
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()
lowercase__          =logging.get_logger(__name__)




def        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ):
 __a			:  Union[str, Any]		    =  SwinConfig(
     embed_dim=1_9_2  ,       depths=(2, 2, 1_8, 2)  ,       num_heads=(6, 1_2, 2_4, 4_8)  ,       window_size=1_2  ,       out_features=['''stage2''', '''stage3''', '''stage4''']  ,       )

 __a			:  Optional[Any]		    =  DetaConfig(
     backbone_config=lowercase__  ,       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=lowercase__  ,       with_box_refine=lowercase__  ,       two_stage=lowercase__  ,       )

 # set labels
 __a			:  Optional[Any]		    =  '''huggingface/label-files'''
 if "o365" in model_name:
  __a			:  Optional[int]		    =  3_6_6
  __a			:  str		    =  '''object365-id2label.json'''
 else:
  __a			:  Any		    =  9_1
  __a			:  Tuple		    =  '''coco-detection-id2label.json'''

 __a			:  List[Any]		    =  num_labels
 __a			:  Optional[int]		    =  json.load(open(cached_download(hf_hub_url(lowercase__  ,       lowercase__  ,       repo_type='''dataset'''  )  )  ,       '''r'''  )  )
 __a			:  List[str]		    =  {int(lowercase__  ): v for k, v in idalabel.items()}
 __a			:  Dict		    =  idalabel
 __a			:  Tuple		    =  {v: k for k, v in idalabel.items()}

 return config




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ):
 __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        __UpperCamelCase					(    lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    str  ):
 __a			:  Union[str, Any]		    =  dct.pop(lowercase__  )
 __a			:  Optional[Any]		    =  val




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[Any]  ):
 __a			:  Dict		    =  [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
 for i in range(len(backbone_config.depths  )  ):
  __a			:  Optional[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			:  Any		    =  state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"  )
   __a			:  Dict		    =  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			:  int		    =  in_proj_weight[:dim, :]
   __a			:  Tuple		    =  in_proj_bias[: dim]
   __a			:  Optional[Any]		    =  in_proj_weight[
       dim : dim * 2, :
   ]
   __a			:  str		    =  in_proj_bias[
       dim : dim * 2
   ]
   __a			:  Dict		    =  in_proj_weight[
       -dim :, :
   ]
   __a			:  str		    =  in_proj_bias[-dim :]
   # fmt: on




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




def        __UpperCamelCase					(    ):
 __a			:  Dict		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __a			:  str		    =  Image.open(requests.get(lowercase__  ,       stream=lowercase__  ).raw  )

 return im




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Dict  ):
 __a			:  str		    =  get_deta_config(lowercase__  )

 # load original state dict
 if model_name == "deta-swin-large":
  __a			:  Tuple		    =  hf_hub_download(repo_id='''nielsr/deta-checkpoints'''  ,       filename='''adet_swin_ft.pth'''  )
 elif model_name == "deta-swin-large-o365":
  __a			:  Tuple		    =  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			:  Optional[int]		    =  torch.load(lowercase__  ,       map_location='''cpu'''  )['''model''']

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

 # rename keys
 __a			:  List[Any]		    =  create_rename_keys(lowercase__  )
 for src, dest in rename_keys:
  rename_key(lowercase__  ,       lowercase__  ,       lowercase__  )
 read_in_swin_q_k_v(lowercase__  ,       config.backbone_config  )
 read_in_decoder_q_k_v(lowercase__  ,       lowercase__  )

 # 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			:  Union[str, Any]		    =  state_dict.pop(lowercase__  )
   __a			:  Tuple		    =  val
  if "input_proj" in key:
   __a			:  List[str]		    =  state_dict.pop(lowercase__  )
   __a			:  int		    =  val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   __a			:  int		    =  state_dict.pop(lowercase__  )
   __a			:  Dict		    =  val

    # finally, create HuggingFace model and load state dict
 __a			:  Union[str, Any]		    =  DetaForObjectDetection(lowercase__  )
 model.load_state_dict(lowercase__  )
 model.eval()

 __a			:  List[Any]		    =  '''cuda''' if torch.cuda.is_available() else '''cpu'''
 model.to(lowercase__  )

 # load image processor
 __a			:  Optional[int]		    =  DetaImageProcessor(format='''coco_detection'''  )

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

 # verify logits
 print('''Logits:'''  ,       outputs.logits[0, :3, :3]  )
 print('''Boxes:'''  ,       outputs.pred_boxes[0, :3, :3]  )
 if model_name == "deta-swin-large":
  __a			:  Optional[Any]		    =  torch.tensor(
      [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]  )
  __a			:  List[str]		    =  torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]  )
 elif model_name == "deta-swin-large-o365":
  __a			:  Dict		    =  torch.tensor(
      [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]  )
  __a			:  str		    =  torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]  )
 assert torch.allclose(outputs.logits[0, :3, :3]  ,       expected_logits.to(lowercase__  )  ,       atol=1e-4  )
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,       expected_boxes.to(lowercase__  )  ,       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(lowercase__  ).mkdir(exist_ok=lowercase__  )
  model.save_pretrained(lowercase__  )
  processor.save_pretrained(lowercase__  )

 # 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__":
  lowercase__          =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.'
  )
  lowercase__          =parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  Any		    =  len(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a			:  List[Any]		    =  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a			:  Union[str, Any]		    =  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
