
'''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__		:   Any            =       logging.get_logger(__name__)





def        snake_case							(		)->			Any:







      """simple docstring"""
      __A       	=			DetaConfig(
          num_queries=9_0_0      ,  encoder_ffn_dim=2_0_4_8      ,  decoder_ffn_dim=2_0_4_8      ,  num_feature_levels=5      ,  assign_first_stage=__a      ,  with_box_refine=__a      ,  two_stage=__a      ,  )

      # set labels
      __A       	=			9_1
      __A       	=			'''huggingface/label-files'''
      __A       	=			'''coco-detection-id2label.json'''
      __A       	=			json.load(open(cached_download(hf_hub_url(__a      ,  __a      ,  repo_type='dataset'	)	)      ,  'r'	)	)
      __A       	=			{int(__a	): v for k, v in idalabel.items()}
      __A       	=			idalabel
      __A       	=			{v: k for k, v in idalabel.items()}

      return config





def        snake_case							(		UpperCAmelCase	)->			Union[str, Any]:







      """simple docstring"""
      __A       	=			[]

      # 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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			Union[str, Any]:







      """simple docstring"""
      __A       	=			dct.pop(__a	)
      __A       	=			val





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase	)->			Dict:







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





def        snake_case							(		)->			int:







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

      return im





@torch.no_grad()
def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			Optional[Any]:







      """simple docstring"""
      __A       	=			get_deta_config()

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

      # rename keys
      __A       	=			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:
                  __A       	=			state_dict.pop(__a	)
                  __A       	=			val
            if "input_proj" in key:
                  __A       	=			state_dict.pop(__a	)
                  __A       	=			val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  __A       	=			state_dict.pop(__a	)
                  __A       	=			val

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

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

      # load image processor
      __A       	=			DetaImageProcessor(format='coco_detection'	)

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

      # verify logits
      if model_name == "deta-resnet-50":
            __A       	=			torch.tensor(
                [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]	)
            __A       	=			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":
            __A       	=			torch.tensor(
                [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]	)
            __A       	=			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__		:   int            =       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__		:   Tuple            =       parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''





from .stack import Stack





def        snake_case							(		UpperCAmelCase	)->			bool:







      """simple docstring"""
      __A       	=			Stack()
      __A       	=			{'(': ')', '[': ']', '{': '}'}
      for bracket in parentheses:
            if bracket in bracket_pairs:
                  stack.push(UpperCAmelCase	)
            elif bracket in (")", "]", "}"):
                  if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                        return False
      return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 a__		:   int            =       ["((()))", "((())", "(()))"]
 print("Balanced parentheses demonstration:\n")
 for example in examples:
  a__		:   int            =       "" if balanced_parentheses(example) else "not "
  print(f'''{example} is {not_str}balanced''')






