"""
    #################################################################################################################################################################################################
    # 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 json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from sapiens_transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def get_yolos_config(yolos_name: str) -> YolosConfig:
    config = YolosConfig()
    if "yolos_ti" in yolos_name:
        config.hidden_size = 192
        config.intermediate_size = 768
        config.num_hidden_layers = 12
        config.num_attention_heads = 3
        config.image_size = [800, 1333]
        config.use_mid_position_embeddings = False
    elif yolos_name == "yolos_s_dWr":
        config.hidden_size = 330
        config.num_hidden_layers = 14
        config.num_attention_heads = 6
        config.intermediate_size = 1320
    elif "yolos_s" in yolos_name:
        config.hidden_size = 384
        config.intermediate_size = 1536
        config.num_hidden_layers = 12
        config.num_attention_heads = 6
    elif "yolos_b" in yolos_name: config.image_size = [800, 1344]
    config.num_labels = 91
    repo_id = "huggingface/label-files"
    filename = "coco-detection-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}
    return config
def read_in_q_k_v(state_dict: dict, config: YolosConfig, base_model: bool = False):
    for i in range(config.num_hidden_layers):
        in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight")
        in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias")
        state_dict[f"encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :]
        state_dict[f"encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
        state_dict[f"encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[config.hidden_size : config.hidden_size * 2, :]
        state_dict[f"encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[config.hidden_size : config.hidden_size * 2]
        state_dict[f"encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-config.hidden_size :, :]
        state_dict[f"encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
def rename_key(name: str) -> str:
    if "backbone" in name: name = name.replace("backbone", "vit")
    if "cls_token" in name: name = name.replace("cls_token", "embeddings.cls_token")
    if "det_token" in name: name = name.replace("det_token", "embeddings.detection_tokens")
    if "mid_pos_embed" in name: name = name.replace("mid_pos_embed", "encoder.mid_position_embeddings")
    if "pos_embed" in name: name = name.replace("pos_embed", "embeddings.position_embeddings")
    if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection")
    if "blocks" in name: name = name.replace("blocks", "encoder.layer")
    if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense")
    if "attn" in name: name = name.replace("attn", "attention.self")
    if "norm1" in name: name = name.replace("norm1", "layernorm_before")
    if "norm2" in name: name = name.replace("norm2", "layernorm_after")
    if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense")
    if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense")
    if "class_embed" in name: name = name.replace("class_embed", "class_labels_classifier")
    if "bbox_embed" in name: name = name.replace("bbox_embed", "bbox_predictor")
    if "vit.norm" in name: name = name.replace("vit.norm", "vit.layernorm")
    return name
def convert_state_dict(orig_state_dict: dict, model: YolosForObjectDetection) -> dict:
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)
        if "qkv" in key:
            key_split = key.split(".")
            layer_num = int(key_split[2])
            dim = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
            if "weight" in key:
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.weight"] = val[:dim, :]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.weight"] = val[-dim:, :]
            else:
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.query.bias"] = val[:dim]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2]
                orig_state_dict[f"vit.encoder.layer.{layer_num}.attention.attention.value.bias"] = val[-dim:]
        else: orig_state_dict[rename_key(key)] = val
    return orig_state_dict
def prepare_img() -> torch.Tensor:
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im
@torch.no_grad()
def convert_yolos_checkpoint(yolos_name: str, checkpoint_path: str, pytorch_dump_folder_path: str, push_to_hub: bool = False):
    config = get_yolos_config(yolos_name)
    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
    model = YolosForObjectDetection(config)
    model.eval()
    new_state_dict = convert_state_dict(state_dict, model)
    model.load_state_dict(new_state_dict)
    size = 800 if yolos_name != "yolos_ti" else 512
    image_processor = YolosImageProcessor(format="coco_detection", size=size)
    encoding = image_processor(images=prepare_img(), return_tensors="pt")
    outputs = model(**encoding)
    logits, pred_boxes = outputs.logits, outputs.pred_boxes
    expected_slice_logits, expected_slice_boxes = None, None
    if yolos_name == "yolos_ti":
        expected_slice_logits = torch.tensor([[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]])
        expected_slice_boxes = torch.tensor([[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]])
    elif yolos_name == "yolos_s_200_pre":
        expected_slice_logits = torch.tensor([[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]])
        expected_slice_boxes = torch.tensor([[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]])
    elif yolos_name == "yolos_s_300_pre":
        expected_slice_logits = torch.tensor([[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]])
        expected_slice_boxes = torch.tensor([[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]])
    elif yolos_name == "yolos_s_dWr":
        expected_slice_logits = torch.tensor([[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]])
        expected_slice_boxes = torch.tensor([[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]])
    elif yolos_name == "yolos_base":
        expected_slice_logits = torch.tensor([[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]])
        expected_slice_boxes = torch.tensor([[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]])
    else: raise ValueError(f"Unknown yolos_name: {yolos_name}")
    assert torch.allclose(logits[0, :3, :3], expected_slice_logits, atol=1e-4)
    assert torch.allclose(pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)
    print(f"Saving image processor to {pytorch_dump_folder_path}")
    image_processor.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        model_mapping = {'yolos_ti': 'yolos-tiny', 'yolos_s_200_pre': 'yolos-small', 'yolos_s_300_pre': 'yolos-small-300', 'yolos_s_dWr': 'yolos-small-dwr', 'yolos_base': 'yolos-base'}
        print("Pushing to the hub...")
        model_name = model_mapping[yolos_name]
        image_processor.push_to_hub(model_name, organization="hustvl")
        model.push_to_hub(model_name, organization="hustvl")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--yolos_name", default="yolos_s_200_pre", type=str, help=("Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre', 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."))
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file).")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, 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_yolos_checkpoint(args.yolos_name, args.checkpoint_path, 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.                                                                 #
    #################################################################################################################################################################################################
"""
