"""
    #################################################################################################################################################################################################
    # 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 collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from sapiens_transformers import (ConditionalDetrConfig, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrImageProcessor)
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
rename_keys = []
for i in range(6):
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight"))
    rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias"))
    rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight"))
    rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias"))
    rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight"))
    rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias"))
    rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight"))
    rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias"))
    rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight"))
    rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight", f"decoder.layers.{i}.encoder_attn.out_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias", f"decoder.layers.{i}.encoder_attn.out_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias"))
    rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias"))
rename_keys.extend([("input_proj.weight", "input_projection.weight"), ("input_proj.bias", "input_projection.bias"), ("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"), ("transformer.decoder.norm.bias", "decoder.layernorm.bias"), ("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"), ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"), ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"), ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"), ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"), ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"), ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"), ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"), ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"), ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias")])
def rename_key(state_dict, old, new):
    val = state_dict.pop(old)
    state_dict[new] = val
def rename_backbone_keys(state_dict):
    new_state_dict = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model")
            new_state_dict[new_key] = value
        else: new_state_dict[key] = value
    return new_state_dict
def read_in_q_k_v(state_dict, is_panoptic=False):
    prefix = ""
    if is_panoptic: prefix = "conditional_detr."
    for i in range(6):
        in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
        in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias")
        state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :]
        state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256]
        state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :]
        state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512]
        state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :]
        state_dict[f"encoder.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_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path):
    config = ConditionalDetrConfig()
    if "resnet101" in model_name: config.backbone = "resnet101"
    if "dc5" in model_name: config.dilation = True
    is_panoptic = "panoptic" in model_name
    if is_panoptic: config.num_labels = 250
    else:
        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()}
    format = "coco_panoptic" if is_panoptic else "coco_detection"
    image_processor = ConditionalDetrImageProcessor(format=format)
    img = prepare_img()
    encoding = image_processor(images=img, return_tensors="pt")
    pixel_values = encoding["pixel_values"]
    logger.info(f"Converting model {model_name}...")
    conditional_detr = torch.hub.load("DeppMeng/ConditionalDETR", model_name, pretrained=True).eval()
    state_dict = conditional_detr.state_dict()
    for src, dest in rename_keys:
        if is_panoptic: src = "conditional_detr." + src
        rename_key(state_dict, src, dest)
    state_dict = rename_backbone_keys(state_dict)
    read_in_q_k_v(state_dict, is_panoptic=is_panoptic)
    prefix = "conditional_detr.model." if is_panoptic else "model."
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (key.startswith("conditional_detr") and not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor")):
                val = state_dict.pop(key)
                state_dict["conditional_detr.model" + key[4:]] = val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                val = state_dict.pop(key)
                state_dict["conditional_detr." + key] = val
            elif key.startswith("bbox_attention") or key.startswith("mask_head"): continue
            else:
                val = state_dict.pop(key)
                state_dict[prefix + key] = val
        else:
            if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"):
                val = state_dict.pop(key)
                state_dict[prefix + key] = val
    model = ConditionalDetrForSegmentation(config) if is_panoptic else ConditionalDetrForObjectDetection(config)
    model.load_state_dict(state_dict)
    model.eval()
    model.push_to_hub(repo_id=model_name, organization="DepuMeng", commit_message="Add model")
    original_outputs = conditional_detr(pixel_values)
    outputs = model(pixel_values)
    assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4)
    assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4)
    if is_panoptic: assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4)
    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 __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_name", default="conditional_detr_resnet50", type=str, help="Name of the CONDITIONAL_DETR model you'd like to convert.")
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model.")
    args = parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
