"""
    #################################################################################################################################################################################################
    # 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 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 sapiens_transformers import SiglipConfig, SiglipImageProcessor, SiglipModel, SiglipProcessor, SiglipTokenizer
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
model_name_to_checkpoint = {'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',
'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',
'siglip-base-patch16-256-i18n': '/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz', '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
    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 = []
    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"))
    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"))
    rename_keys.append(("params/t", "logit_scale"))
    rename_keys.append(("params/b", "logit_bias"))
    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):
    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)
    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))
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):
    config = get_siglip_config(model_name)
    checkpoint = model_name_to_checkpoint[model_name]
    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"
    data = load(checkpoint)
    state_dict = flatten_nested_dict(data)
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys: rename_key(state_dict, src, dest, config)
    read_in_q_k_v_head(state_dict, config)
    model = SiglipModel(config).eval()
    model.load_state_dict(state_dict)
    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)
    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")
    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())
    with torch.no_grad(): outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values)
    print(outputs.logits_per_image[:3, :3])
    probs = torch.sigmoid(outputs.logits_per_image)
    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()
    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 HF hub.")
    args = parser.parse_args()
    convert_siglip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, 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.                                                                 #
    #################################################################################################################################################################################################
"""
