

"""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						(						UpperCamelCase_  : List[str])			->		Union[str, Any]:
   '''simple docstring'''


   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						(						UpperCamelCase_  : Any)			->		Optional[Any]:
   '''simple docstring'''


   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						(						)			->		List[str]:
   '''simple docstring'''


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

   return im




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Optional[int],    UpperCamelCase_  : int,    UpperCamelCase_  : int,    UpperCamelCase_  : int,    UpperCamelCase_  : str,    UpperCamelCase_  : int,    UpperCamelCase_  : List[Any],    )			->		List[str]:
   '''simple docstring'''


   __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(UpperCamelCase_,    UpperCamelCase_,    repo_type="dataset")),    "r"))
   __lowercase			=		{int(UpperCamelCase_): 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=UpperCamelCase_,    return_tensors="pt")
   __lowercase			=		encoding["pixel_values"]

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

   # load original state dict
   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")["model"]
   # rename keys
   for key in state_dict.copy().keys():
      __lowercase			=		state_dict.pop(UpperCamelCase_)
      __lowercase			=		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
   __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(UpperCamelCase_)
         __lowercase			=		val
    # finally, create HuggingFace model and load state dict
   __lowercase			=		DeformableDetrForObjectDetection(UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)
   model.eval()

   __lowercase			=		"cuda" if torch.cuda.is_available() else "cpu"
   model.to(UpperCamelCase_)
   # verify our conversion
   __lowercase			=		model(pixel_values.to(UpperCamelCase_))

   __lowercase			=		torch.tensor(
       [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]])
   __lowercase			=		torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]])

   if single_scale:
      __lowercase			=		torch.tensor(
          [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]])
      __lowercase			=		torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]])

   if single_scale and dilation:
      __lowercase			=		torch.tensor(
          [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]])
      __lowercase			=		torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]])

   if with_box_refine:
      __lowercase			=		torch.tensor(
          [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]])
      __lowercase			=		torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]])

   if with_box_refine and two_stage:
      __lowercase			=		torch.tensor(
          [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]])
      __lowercase			=		torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]])

   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:
      __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=UpperCamelCase_,    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,
   )







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : float,    UpperCamelCase_  : float)			->		float:
   '''simple docstring'''


   __lowercase			=		float()
   try:
      __lowercase			=		mass * acceleration
   except Exception:
      return -0.0
   return force


if __name__ == "__main__":
   import doctest

   # run doctest
   doctest.testmod()

   # demo
   _a				   =							12.5
   _a				   =							10
   _a				   =							newtons_second_law_of_motion(mass, acceleration)
   print('The force is ', force, 'N')




