

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


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





def 				__a							(       ):
    SCREAMING_SNAKE_CASE__         =    DetaConfig(
        num_queries=900			,							encoder_ffn_dim=2048			,							decoder_ffn_dim=2048			,							num_feature_levels=5			,							assign_first_stage=__A			,							with_box_refine=__A			,							two_stage=__A			,							)

    # set labels
    SCREAMING_SNAKE_CASE__         =    91
    SCREAMING_SNAKE_CASE__         =    '''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__         =    '''coco-detection-id2label.json'''
    SCREAMING_SNAKE_CASE__         =    json.load(open(cached_download(hf_hub_url(__A			,							__A			,							repo_type="dataset"						)						)			,							"r"						)						)
    SCREAMING_SNAKE_CASE__         =    {int(__A						): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE__         =    idalabel
    SCREAMING_SNAKE_CASE__         =    {v: k for k, v in idalabel.items()}

    return config





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    []

    # stem
    # fmt: off
    rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")						)
    rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")						)
    rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")						)
    rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")						)
    rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")						)
    # stages
    for stage_idx in range(len(config.backbone_config.depths						)						):
        for layer_idx in range(config.backbone_config.depths[stage_idx]						):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
                    )						)
            # 3 convs
            for i in range(3						):
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
                    )						)
                rename_keys.append(
                    (
                        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
                        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
                    )						)
    # transformer encoder
    for i in range(config.encoder_layers						):
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")						)
        rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")						)

    # transformer decoder
    for i in range(config.decoder_layers						):
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")						)
        rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")						)

    # fmt: on

    return rename_keys





def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    dct.pop(__A						)
    SCREAMING_SNAKE_CASE__         =    val





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    config.d_model
    for i in range(config.decoder_layers						):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE__         =    state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"						)
        SCREAMING_SNAKE_CASE__         =    state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias"						)
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE__         =    in_proj_weight[:hidden_size, :]
        SCREAMING_SNAKE_CASE__         =    in_proj_bias[:hidden_size]
        SCREAMING_SNAKE_CASE__         =    in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE__         =    in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__         =    in_proj_weight[-hidden_size:, :]
        SCREAMING_SNAKE_CASE__         =    in_proj_bias[-hidden_size:]





def 				__a							(       ):
    SCREAMING_SNAKE_CASE__         =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__         =    Image.open(requests.get(__A			,							stream=__A						).raw						)

    return im





@torch.no_grad()
def 				__a							(       __A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    get_deta_config()

    # load original state dict
    if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__         =    '''adet_checkpoint0011.pth'''
    elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__         =    '''adet_2x_checkpoint0023.pth'''
    else:
        raise ValueError(f"Model name {model_name} not supported"						)
    SCREAMING_SNAKE_CASE__         =    hf_hub_download(repo_id="nielsr/deta-checkpoints"			,							filename=__A						)
    SCREAMING_SNAKE_CASE__         =    torch.load(__A			,							map_location="cpu"						)['''model''']

    # rename keys
    SCREAMING_SNAKE_CASE__         =    create_rename_keys(__A						)
    for src, dest in rename_keys:
        rename_key(__A			,							__A			,							__A						)
    read_in_decoder_q_k_v(__A			,							__A						)

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            SCREAMING_SNAKE_CASE__         =    state_dict.pop(__A						)
            SCREAMING_SNAKE_CASE__         =    val
        if "input_proj" in key:
            SCREAMING_SNAKE_CASE__         =    state_dict.pop(__A						)
            SCREAMING_SNAKE_CASE__         =    val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            SCREAMING_SNAKE_CASE__         =    state_dict.pop(__A						)
            SCREAMING_SNAKE_CASE__         =    val

    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE__         =    DetaForObjectDetection(__A						)
    model.load_state_dict(__A						)
    model.eval()

    SCREAMING_SNAKE_CASE__         =    '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(__A						)

    # load image processor
    SCREAMING_SNAKE_CASE__         =    DetaImageProcessor(format="coco_detection"						)

    # verify our conversion on image
    SCREAMING_SNAKE_CASE__         =    prepare_img()
    SCREAMING_SNAKE_CASE__         =    processor(images=__A			,							return_tensors="pt"						)
    SCREAMING_SNAKE_CASE__         =    encoding['''pixel_values''']
    SCREAMING_SNAKE_CASE__         =    model(pixel_values.to(__A						)						)

    # verify logits
    if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE__         =    torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]						)
        SCREAMING_SNAKE_CASE__         =    torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]						)
    elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE__         =    torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]						)
        SCREAMING_SNAKE_CASE__         =    torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]						)

    assert torch.allclose(outputs.logits[0, :3, :3]			,							expected_logits.to(__A						)			,							atol=1E-4						)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,							expected_boxes.to(__A						)			,							atol=1E-4						)
    print("Everything ok!"						)

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."						)
        Path(__A						).mkdir(exist_ok=__A						)
        model.save_pretrained(__A						)
        processor.save_pretrained(__A						)

    # Push to hub
    if push_to_hub:
        print("Pushing model and processor to hub..."						)
        model.push_to_hub(f"jozhang97/{model_name}"						)
        processor.push_to_hub(f"jozhang97/{model_name}"						)


if __name__ == "__main__":
  A__										=      argparse.ArgumentParser()

  parser.add_argument(
      "--model_name",
      type=str,
      default="deta-resnet-50",
      choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
      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 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 🤗 hub."
  )
  A__										=      parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










__lowerCamelCase						= {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase						= {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    List[Any]			=						True
							_a  :    Optional[Any]			=						[]

							for neighbour in graph[vert]:
														if not visited[neighbour]:
																					connected_verts += dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )

							return [vert, *connected_verts]






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    Optional[int]			=						len(UpperCAmelCase     )
							_a  :    Optional[Any]			=						graph_size * [False]
							_a  :    Optional[int]			=						[]

							for i in range(UpperCAmelCase     ):
														if not visited[i]:
																					_a  :    List[str]			=						dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					components_list.append(UpperCAmelCase     )

							return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()