






import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def 		A_			(		lowercase_					,       lowercase_=False					,       lowercase_=False					,       lowercase_="data2vec."	)							->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						'backbone.' if is_semantic else ''

 SCREAMING_SNAKE_CASE							  =						[]
 for i in range(config.num_hidden_layers	):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''{prefix}blocks.{i}.norm1.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')	)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')	)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')	)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
         (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]	)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('mask_token', f'''{hf_prefix}embeddings.mask_token'''),
          (
              'rel_pos_bias.relative_position_bias_table',
              f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              'rel_pos_bias.relative_position_index',
              f'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
      ]	)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]	)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('fc_norm.weight', f'''{hf_prefix}pooler.layernorm.weight'''),
          ('fc_norm.bias', f'''{hf_prefix}pooler.layernorm.bias'''),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]	)

 return rename_keys





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_=False					,       lowercase_=False					,       lowercase_="data2vec_vision."	)							->Any:







 """simple docstring"""

 for i in range(config.num_hidden_layers	):
  SCREAMING_SNAKE_CASE							  =						'backbone.' if is_semantic else ''
  # queries, keys and values
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''	)

  SCREAMING_SNAKE_CASE							  =						in_proj_weight[
      : config.hidden_size, :
  ]
  SCREAMING_SNAKE_CASE							  =						q_bias
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[
      -config.hidden_size :, :
  ]
  SCREAMING_SNAKE_CASE							  =						v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''	)

  SCREAMING_SNAKE_CASE							  =						gamma_a
  SCREAMING_SNAKE_CASE							  =						gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''	)
   SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''	)

   SCREAMING_SNAKE_CASE							  =						table
   SCREAMING_SNAKE_CASE							  =						index





def 		A_			(		)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						argparse.ArgumentParser(
     'Convert Data2VecVision to HF for image classification and pretraining'					,       add_help=lowercase_	)
 parser.add_argument('--hf_checkpoint_name'					,       type=lowercase_	)
 parser.add_argument('--input_size'					,       default=2_2_4					,       type=lowercase_					,       help='images input size'	)
 parser.add_argument('--beit_checkpoint'					,       default=''					,       help='beit checkpoint'	)

 return parser.parse_args()





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->List[str]:







 """simple docstring"""

 def load_state_dict(lowercase_					,       lowercase_					,       lowercase_=""					,       lowercase_="relative_position_index"	):
  SCREAMING_SNAKE_CASE							  =						[]
  SCREAMING_SNAKE_CASE							  =						[]
  SCREAMING_SNAKE_CASE							  =						[]
  # copy state_dict so _load_from_state_dict can modify it
  SCREAMING_SNAKE_CASE							  =						getattr(lowercase_					,       '_metadata'					,       lowercase_	)
  SCREAMING_SNAKE_CASE							  =						state_dict.copy()
  if metadata is not None:
   SCREAMING_SNAKE_CASE							  =						metadata

  def load(lowercase_					,       lowercase_=""	):
   SCREAMING_SNAKE_CASE							  =						{} if metadata is None else metadata.get(prefix[:-1]					,       {}	)
   module._load_from_state_dict(
       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_	)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase_					,       prefix + name + '.'	)

  load(lowercase_					,       prefix=lowercase_	)

  SCREAMING_SNAKE_CASE							  =						[]
  SCREAMING_SNAKE_CASE							  =						[]
  for key in missing_keys:
   SCREAMING_SNAKE_CASE							  =						True
   for ignore_key in ignore_missing.split('|'	):
    if ignore_key in key:
     SCREAMING_SNAKE_CASE							  =						False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_	)
   else:
    ignore_missing_keys.append(lowercase_	)

  SCREAMING_SNAKE_CASE							  =						warn_missing_keys

  if len(lowercase_	) > 0:
   print(
       'Weights of {} not initialized from pretrained model: {}'.format(
           model.__class__.__name__					,       lowercase_	)	)
  if len(lowercase_	) > 0:
   print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__					,       lowercase_	)	)
  if len(lowercase_	) > 0:
   print(
       'Ignored weights of {} not initialized from pretrained model: {}'.format(
           model.__class__.__name__					,       lowercase_	)	)
  if len(lowercase_	) > 0:
   print('\n'.join(lowercase_	)	)

 SCREAMING_SNAKE_CASE							  =						{
     'pretrained': False,
     'use_shared_rel_pos_bias': True,
     'use_abs_pos_emb': False,
     'init_values': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          'num_classes': 1_0_0_0,
          'use_mean_pooling': True,
          'init_scale': 0.0_0_1,
          'use_rel_pos_bias': True,
      }	)

 SCREAMING_SNAKE_CASE							  =						create_model(
     'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'					,       **lowercase_					,       )
 SCREAMING_SNAKE_CASE							  =						model.patch_embed.patch_size
 SCREAMING_SNAKE_CASE							  =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
 SCREAMING_SNAKE_CASE							  =						torch.load(args.beit_checkpoint					,       map_location='cpu'	)

 print(f'''Load ckpt from {args.beit_checkpoint}'''	)
 SCREAMING_SNAKE_CASE							  =						None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   SCREAMING_SNAKE_CASE							  =						checkpoint[model_key]
   print(f'''Load state_dict by model_key = {model_key}'''	)
   break

 SCREAMING_SNAKE_CASE							  =						list(checkpoint_model.keys()	)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase_	)

  if "relative_position_bias_table" in key:
   SCREAMING_SNAKE_CASE							  =						checkpoint_model[key]
   SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						rel_pos_bias.size()
   SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						model.state_dict()[key].size()
   SCREAMING_SNAKE_CASE							  =						model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase_					,       lowercase_					,       prefix=''	)

 return model





def 		A_			(		)							->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						get_args()

 SCREAMING_SNAKE_CASE							  =						'ft1k' in args.hf_checkpoint_name
 SCREAMING_SNAKE_CASE							  =						'large' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 SCREAMING_SNAKE_CASE							  =						DataaVecVisionConfig()
 if is_finetuned:
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						False
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						1_0_0_0

  SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
  SCREAMING_SNAKE_CASE							  =						'imagenet-1k-id2label.json'
  SCREAMING_SNAKE_CASE							  =						json.load(open(hf_hub_download(lowercase_					,       lowercase_					,       repo_type='dataset'	)					,       'r'	)	)
  SCREAMING_SNAKE_CASE							  =						{int(lowercase_	): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE							  =						idalabel
  SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}
 else:
  SCREAMING_SNAKE_CASE							  =						False
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						False

 if is_large:
  SCREAMING_SNAKE_CASE							  =						1_0_2_4
  SCREAMING_SNAKE_CASE							  =						4_0_9_6
  SCREAMING_SNAKE_CASE							  =						2_4
  SCREAMING_SNAKE_CASE							  =						1_6

 # 2. Load Beit model
 SCREAMING_SNAKE_CASE							  =						load_beit_model(lowercase_					,       lowercase_					,       lowercase_	)
 orig_model.eval()

 # 3. Forward Beit model
 SCREAMING_SNAKE_CASE							  =						BeitImageProcessor(size=config.image_size					,       do_center_crop=lowercase_	)
 SCREAMING_SNAKE_CASE							  =						Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'	)
 SCREAMING_SNAKE_CASE							  =						image_processor(images=lowercase_					,       return_tensors='pt'	)
 SCREAMING_SNAKE_CASE							  =						encoding['pixel_values']

 SCREAMING_SNAKE_CASE							  =						(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  SCREAMING_SNAKE_CASE							  =						orig_model(*lowercase_	)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  SCREAMING_SNAKE_CASE							  =						DataaVecVisionForImageClassification(lowercase_	)
  hf_model.eval()
  SCREAMING_SNAKE_CASE							  =						False
  SCREAMING_SNAKE_CASE							  =						'data2vec_vision.'
 else:
  SCREAMING_SNAKE_CASE							  =						DataaVecVisionModel(lowercase_	)
  hf_model.eval()
  SCREAMING_SNAKE_CASE							  =						True
  SCREAMING_SNAKE_CASE							  =						''

 SCREAMING_SNAKE_CASE							  =						create_rename_keys(lowercase_					,       hf_prefix=lowercase_					,       has_lm_head=lowercase_	)
 SCREAMING_SNAKE_CASE							  =						orig_model.state_dict()
 for src, dest in rename_keys:
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(lowercase_	)
  SCREAMING_SNAKE_CASE							  =						val

 read_in_q_k_v(lowercase_					,       lowercase_					,       hf_prefix=lowercase_					,       has_lm_head=lowercase_	)
 SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						hf_model.load_state_dict(lowercase_					,       strict=lowercase_	)
 print('HF missing'					,       lowercase_	)
 print('HF unexpected_keys'					,       lowercase_	)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  SCREAMING_SNAKE_CASE							  =						hf_model(lowercase_	)

 SCREAMING_SNAKE_CASE							  =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 SCREAMING_SNAKE_CASE							  =						torch.max(torch.abs(hf_output - orig_model_output	)	).item()

 print(f'''max_absolute_diff = {max_absolute_diff}'''	)
 SCREAMING_SNAKE_CASE							  =						torch.allclose(lowercase_					,       lowercase_					,       atol=1e-3	)
 print('Do both models output the same tensors?'					,       '🔥' if success else '💩'	)
 if not success:
  raise Exception('Something went wRoNg'	)

 # 7. Save
 print(f'''Saving to {args.hf_checkpoint_name}'''	)
 hf_model.save_pretrained(args.hf_checkpoint_name	)
 image_processor.save_pretrained(args.hf_checkpoint_name	)


if __name__ == "__main__":
      main()
      # Run the following to convert checkpoints
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./pretrained_base.pt \
      #          --hf_checkpoint_name "./data2vec-vision-base"
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./finetuned_base.pt \
      #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./pretrained_large.pt \
      #          --hf_checkpoint_name "./data2vec-vision-large"
      #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
      #          --beit_checkpoint ./finetuned_large.pt \
      #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"












import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      				=							logging.get_logger(__name__)





def 		A_			(		lowercase_	)							->Tuple:







 """simple docstring"""

 if "backbone.0.body" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('backbone.0.body'					,       'backbone.conv_encoder.model'	)
 if "transformer" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('transformer.'					,       ''	)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm1'					,       'self_attn_layer_norm'	)
  else:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm1'					,       'encoder_attn_layer_norm'	)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm2'					,       'final_layer_norm'	)
  else:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm2'					,       'self_attn_layer_norm'	)
 if "norm3" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm3'					,       'final_layer_norm'	)
 if "linear1" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('linear1'					,       'fc1'	)
 if "linear2" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('linear2'					,       'fc2'	)
 if "query_embed" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('query_embed'					,       'query_position_embeddings'	)
 if "cross_attn" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('cross_attn'					,       'encoder_attn'	)

 return orig_key





def 		A_			(		lowercase_	)							->Dict:







 """simple docstring"""

 for i in range(6	):
  # read in weights + bias of input projection layer of self-attention
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_bias'''	)
  # next, add query, keys and values (in that order) to the state dict
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[:2_5_6, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[:2_5_6]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[2_5_6:5_1_2, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[2_5_6:5_1_2]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[-2_5_6:, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[-2_5_6:]





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







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE							  =						Image.open(requests.get(lowercase_					,       stream=lowercase_	).raw	)

 return im





@torch.no_grad()
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       )							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						DeformableDetrConfig()
 # set config attributes
 if single_scale:
  SCREAMING_SNAKE_CASE							  =						1
 SCREAMING_SNAKE_CASE							  =						dilation
 SCREAMING_SNAKE_CASE							  =						with_box_refine
 SCREAMING_SNAKE_CASE							  =						two_stage
 # set labels
 SCREAMING_SNAKE_CASE							  =						9_1
 SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
 SCREAMING_SNAKE_CASE							  =						'coco-detection-id2label.json'
 SCREAMING_SNAKE_CASE							  =						json.load(open(cached_download(hf_hub_url(lowercase_					,       lowercase_					,       repo_type='dataset'	)	)					,       'r'	)	)
 SCREAMING_SNAKE_CASE							  =						{int(lowercase_	): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE							  =						idalabel
 SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}

 # load image processor
 SCREAMING_SNAKE_CASE							  =						DeformableDetrImageProcessor(format='coco_detection'	)

 # prepare image
 SCREAMING_SNAKE_CASE							  =						prepare_img()
 SCREAMING_SNAKE_CASE							  =						image_processor(images=lowercase_					,       return_tensors='pt'	)
 SCREAMING_SNAKE_CASE							  =						encoding['pixel_values']

 logger.info('Converting model...'	)

 # load original state dict
 SCREAMING_SNAKE_CASE							  =						torch.load(lowercase_					,       map_location='cpu'	)['model']
 # rename keys
 for key in state_dict.copy().keys():
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(lowercase_	)
  SCREAMING_SNAKE_CASE							  =						val
 # query, key and value matrices need special treatment
 read_in_q_k_v(lowercase_	)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 SCREAMING_SNAKE_CASE							  =						'model.'
 for key in state_dict.copy().keys():
  if not key.startswith('class_embed'	) and not key.startswith('bbox_embed'	):
   SCREAMING_SNAKE_CASE							  =						state_dict.pop(lowercase_	)
   SCREAMING_SNAKE_CASE							  =						val
    # finally, create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE							  =						DeformableDetrForObjectDetection(lowercase_	)
 model.load_state_dict(lowercase_	)
 model.eval()

 SCREAMING_SNAKE_CASE							  =						'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(lowercase_	)
 # verify our conversion
 SCREAMING_SNAKE_CASE							  =						model(pixel_values.to(lowercase_	)	)

 SCREAMING_SNAKE_CASE							  =						torch.tensor(
     [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]	)
 SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]	)

 if single_scale:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]	)

 if single_scale and dilation:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]	)

 if with_box_refine:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]	)

 if with_box_refine and two_stage:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]	)

 print('Logits:'					,       outputs.logits[0, :3, :3]	)

 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!'	)

 # Save model and image processor
 logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
 Path(lowercase_	).mkdir(exist_ok=lowercase_	)
 model.save_pretrained(lowercase_	)
 image_processor.save_pretrained(lowercase_	)

 # Push to hub
 if push_to_hub:
  SCREAMING_SNAKE_CASE							  =						'deformable-detr'
  model_name += "-single-scale" if single_scale else ""
  model_name += "-dc5" if dilation else ""
  model_name += "-with-box-refine" if with_box_refine else ""
  model_name += "-two-stage" if two_stage else ""
  print('Pushing model to hub...'	)
  model.push_to_hub(repo_path_or_name=lowercase_					,       organization='nielsr'					,       commit_message='Add model'	)


if __name__ == "__main__":
      __UpperCAmelCase      				=							argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path",
          type=str,
          default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
          help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
      )
      parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
      parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
      parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
      parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          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_deformable_detr_checkpoint(
          args.checkpoint_path,
          args.single_scale,
          args.dilation,
          args.with_box_refine,
          args.two_stage,
          args.pytorch_dump_folder_path,
          args.push_to_hub,
      )




