





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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    logging.get_logger(__name__)






def    _A	(   lowerCamelCase ):
   a__						:    Optional[Any]   		=	SwinConfig(
       embed_dim=192				,			depths=(2, 2, 18, 2)				,			num_heads=(6, 12, 24, 48)				,			window_size=12				,			out_features=["stage2", "stage3", "stage4"]				,			)

   a__						:    Dict   		=	DetaConfig(
       backbone_config=lowerCamelCase				,			num_queries=900				,			encoder_ffn_dim=2048				,			decoder_ffn_dim=2048				,			num_feature_levels=5				,			assign_first_stage=lowerCamelCase				,			with_box_refine=lowerCamelCase				,			two_stage=lowerCamelCase				,			)

   # set labels
   a__						:    int   		=	"huggingface/label-files"
   if "o365" in model_name:
      a__						:    List[Any]   		=	366
      a__						:    Optional[Any]   		=	"object365-id2label.json"
   else:
      a__						:    Tuple   		=	91
      a__						:    List[Any]   		=	"coco-detection-id2label.json"

   a__						:    Any   		=	num_labels
   a__						:    Any   		=	json.load(open(cached_download(hf_hub_url(lowerCamelCase				,			lowerCamelCase				,			repo_type="dataset" ) )				,			"r" ) )
   a__						:    int   		=	{int(lowerCamelCase ): v for k, v in idalabel.items()}
   a__						:    Union[str, Any]   		=	idalabel
   a__						:    Dict   		=	{v: k for k, v in idalabel.items()}

   return config






def    _A	(   lowerCamelCase ):
   a__						:    Any   		=	[]

   # stem
   # fmt: off
   rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight") )
   rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias") )
   rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight") )
   rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias") )
   # stages
   for i in range(len(config.backbone_config.depths ) ):
      for j in range(config.backbone_config.depths[i] ):
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )

      if i < 3:
         rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.reduction.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""") )
         rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.bias""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""") )

   rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight") )
   rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias") )
   rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight") )
   rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias") )
   rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight") )
   rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias") )

   # 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				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    List[Any]   		=	dct.pop(lowerCamelCase )
   a__						:    str   		=	val






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    str   		=	[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
   for i in range(len(backbone_config.depths ) ):
      a__						:    List[Any]   		=	num_features[i]
      for j in range(backbone_config.depths[i] ):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         a__						:    List[Any]   		=	state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
         a__						:    List[Any]   		=	state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias""" )
         # next, add query, keys and values (in that order) to the state dict
         a__						:    List[Any]   		=	in_proj_weight[:dim, :]
         a__						:    int   		=	in_proj_bias[: dim]
         a__						:    Optional[Any]   		=	in_proj_weight[
             dim : dim * 2, :
         ]
         a__						:    Dict   		=	in_proj_bias[
             dim : dim * 2
         ]
         a__						:    Dict   		=	in_proj_weight[
             -dim :, :
         ]
         a__						:    Union[str, Any]   		=	in_proj_bias[-dim :]
         # fmt: on






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   # transformer decoder self-attention layers
   a__						:    Dict   		=	config.d_model
   for i in range(config.decoder_layers ):
      # read in weights + bias of input projection layer of self-attention
      a__						:    int   		=	state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
      a__						:    str   		=	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__						:    Union[str, Any]   		=	in_proj_bias[:hidden_size]
      a__						:    Union[str, Any]   		=	in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      a__						:    Dict   		=	in_proj_bias[hidden_size : hidden_size * 2]
      a__						:    str   		=	in_proj_weight[-hidden_size:, :]
      a__						:    int   		=	in_proj_bias[-hidden_size:]






def    _A	(   ):
   a__						:    List[str]   		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
   a__						:    Tuple   		=	Image.open(requests.get(lowerCamelCase				,			stream=lowerCamelCase ).raw )

   return im






@torch.no_grad()
def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    Optional[int]   		=	get_deta_config(lowerCamelCase )

   # load original state dict
   if model_name == "deta-swin-large":
      a__						:    Optional[int]   		=	hf_hub_download(repo_id="nielsr/deta-checkpoints"				,			filename="adet_swin_ft.pth" )
   elif model_name == "deta-swin-large-o365":
      a__						:    int   		=	hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"				,			filename="deta_swin_pt_o365.pth" )
   else:
      raise ValueError(F"""Model name {model_name} not supported""" )

   a__						:    List[Any]   		=	torch.load(lowerCamelCase				,			map_location="cpu" )["model"]

   # original state dict
   for name, param in state_dict.items():
      print(lowerCamelCase				,			param.shape )

   # rename keys
   a__						:    Union[str, Any]   		=	create_rename_keys(lowerCamelCase )
   for src, dest in rename_keys:
      rename_key(lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )
   read_in_swin_q_k_v(lowerCamelCase				,			config.backbone_config )
   read_in_decoder_q_k_v(lowerCamelCase				,			lowerCamelCase )

   # 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__						:    Optional[Any]   		=	state_dict.pop(lowerCamelCase )
         a__						:    List[str]   		=	val
      if "input_proj" in key:
         a__						:    Dict   		=	state_dict.pop(lowerCamelCase )
         a__						:    Union[str, Any]   		=	val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         a__						:    Any   		=	state_dict.pop(lowerCamelCase )
         a__						:    List[str]   		=	val

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

   a__						:    int   		=	"cuda" if torch.cuda.is_available() else "cpu"
   model.to(lowerCamelCase )

   # load image processor
   a__						:    Any   		=	DetaImageProcessor(format="coco_detection" )

   # verify our conversion on image
   a__						:    List[Any]   		=	prepare_img()
   a__						:    List[Any]   		=	processor(images=lowerCamelCase				,			return_tensors="pt" )
   a__						:    List[str]   		=	encoding["pixel_values"]
   a__						:    Tuple   		=	model(pixel_values.to(lowerCamelCase ) )

   # verify logits
   print("Logits:"				,			outputs.logits[0, :3, :3] )
   print("Boxes:"				,			outputs.pred_boxes[0, :3, :3] )
   if model_name == "deta-swin-large":
      a__						:    Dict   		=	torch.tensor(
          [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
      a__						:    Optional[Any]   		=	torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
   elif model_name == "deta-swin-large-o365":
      a__						:    List[Any]   		=	torch.tensor(
          [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
      a__						:    List[Any]   		=	torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
   assert torch.allclose(outputs.logits[0, :3, :3]				,			expected_logits.to(lowerCamelCase )				,			atol=1E-4 )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,			expected_boxes.to(lowerCamelCase )				,			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(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
      model.save_pretrained(lowerCamelCase )
      processor.save_pretrained(lowerCamelCase )

   # 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__":
  SCREAMING_SNAKE_CASE__							:      int         =    argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      type=str,
      default="""deta-swin-large""",
      choices=["""deta-swin-large""", """deta-swin-large-o365"""],
      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."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   for i in range(len(lowerCamelCase ) ):
      if s[i] in open_brackets:
         stack.append(s[i] )

      elif s[i] in closed_brackets and (
          len(lowerCamelCase ) == 0 or (len(lowerCamelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
      ):
         return False

   return len(lowerCamelCase ) == 0






def    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

