
"""simple docstring"""




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__   :						List[str] 							=     logging.get_logger(__name__)



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    DetaConfig(
         num_queries=900							,     encoder_ffn_dim=2048							,     decoder_ffn_dim=2048							,     num_feature_levels=5							,     assign_first_stage=_SCREAMING_SNAKE_CASE							,     with_box_refine=_SCREAMING_SNAKE_CASE							,     two_stage=_SCREAMING_SNAKE_CASE							,     )

     # set labels
     UpperCAmelCase__				:			List[Any]							=    91
     UpperCAmelCase__				:			Optional[Any]							=    "huggingface/label-files"
     UpperCAmelCase__				:			Optional[int]							=    "coco-detection-id2label.json"
     UpperCAmelCase__				:			List[str]							=    json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     repo_type="dataset"		)		)							,     "r"		)		)
     UpperCAmelCase__				:			List[str]							=    {int(_SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
     UpperCAmelCase__				:			List[Any]							=    idalabel
     UpperCAmelCase__				:			str							=    {v: k for k, v in idalabel.items()}

     return config



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    []

     # 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__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    dct.pop(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase__				:			str							=    val



def 				a__			(					lowerCAmelCase						:							Optional[int]							,     lowerCAmelCase						:							Any		):






     '''simple docstring'''
     # transformer decoder self-attention layers
     UpperCAmelCase__				:			Any							=    config.d_model
     for i in range(config.decoder_layers		):
          # read in weights + bias of input projection layer of self-attention
          UpperCAmelCase__				:			Any							=    state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
          UpperCAmelCase__				:			Optional[Any]							=    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
          UpperCAmelCase__				:			int							=    in_proj_weight[:hidden_size, :]
          UpperCAmelCase__				:			Optional[int]							=    in_proj_bias[:hidden_size]
          UpperCAmelCase__				:			str							=    in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          UpperCAmelCase__				:			Any							=    in_proj_bias[hidden_size : hidden_size * 2]
          UpperCAmelCase__				:			int							=    in_proj_weight[-hidden_size:, :]
          UpperCAmelCase__				:			Any							=    in_proj_bias[-hidden_size:]



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__				:			List[str]							=    Image.open(requests.get(_SCREAMING_SNAKE_CASE							,     stream=_SCREAMING_SNAKE_CASE		).raw		)

     return im



@torch.no_grad()
def 				a__			(					lowerCAmelCase						:							List[Any]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    get_deta_config()

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

     # rename keys
     UpperCAmelCase__				:			str							=    create_rename_keys(_SCREAMING_SNAKE_CASE		)
     for src, dest in rename_keys:
          rename_key(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE		)
     read_in_decoder_q_k_v(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE		)

     # fix some prefixes
     for key in state_dict.copy().keys():
          if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
               UpperCAmelCase__				:			str							=    state_dict.pop(_SCREAMING_SNAKE_CASE		)
               UpperCAmelCase__				:			Any							=    val
          if "input_proj" in key:
               UpperCAmelCase__				:			Union[str, Any]							=    state_dict.pop(_SCREAMING_SNAKE_CASE		)
               UpperCAmelCase__				:			Any							=    val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               UpperCAmelCase__				:			Any							=    state_dict.pop(_SCREAMING_SNAKE_CASE		)
               UpperCAmelCase__				:			Optional[int]							=    val

    # finally, create HuggingFace model and load state dict
     UpperCAmelCase__				:			Optional[int]							=    DetaForObjectDetection(_SCREAMING_SNAKE_CASE		)
     model.load_state_dict(_SCREAMING_SNAKE_CASE		)
     model.eval()

     UpperCAmelCase__				:			List[Any]							=    "cuda" if torch.cuda.is_available() else "cpu"
     model.to(_SCREAMING_SNAKE_CASE		)

     # load image processor
     UpperCAmelCase__				:			Tuple							=    DetaImageProcessor(format="coco_detection"		)

     # verify our conversion on image
     UpperCAmelCase__				:			Dict							=    prepare_img()
     UpperCAmelCase__				:			Tuple							=    processor(images=_SCREAMING_SNAKE_CASE							,     return_tensors="pt"		)
     UpperCAmelCase__				:			Any							=    encoding["pixel_values"]
     UpperCAmelCase__				:			Union[str, Any]							=    model(pixel_values.to(_SCREAMING_SNAKE_CASE		)		)

     # verify logits
     if model_name == "deta-resnet-50":
          UpperCAmelCase__				:			int							=    torch.tensor(
              [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]		)
          UpperCAmelCase__				:			int							=    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":
          UpperCAmelCase__				:			Tuple							=    torch.tensor(
              [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]		)
          UpperCAmelCase__				:			Optional[Any]							=    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(_SCREAMING_SNAKE_CASE		)							,     atol=1E-4		)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,     expected_boxes.to(_SCREAMING_SNAKE_CASE		)							,     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(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
          model.save_pretrained(_SCREAMING_SNAKE_CASE		)
          processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

     # 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__   :						List[str] 							=     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__   :						List[str] 							=     parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



