"""
    #################################################################################################################################################################################################
    # 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 DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
def rename_key(orig_key):
    if "backbone.0.body" in orig_key: orig_key = orig_key.replace("backbone.0.body", "backbone.conv_encoder.model")
    if "transformer" in orig_key: orig_key = orig_key.replace("transformer.", "")
    if "norm1" in orig_key:
        if "encoder" in orig_key: orig_key = orig_key.replace("norm1", "self_attn_layer_norm")
        else: orig_key = orig_key.replace("norm1", "encoder_attn_layer_norm")
    if "norm2" in orig_key:
        if "encoder" in orig_key: orig_key = orig_key.replace("norm2", "final_layer_norm")
        else: orig_key = orig_key.replace("norm2", "self_attn_layer_norm")
    if "norm3" in orig_key: orig_key = orig_key.replace("norm3", "final_layer_norm")
    if "linear1" in orig_key: orig_key = orig_key.replace("linear1", "fc1")
    if "linear2" in orig_key: orig_key = orig_key.replace("linear2", "fc2")
    if "query_embed" in orig_key: orig_key = orig_key.replace("query_embed", "query_position_embeddings")
    if "cross_attn" in orig_key: orig_key = orig_key.replace("cross_attn", "encoder_attn")
    return orig_key
def read_in_q_k_v(state_dict):
    for i in range(6):
        in_proj_weight = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight")
        in_proj_bias = state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_bias")
        state_dict[f"decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
        state_dict[f"decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
        state_dict[f"decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
        state_dict[f"decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
        state_dict[f"decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
        state_dict[f"decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:]
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im
@torch.no_grad()
def convert_deformable_detr_checkpoint(checkpoint_path, single_scale, dilation, with_box_refine, two_stage, pytorch_dump_folder_path, push_to_hub):
    config = DeformableDetrConfig()
    if single_scale: config.num_feature_levels = 1
    config.dilation = dilation
    config.with_box_refine = with_box_refine
    config.two_stage = two_stage
    config.num_labels = 91
    repo_id = "huggingface/label-files"
    filename = "coco-detection-id2label.json"
    id2label = json.loads(Path(hf_hub_download(repo_id, filename, repo_type="dataset")).read_text())
    id2label = {int(k): v for k, v in id2label.items()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}
    image_processor = DeformableDetrImageProcessor(format="coco_detection")
    img = prepare_img()
    encoding = image_processor(images=img, return_tensors="pt")
    pixel_values = encoding["pixel_values"]
    logger.info("Converting model...")
    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        state_dict[rename_key(key)] = val
    read_in_q_k_v(state_dict)
    prefix = "model."
    for key in state_dict.copy().keys():
        if not key.startswith("class_embed") and not key.startswith("bbox_embed"):
            val = state_dict.pop(key)
            state_dict[prefix + key] = val
    model = DeformableDetrForObjectDetection(config)
    model.load_state_dict(state_dict)
    model.eval()
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    outputs = model(pixel_values.to(device))
    expected_logits = torch.tensor([[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]])
    expected_boxes = torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]])
    if single_scale:
        expected_logits = torch.tensor([[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]])
        expected_boxes = torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]])
    if single_scale and dilation:
        expected_logits = torch.tensor([[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]])
        expected_boxes = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]])
    if with_box_refine:
        expected_logits = torch.tensor([[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]])
        expected_boxes = torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]])
    if with_box_refine and two_stage:
        expected_logits = torch.tensor([[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -1.035, -6.2298]])
        expected_boxes = torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]])
    print("Logits:", outputs.logits[0, :3, :3])
    assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4)
    print("Everything ok!")
    logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...")
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    model.save_pretrained(pytorch_dump_folder_path)
    image_processor.save_pretrained(pytorch_dump_folder_path)
    if push_to_hub:
        model_name = "deformable-detr"
        model_name += "-single-scale" if single_scale else ""
        model_name += "-dc5" if dilation else ""
        model_name += "-with-box-refine" if with_box_refine else ""
        model_name += "-two-stage" if two_stage else ""
        print("Pushing model to hub...")
        model.push_to_hub(repo_path_or_name=model_name, organization="nielsr", commit_message="Add model")
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint_path", type=str, default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth", help="Path to Pytorch checkpoint (.pth file) you'd like to convert.")
    parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
    parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
    parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
    parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the folder to output PyTorch model.")
    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_deformable_detr_checkpoint(args.checkpoint_path, args.single_scale, args.dilation, args.with_box_refine, args.two_stage, 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.                                                                 #
    #################################################################################################################################################################################################
"""
