'''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()
UpperCAmelCase							   =      logging.get_logger(__name__)







def 	UpperCAmelCase       (		)	->	List[str]:
     a  :    Optional[Any]				=						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=snake_case__						,      with_box_refine=snake_case__						,      two_stage=snake_case__						,      )

     # set labels
     a  :    int				=						9_1
     a  :    Tuple				=						'huggingface/label-files'
     a  :    List[str]				=						'coco-detection-id2label.json'
     a  :    int				=						json.load(open(cached_download(hf_hub_url(snake_case__						,      snake_case__						,      repo_type='''dataset'''       )       )						,      '''r'''       )       )
     a  :    List[Any]				=						{int(snake_case__       ): v for k, v in idalabel.items()}
     a  :    Tuple				=						idalabel
     a  :    str				=						{v: k for k, v in idalabel.items()}

     return config







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Dict:
     a  :    str				=						[]

     # 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 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	List[Any]:
     a  :    int				=						dct.pop(snake_case__       )
     a  :    List[Any]				=						val







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_       )	->	Any:
     # transformer decoder self-attention layers
     a  :    Union[str, Any]				=						config.d_model
     for i in range(config.decoder_layers       ):
          # read in weights + bias of input projection layer of self-attention
          a  :    List[str]				=						state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
          a  :    Tuple				=						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  :    Optional[Any]				=						in_proj_weight[:hidden_size, :]
          a  :    int				=						in_proj_bias[:hidden_size]
          a  :    Optional[Any]				=						in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          a  :    str				=						in_proj_bias[hidden_size : hidden_size * 2]
          a  :    Optional[int]				=						in_proj_weight[-hidden_size:, :]
          a  :    Union[str, Any]				=						in_proj_bias[-hidden_size:]







def 	UpperCAmelCase       (		)	->	List[Any]:
     a  :    Optional[int]				=						'http://images.cocodataset.org/val2017/000000039769.jpg'
     a  :    Union[str, Any]				=						Image.open(requests.get(snake_case__						,      stream=snake_case__       ).raw       )

     return im







@torch.no_grad()
def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	Tuple:
     a  :    Union[str, Any]				=						get_deta_config()

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

     # rename keys
     a  :    Optional[int]				=						create_rename_keys(snake_case__       )
     for src, dest in rename_keys:
          rename_key(snake_case__						,      snake_case__						,      snake_case__       )
     read_in_decoder_q_k_v(snake_case__						,      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:
               a  :    List[str]				=						state_dict.pop(snake_case__       )
               a  :    Optional[Any]				=						val
          if "input_proj" in key:
               a  :    str				=						state_dict.pop(snake_case__       )
               a  :    Tuple				=						val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               a  :    List[Any]				=						state_dict.pop(snake_case__       )
               a  :    Dict				=						val

    # finally, create HuggingFace model and load state dict
     a  :    List[Any]				=						DetaForObjectDetection(snake_case__       )
     model.load_state_dict(snake_case__       )
     model.eval()

     a  :    int				=						'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(snake_case__       )

     # load image processor
     a  :    Optional[Any]				=						DetaImageProcessor(format='''coco_detection'''       )

     # verify our conversion on image
     a  :    Dict				=						prepare_img()
     a  :    List[Any]				=						processor(images=snake_case__						,      return_tensors='''pt'''       )
     a  :    List[Any]				=						encoding['pixel_values']
     a  :    Dict				=						model(pixel_values.to(snake_case__       )       )

     # verify logits
     if model_name == "deta-resnet-50":
          a  :    Dict				=						torch.tensor(
              [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]       )
          a  :    Tuple				=						torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]       )
     elif model_name == "deta-resnet-50-24-epochs":
          a  :    Any				=						torch.tensor(
              [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]       )
          a  :    List[Any]				=						torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]       )

     assert torch.allclose(outputs.logits[0, :3, :3]						,      expected_logits.to(snake_case__       )						,      atol=1e-4       )
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,      expected_boxes.to(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(snake_case__       ).mkdir(exist_ok=snake_case__       )
          model.save_pretrained(snake_case__       )
          processor.save_pretrained(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__":
  UpperCAmelCase							   =      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."""
  )
  UpperCAmelCase							   =      parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
from __future__ import annotations

UpperCAmelCase							   =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self							,     lowerCAmelCase_    )	->None:
        a  :    int				=						-1
        a  :    int				=						-1
        a  :    list[int]				=						[-1] * length
        a  :    Node | None				=						None
        a  :    Node | None				=						None


   def __repr__(    self    )	->str:
        return F"""Node(min_value={self.minn} max_value={self.maxx})"""







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Node | None:
     a  :    Dict				=						Node(len(lowerCAmelCase_       )       )
     a			,							a  :    Tuple				=						min(lowerCAmelCase_       ), max(lowerCAmelCase_       )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     a  :    Optional[Any]				=						(root.minn + root.maxx) // 2

     a  :    list[int]				=						[]
     a  :    list[int]				=						[]

     for index, num in enumerate(lowerCAmelCase_       ):
          if num <= pivot:
               left_arr.append(lowerCAmelCase_       )
          else:
               right_arr.append(lowerCAmelCase_       )
          a  :    int				=						len(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     a  :    List[str]				=						build_tree(lowerCAmelCase_       )
     return root







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     a  :    List[Any]				=						(node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left						,      lowerCAmelCase_						,      node.map_left[index] - 1       )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right						,      lowerCAmelCase_						,      index - node.map_left[index]       )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if start > end:
          return 0
     a  :    Optional[Any]				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )
     a  :    Any				=						rank_till_index(lowerCAmelCase_						,      lowerCAmelCase_						,      start - 1       )
     return rank_till_end - rank_before_start







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     a  :    Optional[int]				=						node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left						,      lowerCAmelCase_						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      )
     else:
          return quantile(
              node.right						,      index - num_elements_in_left_tree						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      )







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )	->	int:
     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     a  :    Tuple				=						range_counting(
         node.left						,      (node.map_left[start - 1] if start else 0)						,      node.map_left[end] - 1						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     a  :    int				=						range_counting(
         node.right						,      start - (node.map_left[start - 1] if start else 0)						,      end - node.map_left[end]						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )
     return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()