

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 	snake_case_      (__A     :						Any     )			->	Union[str, Any]:
   if "backbone.0.body" in orig_key:
      __lowerCAmelCase  :			Any					    =						orig_key.replace("""backbone.0.body"""					,   """backbone.conv_encoder.model"""     )
   if "transformer" in orig_key:
      __lowerCAmelCase  :			Union[str, Any]					    =						orig_key.replace("""transformer."""					,   """"""     )
   if "norm1" in orig_key:
      if "encoder" in orig_key:
         __lowerCAmelCase  :			Dict					    =						orig_key.replace("""norm1"""					,   """self_attn_layer_norm"""     )
      else:
         __lowerCAmelCase  :			Optional[int]					    =						orig_key.replace("""norm1"""					,   """encoder_attn_layer_norm"""     )
   if "norm2" in orig_key:
      if "encoder" in orig_key:
         __lowerCAmelCase  :			Union[str, Any]					    =						orig_key.replace("""norm2"""					,   """final_layer_norm"""     )
      else:
         __lowerCAmelCase  :			Tuple					    =						orig_key.replace("""norm2"""					,   """self_attn_layer_norm"""     )
   if "norm3" in orig_key:
      __lowerCAmelCase  :			Optional[Any]					    =						orig_key.replace("""norm3"""					,   """final_layer_norm"""     )
   if "linear1" in orig_key:
      __lowerCAmelCase  :			Optional[int]					    =						orig_key.replace("""linear1"""					,   """fc1"""     )
   if "linear2" in orig_key:
      __lowerCAmelCase  :			Dict					    =						orig_key.replace("""linear2"""					,   """fc2"""     )
   if "query_embed" in orig_key:
      __lowerCAmelCase  :			Tuple					    =						orig_key.replace("""query_embed"""					,   """query_position_embeddings"""     )
   if "cross_attn" in orig_key:
      __lowerCAmelCase  :			Tuple					    =						orig_key.replace("""cross_attn"""					,   """encoder_attn"""     )

   return orig_key





def 	snake_case_      (__A     :						Union[str, Any]     )			->	Optional[Any]:
   # transformer decoder self-attention layers
   for i in range(6     ):
      # read in weights + bias of input projection layer of self-attention
      __lowerCAmelCase  :			Optional[int]					    =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''     )
      __lowerCAmelCase  :			List[str]					    =						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
      __lowerCAmelCase  :			List[Any]					    =						in_proj_weight[:2_5_6, :]
      __lowerCAmelCase  :			Union[str, Any]					    =						in_proj_bias[:2_5_6]
      __lowerCAmelCase  :			Optional[Any]					    =						in_proj_weight[2_5_6:5_1_2, :]
      __lowerCAmelCase  :			List[str]					    =						in_proj_bias[2_5_6:5_1_2]
      __lowerCAmelCase  :			Union[str, Any]					    =						in_proj_weight[-2_5_6:, :]
      __lowerCAmelCase  :			int					    =						in_proj_bias[-2_5_6:]





def 	snake_case_      ()			->	Tuple:
   __lowerCAmelCase  :			List[Any]					    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __lowerCAmelCase  :			Any					    =						Image.open(requests.get(__A					,   stream=__A     ).raw     )

   return im





@torch.no_grad()
def 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]					,   __A     :						Any					,   __A     :						str					,   __A     :						Union[str, Any]					,   __A     :						int					,   __A     :						Any					,   )			->	Union[str, Any]:
   __lowerCAmelCase  :			Union[str, Any]					    =						DeformableDetrConfig()
   # set config attributes
   if single_scale:
      __lowerCAmelCase  :			Optional[Any]					    =						1
   __lowerCAmelCase  :			List[str]					    =						dilation
   __lowerCAmelCase  :			List[str]					    =						with_box_refine
   __lowerCAmelCase  :			List[Any]					    =						two_stage
   # set labels
   __lowerCAmelCase  :			Optional[int]					    =						9_1
   __lowerCAmelCase  :			str					    =						"""huggingface/label-files"""
   __lowerCAmelCase  :			Optional[int]					    =						"""coco-detection-id2label.json"""
   __lowerCAmelCase  :			List[str]					    =						json.load(open(cached_download(hf_hub_url(__A					,   __A					,   repo_type="""dataset"""     )     )					,   """r"""     )     )
   __lowerCAmelCase  :			Optional[int]					    =						{int(__A     ): v for k, v in idalabel.items()}
   __lowerCAmelCase  :			List[str]					    =						idalabel
   __lowerCAmelCase  :			str					    =						{v: k for k, v in idalabel.items()}

   # load image processor
   __lowerCAmelCase  :			Optional[int]					    =						DeformableDetrImageProcessor(format="""coco_detection"""     )

   # prepare image
   __lowerCAmelCase  :			str					    =						prepare_img()
   __lowerCAmelCase  :			Optional[int]					    =						image_processor(images=__A					,   return_tensors="""pt"""     )
   __lowerCAmelCase  :			Any					    =						encoding["""pixel_values"""]

   logger.info("""Converting model..."""     )

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

   __lowerCAmelCase  :			Tuple					    =						"""cuda""" if torch.cuda.is_available() else """cpu"""
   model.to(__A     )
   # verify our conversion
   __lowerCAmelCase  :			Tuple					    =						model(pixel_values.to(__A     )     )

   __lowerCAmelCase  :			List[Any]					    =						torch.tensor(
       [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]     )
   __lowerCAmelCase  :			Optional[int]					    =						torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]     )

   if single_scale:
      __lowerCAmelCase  :			Optional[Any]					    =						torch.tensor(
          [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]     )
      __lowerCAmelCase  :			List[str]					    =						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:
      __lowerCAmelCase  :			Any					    =						torch.tensor(
          [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]     )
      __lowerCAmelCase  :			Union[str, Any]					    =						torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]     )

   if with_box_refine:
      __lowerCAmelCase  :			List[str]					    =						torch.tensor(
          [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]     )
      __lowerCAmelCase  :			List[str]					    =						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:
      __lowerCAmelCase  :			str					    =						torch.tensor(
          [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]     )
      __lowerCAmelCase  :			List[str]					    =						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(__A     )					,   atol=1e-4     )
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,   expected_boxes.to(__A     )					,   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(__A     ).mkdir(exist_ok=__A     )
   model.save_pretrained(__A     )
   image_processor.save_pretrained(__A     )

   # Push to hub
   if push_to_hub:
      __lowerCAmelCase  :			Tuple					    =						"""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=__A					,   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,
   )





import importlib.metadata
import subprocess
import sys





def 	snake_case_      (__A     :						bool = False     )			->	str:
   __lowerCAmelCase  :			Any					    =						False
   if "IPython" in sys.modules:
      __lowerCAmelCase  :			Optional[int]					    =						"""google.colab""" in str(sys.modules["""IPython"""].get_ipython()     )

   if in_colab:
      if upgrade:
         __lowerCAmelCase  :			int					    =						["""pip""", """install""", """-U""", """torch"""]
         subprocess.run(__A					,   check=__A     )
      # get the current version of torch
      __lowerCAmelCase  :			List[Any]					    =						importlib.metadata.version("""torch"""     )
      __lowerCAmelCase  :			List[Any]					    =						torch_version[: torch_version.rindex("""."""     )]
      __lowerCAmelCase  :			int					    =						f'''https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl'''
      __lowerCAmelCase  :			List[Any]					    =						["""pip""", """install""", xla_wheel]
      subprocess.run(__A					,   check=__A     )
   else:
      raise RuntimeError("""`install_xla` utility works only on google colab."""     )


