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()
UpperCAmelCase_			    =							logging.get_logger(__name__)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Any							= SwinConfig(
     embed_dim=192  ,					depths=(2, 2, 18, 2)  ,					num_heads=(6, 12, 24, 48)  ,					window_size=12  ,					out_features=['''stage2''', '''stage3''', '''stage4''']  ,					)

 _UpperCamelCase:     int							= DetaConfig(
     backbone_config=lowercase  ,					num_queries=900  ,					encoder_ffn_dim=2_048  ,					decoder_ffn_dim=2_048  ,					num_feature_levels=5  ,					assign_first_stage=lowercase  ,					with_box_refine=lowercase  ,					two_stage=lowercase  ,					)

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

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

 return config




def       lowerCAmelCase_     (		lowercase:							Optional[int]		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, 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       lowerCAmelCase_     (		lowercase:							List[str]  ,					lowercase:							str  ,					lowercase:							Union[str, Any]		)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= dct.pop(lowercase		)
 _UpperCamelCase:     Optional[Any]							= val




def       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							Optional[Any]		)    ->				List[Any]:







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




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							Tuple		)    ->				Dict:







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




def       lowerCAmelCase_     (		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Any							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _UpperCamelCase:     int							= Image.open(requests.get(lowercase  ,					stream=lowercase		).raw		)

 return im




@torch.no_grad()
def       lowerCAmelCase_     (		lowercase:							Optional[Any]  ,					lowercase:							List[str]  ,					lowercase:							Dict		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     int							= get_deta_config(lowercase		)

 # load original state dict
 if model_name == "deta-swin-large":
  _UpperCamelCase:     str							= hf_hub_download(repo_id='''nielsr/deta-checkpoints'''  ,					filename='''adet_swin_ft.pth'''		)
 elif model_name == "deta-swin-large-o365":
  _UpperCamelCase:     Union[str, Any]							= 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"""		)

 _UpperCamelCase:     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
 _UpperCamelCase:     Optional[int]							= 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:
   _UpperCamelCase:     Tuple							= state_dict.pop(lowercase		)
   _UpperCamelCase:     int							= val
  if "input_proj" in key:
   _UpperCamelCase:     Optional[int]							= state_dict.pop(lowercase		)
   _UpperCamelCase:     Any							= val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   _UpperCamelCase:     int							= state_dict.pop(lowercase		)
   _UpperCamelCase:     Optional[Any]							= val

    # finally, create HuggingFace model and load state dict
 _UpperCamelCase:     int							= DetaForObjectDetection(lowercase		)
 model.load_state_dict(lowercase		)
 model.eval()

 _UpperCamelCase:     Dict							= '''cuda''' if torch.cuda.is_available() else '''cpu'''
 model.to(lowercase		)

 # load image processor
 _UpperCamelCase:     str							= DetaImageProcessor(format='''coco_detection'''		)

 # verify our conversion on image
 _UpperCamelCase:     str							= prepare_img()
 _UpperCamelCase:     Union[str, Any]							= processor(images=lowercase  ,					return_tensors='''pt'''		)
 _UpperCamelCase:     Optional[Any]							= encoding['''pixel_values''']
 _UpperCamelCase:     Tuple							= 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":
  _UpperCamelCase:     Optional[int]							= torch.tensor(
      [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]		)
  _UpperCamelCase:     str							= 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":
  _UpperCamelCase:     Optional[int]							= torch.tensor(
      [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]		)
  _UpperCamelCase:     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(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__":
  UpperCAmelCase_			    =							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.'''
  )
  UpperCAmelCase_			    =							parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  str		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



    warnings.warn(
        '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use PerceiverImageProcessor instead.'''		,  _lowercase		,  )
    super().__init__(*_lowercase		,  **_lowercase							)