code: 
  '''simple docstring'''
  
  
  
  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()
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						if "backbone.0.body" in orig_key:
  												lowercase__							 =       orig_key.replace("""backbone.0.body"""		,		"""backbone.conv_encoder.model"""	)
  						if "transformer" in orig_key:
  												lowercase__							 =       orig_key.replace("""transformer."""		,		""""""	)
  						if "norm1" in orig_key:
  												if "encoder" in orig_key:
  																		lowercase__							 =       orig_key.replace("""norm1"""		,		"""self_attn_layer_norm"""	)
  												else:
  																		lowercase__							 =       orig_key.replace("""norm1"""		,		"""encoder_attn_layer_norm"""	)
  						if "norm2" in orig_key:
  												if "encoder" in orig_key:
  																		lowercase__							 =       orig_key.replace("""norm2"""		,		"""final_layer_norm"""	)
  												else:
  																		lowercase__							 =       orig_key.replace("""norm2"""		,		"""self_attn_layer_norm"""	)
  						if "norm3" in orig_key:
  												lowercase__							 =       orig_key.replace("""norm3"""		,		"""final_layer_norm"""	)
  						if "linear1" in orig_key:
  												lowercase__							 =       orig_key.replace("""linear1"""		,		"""fc1"""	)
  						if "linear2" in orig_key:
  												lowercase__							 =       orig_key.replace("""linear2"""		,		"""fc2"""	)
  						if "query_embed" in orig_key:
  												lowercase__							 =       orig_key.replace("""query_embed"""		,		"""query_position_embeddings"""	)
  						if "cross_attn" in orig_key:
  												lowercase__							 =       orig_key.replace("""cross_attn"""		,		"""encoder_attn"""	)
  
  						return orig_key
  
  
  
  
  
  def  _A		(							lowercase__	):
  						# transformer decoder self-attention layers
  						for i in range(6	):
  												# read in weights + bias of input projection layer of self-attention
  												lowercase__							 =       state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
  												lowercase__							 =       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
  												lowercase__							 =       in_proj_weight[:256, :]
  												lowercase__							 =       in_proj_bias[:256]
  												lowercase__							 =       in_proj_weight[256:512, :]
  												lowercase__							 =       in_proj_bias[256:512]
  												lowercase__							 =       in_proj_weight[-256:, :]
  												lowercase__							 =       in_proj_bias[-256:]
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  						lowercase__							 =       Image.open(requests.get(lowercase__		,		stream=lowercase__	).raw	)
  
  						return im
  
  
  
  
  
  @torch.no_grad()
  def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		):
  						lowercase__							 =       DeformableDetrConfig()
  						# set config attributes
  						if single_scale:
  												lowercase__							 =       1
  						lowercase__							 =       dilation
  						lowercase__							 =       with_box_refine
  						lowercase__							 =       two_stage
  						# set labels
  						lowercase__							 =       91
  						lowercase__							 =       """huggingface/label-files"""
  						lowercase__							 =       """coco-detection-id2label.json"""
  						lowercase__							 =       json.load(open(cached_download(hf_hub_url(lowercase__		,		lowercase__		,		repo_type="""dataset"""	)	)		,		"""r"""	)	)
  						lowercase__							 =       {int(lowercase__	): v for k, v in idalabel.items()}
  						lowercase__							 =       idalabel
  						lowercase__							 =       {v: k for k, v in idalabel.items()}
  
  						# load image processor
  						lowercase__							 =       DeformableDetrImageProcessor(format="""coco_detection"""	)
  
  						# prepare image
  						lowercase__							 =       prepare_img()
  						lowercase__							 =       image_processor(images=lowercase__		,		return_tensors="""pt"""	)
  						lowercase__							 =       encoding["""pixel_values"""]
  
  						logger.info("""Converting model..."""	)
  
  						# load original state dict
  						lowercase__							 =       torch.load(lowercase__		,		map_location="""cpu"""	)["""model"""]
  						# rename keys
  						for key in state_dict.copy().keys():
  												lowercase__							 =       state_dict.pop(lowercase__	)
  												lowercase__							 =       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
  						lowercase__							 =       """model."""
  						for key in state_dict.copy().keys():
  												if not key.startswith("""class_embed"""	) and not key.startswith("""bbox_embed"""	):
  																		lowercase__							 =       state_dict.pop(lowercase__	)
  																		lowercase__							 =       val
      # finally, create HuggingFace model and load state dict
  						lowercase__							 =       DeformableDetrForObjectDetection(lowercase__	)
  						model.load_state_dict(lowercase__	)
  						model.eval()
  
  						lowercase__							 =       """cuda""" if torch.cuda.is_available() else """cpu"""
  						model.to(lowercase__	)
  						# verify our conversion
  						lowercase__							 =       model(pixel_values.to(lowercase__	)	)
  
  						lowercase__							 =       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]]	)
  						lowercase__							 =       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:
  												lowercase__							 =       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]]	)
  												lowercase__							 =       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:
  												lowercase__							 =       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]]	)
  												lowercase__							 =       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:
  												lowercase__							 =       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]]	)
  												lowercase__							 =       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:
  												lowercase__							 =       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]]	)
  												lowercase__							 =       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:
  												lowercase__							 =       """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__":
  				__A          =    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."
  				)
  				__A          =    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: 
  '''simple docstring'''
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  __A          =    torch.cuda.is_available()
  __A          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
  __A          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  __A          =    {
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-0_8,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       """\n""".join(lowercase__	)
  						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						for split in ["train", "val", "test"]:
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.source'''	)		,		lowercase__	)
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.target'''	)		,		lowercase__	)
  						return tmp_dir
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls
  
  
  
  			@require_torch_multi_gpu
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """no_teacher""": True,
  									    """freeze_encoder""": True,
  									    """gpus""": 2,
  									    """overwrite_output_dir""": True,
  									    """sortish_sampler""": True,
  									}
  									self._test_distiller_cli_fork(lowerCamelCase__       ,     check_contents=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=True       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """label_smoothing""": 0.0,
  									    """early_stopping_patience""": -1,
  									    """train_batch_size""": 1,
  									    """eval_batch_size""": 2,
  									    """max_epochs""": 2,
  									    """alpha_mlm""": 0.2,
  									    """alpha_ce""": 0.8,
  									    """do_predict""": True,
  									    """model_name_or_path""": """sshleifer/tinier_bart""",
  									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
  									    """val_check_interval""": 0.5,
  									}
  									default_updates.update(lowerCamelCase__       )
  									lowercase__							 =       CHEAP_ARGS.copy()
  									lowercase__							 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  									lowercase__							 =       self.get_auto_remove_tmp_dir()
  									args_d.update(data_dir=lowerCamelCase__       ,     output_dir=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									def convert(lowerCamelCase__       ,     lowerCamelCase__       ):
  															if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
  																					return ""
  															if v is False or v is None:
  																					return ""
  															if v is True:  # or len(str(v))==0:
  																					return F'''--{k}'''
  															return F'''--{k}={v}'''
  
  									lowercase__							 =       [x for x in (convert(lowerCamelCase__       ,     lowerCamelCase__       ) for k, v in args_d.items()) if len(lowerCamelCase__       )]
  									lowercase__							 =       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
  									execute_subprocess_async(lowerCamelCase__       ,     env=self.get_env()       )
  
  									lowercase__							 =       os.listdir(lowerCamelCase__       )
  									lowercase__							 =       {os.path.basename(lowerCamelCase__       ) for p in contents}
  									lowercase__							 =       [p for p in contents if p.endswith("""ckpt"""       )]
  									assert len(lowerCamelCase__       ) > 0
  
  									self.assertIn("""test_generations.txt"""       ,     lowerCamelCase__       )
  									self.assertIn("""test_results.txt"""       ,     lowerCamelCase__       )
  
  									# get the following from the module, (we don't have access to `model` here)
  									lowercase__							 =       os.path.join(lowerCamelCase__       ,     """metrics.json"""       )
  									lowercase__							 =       """rouge2"""
  
  									lowercase__							 =       load_json(lowerCamelCase__       )
  									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
  									print(lowerCamelCase__       )
  									lowercase__							 =       metrics["""val"""][-1]
  									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.01       )
  									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,     lowerCamelCase__       )
  									self.assertEqual(len(metrics["""test"""]       )       ,     1       )
  									lowercase__							 =       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
  									self.assertEqual(len(metrics["""val"""]       )       ,     lowerCamelCase__       )
  
  
  
  
  
  
  