        filename = "siglip_pixel_values_256.pt"
    elif image_size == 384:
        filename = "siglip_pixel_values_384.pt"
    elif image_size == 512:
        filename = "siglip_pixel_values_512.pt"
    else:
        raise ValueError("Image size not supported")

    filepath = hf_hub_download(
        repo_id="nielsr/test-image", filename=filename, repo_type="dataset"
    )
    original_pixel_values = torch.load(filepath)
    filepath = hf_hub_download(
        repo_id="nielsr/test-image", filename="siglip_input_ids.pt", repo_type="dataset"
    )
    original_input_ids = torch.load(filepath)

    if "i18n" not in model_name:
        assert inputs.input_ids.tolist() == original_input_ids.tolist()

    print("Mean of original pixel values:", original_pixel_values.mean())
    print("Mean of new pixel values:", inputs.pixel_values.mean())

    # note: we're testing with original pixel values here since we don't have exact pixel values
    with torch.no_grad():
        outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values)

    # with torch.no_grad():
    #     outputs = model(input_ids=inputs.input_ids, pixel_values=inputs.pixel_values)

    print(outputs.logits_per_image[:3, :3])

    probs = torch.sigmoid(outputs.logits_per_image)  # these are the probabilities
    print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
    print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")

    if verify_logits:
        if model_name == "siglip-base-patch16-224":
            expected_slice = torch.tensor(
                [[-2.9621, -2.1672], [-0.2713, 0.2910]],
            )
        elif model_name == "siglip-base-patch16-256":
            expected_slice = torch.tensor(
                [[-3.1146, -1.9894], [-0.7312, 0.6387]],
            )
        elif model_name == "siglip-base-patch16-384":
            expected_slice = torch.tensor(
                [[-2.8098, -2.1891], [-0.4242, 0.4102]],
            )
        elif model_name == "siglip-base-patch16-512":
            expected_slice = torch.tensor(
                [[-2.7899, -2.2668], [-0.4295, -0.0735]],
            )
        elif model_name == "siglip-large-patch16-256":
            expected_slice = torch.tensor(
                [[-1.5827, -0.5801], [-0.9153, 0.1363]],
            )
        elif model_name == "siglip-large-patch16-384":
            expected_slice = torch.tensor(
                [[-2.1523, -0.2899], [-0.2959, 0.7884]],
            )
        elif model_name == "siglip-so400m-patch14-384":
            expected_slice = torch.tensor([[-1.2441, -0.6649], [-0.7060, 0.7374]])
        elif model_name == "siglip-base-patch16-256-i18n":
            expected_slice = torch.tensor(
                [[-0.9064, 0.1073], [-0.0299, 0.5304]],
            )

        assert torch.allclose(
            outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4
        )
        print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving processor to {pytorch_dump_folder_path}")
        processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        model.push_to_hub(f"nielsr/{model_name}")
        processor.push_to_hub(f"nielsr/{model_name}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="siglip-base-patch16-224",
        type=str,
        choices=model_name_to_checkpoint.keys(),
        help="Name of the model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        help="Path to the output PyTorch model directory.",
    )
    parser.add_argument(
        "--verify_logits",
        action="store_false",
        help="Whether to verify logits against the original implementation.",
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether or not to push the converted model to the 🤗 hub.",
    )

    args = parser.parse_args()
    convert_siglip_checkpoint(
        args.model_name,
        args.pytorch_dump_folder_path,
        args.verify_logits,
        args.push_to_hub,
    )
