






"""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()
UpperCamelCase											=     logging.get_logger(__name__)






def 							lowerCAmelCase			(   UpperCamelCase_:				str       )							->				Tuple:







  '''simple docstring'''







  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 							lowerCAmelCase			(   UpperCamelCase_:				Union[str, Any]       )							->				Any:







  '''simple docstring'''







  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 							lowerCAmelCase			(   )							->				Optional[Any]:







  '''simple docstring'''







  _a					      =						"http://images.cocodataset.org/val2017/000000039769.jpg"
  _a					      =						Image.open(requests.get(UpperCamelCase_  ,			stream=UpperCamelCase_       ).raw       )

  return im






@torch.no_grad()
def 							lowerCAmelCase			(   UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Union[str, Any]  ,			UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				Any  ,			)							->				int:







  '''simple docstring'''







  _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__":
     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,
     )










"""simple docstring"""




import math






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				list:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive integers have prime factors"       )
  _a					      =						[]
  while n % 2 == 0:
    pf.append(2       )
    _a					      =						int(n / 2       )
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    while n % i == 0:
      pf.append(UpperCamelCase_       )
      _a					      =						int(n / i       )
  if n > 2:
    pf.append(UpperCamelCase_       )
  return pf






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  div *= temp
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    div *= temp
  if n > 1:
    div *= 2
  return div






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  if temp > 1:
    s *= (2**temp - 1) / (2 - 1)
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    if temp > 1:
      s *= (i**temp - 1) / (i - 1)
  return int(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  _a					      =						n
  for x in set(prime_factors(UpperCamelCase_       )       ):
    s *= (x - 1) / x
  return int(UpperCamelCase_       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


