code: 
  
  
  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()
  lowercase_								=    logging.get_logger(__name__)
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						if "backbone.0.body" in orig_key:
  												_a     =			orig_key.replace('backbone.0.body'							,     'backbone.conv_encoder.model'				)
  						if "transformer" in orig_key:
  												_a     =			orig_key.replace('transformer.'							,     ''				)
  						if "norm1" in orig_key:
  												if "encoder" in orig_key:
  																		_a     =			orig_key.replace('norm1'							,     'self_attn_layer_norm'				)
  												else:
  																		_a     =			orig_key.replace('norm1'							,     'encoder_attn_layer_norm'				)
  						if "norm2" in orig_key:
  												if "encoder" in orig_key:
  																		_a     =			orig_key.replace('norm2'							,     'final_layer_norm'				)
  												else:
  																		_a     =			orig_key.replace('norm2'							,     'self_attn_layer_norm'				)
  						if "norm3" in orig_key:
  												_a     =			orig_key.replace('norm3'							,     'final_layer_norm'				)
  						if "linear1" in orig_key:
  												_a     =			orig_key.replace('linear1'							,     'fc1'				)
  						if "linear2" in orig_key:
  												_a     =			orig_key.replace('linear2'							,     'fc2'				)
  						if "query_embed" in orig_key:
  												_a     =			orig_key.replace('query_embed'							,     'query_position_embeddings'				)
  						if "cross_attn" in orig_key:
  												_a     =			orig_key.replace('cross_attn'							,     'encoder_attn'				)
  
  						return orig_key
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Dict:
  						# transformer decoder self-attention layers
  						for i in range(6				):
  												# read in weights + bias of input projection layer of self-attention
  												_a     =			state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""				)
  												_a     =			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
  												_a     =			in_proj_weight[:256, :]
  												_a     =			in_proj_bias[:256]
  												_a     =			in_proj_weight[256:512, :]
  												_a     =			in_proj_bias[256:512]
  												_a     =			in_proj_weight[-256:, :]
  												_a     =			in_proj_bias[-256:]
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  Dict:
  						_a     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  						_a     =			Image.open(requests.get(_UpperCAmelCase							,     stream=_UpperCAmelCase				).raw				)
  
  						return im
  
  
  @torch.no_grad()
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     )    ->  int:
  						_a     =			DeformableDetrConfig()
  						# set config attributes
  						if single_scale:
  												_a     =			1
  						_a     =			dilation
  						_a     =			with_box_refine
  						_a     =			two_stage
  						# set labels
  						_a     =			91
  						_a     =			'huggingface/label-files'
  						_a     =			'coco-detection-id2label.json'
  						_a     =			json.load(open(cached_download(hf_hub_url(_UpperCAmelCase							,     _UpperCAmelCase							,     repo_type='dataset'				)				)							,     'r'				)				)
  						_a     =			{int(_UpperCAmelCase				): v for k, v in idalabel.items()}
  						_a     =			idalabel
  						_a     =			{v: k for k, v in idalabel.items()}
  
  						# load image processor
  						_a     =			DeformableDetrImageProcessor(format='coco_detection'				)
  
  						# prepare image
  						_a     =			prepare_img()
  						_a     =			image_processor(images=_UpperCAmelCase							,     return_tensors='pt'				)
  						_a     =			encoding['pixel_values']
  
  						logger.info('Converting model...'				)
  
  						# load original state dict
  						_a     =			torch.load(_UpperCAmelCase							,     map_location='cpu'				)['model']
  						# rename keys
  						for key in state_dict.copy().keys():
  												_a     =			state_dict.pop(_UpperCAmelCase				)
  												_a     =			val
  						# query, key and value matrices need special treatment
  						read_in_q_k_v(_UpperCAmelCase				)
  						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  						_a     =			'model.'
  						for key in state_dict.copy().keys():
  												if not key.startswith('class_embed'				) and not key.startswith('bbox_embed'				):
  																		_a     =			state_dict.pop(_UpperCAmelCase				)
  																		_a     =			val
      # finally, create HuggingFace model and load state dict
  						_a     =			DeformableDetrForObjectDetection(_UpperCAmelCase				)
  						model.load_state_dict(_UpperCAmelCase				)
  						model.eval()
  
  						_a     =			'cuda' if torch.cuda.is_available() else 'cpu'
  						model.to(_UpperCAmelCase				)
  						# verify our conversion
  						_a     =			model(pixel_values.to(_UpperCAmelCase				)				)
  
  						_a     =			torch.tensor(
  						    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]				)
  						_a     =			torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]				)
  
  						if single_scale:
  												_a     =			torch.tensor(
  												    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]				)
  												_a     =			torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]				)
  
  						if single_scale and dilation:
  												_a     =			torch.tensor(
  												    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]				)
  												_a     =			torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]				)
  
  						if with_box_refine:
  												_a     =			torch.tensor(
  												    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]				)
  												_a     =			torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]				)
  
  						if with_box_refine and two_stage:
  												_a     =			torch.tensor(
  												    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]				)
  												_a     =			torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]				)
  
  						print('Logits:'							,     outputs.logits[0, :3, :3]				)
  
  						assert torch.allclose(outputs.logits[0, :3, :3]							,     expected_logits.to(_UpperCAmelCase				)							,     atol=1e-4				)
  						assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,     expected_boxes.to(_UpperCAmelCase				)							,     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(_UpperCAmelCase				).mkdir(exist_ok=_UpperCAmelCase				)
  						model.save_pretrained(_UpperCAmelCase				)
  						image_processor.save_pretrained(_UpperCAmelCase				)
  
  						# Push to hub
  						if push_to_hub:
  												_a     =			'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=_UpperCAmelCase							,     organization='nielsr'							,     commit_message='Add model'				)
  
  
  if __name__ == "__main__":
  		lowercase_								=    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.'
  		)
  		lowercase_								=    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,
  		)
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  