
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()
lowerCamelCase_  :       List[str]					      =     logging.get_logger(__name__)







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

				return orig_key







def 				A__    (  lowerCamelCase  )     ->		str:
				# transformer decoder self-attention layers
				for i in range(6  ):
								# read in weights + bias of input projection layer of self-attention
								UpperCamelCase_:							Tuple		=  state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''  )
								UpperCamelCase_:							List[Any]		=  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
								UpperCamelCase_:							List[str]		=  in_proj_weight[:2_56, :]
								UpperCamelCase_:							int		=  in_proj_bias[:2_56]
								UpperCamelCase_:							int		=  in_proj_weight[2_56:5_12, :]
								UpperCamelCase_:							List[str]		=  in_proj_bias[2_56:5_12]
								UpperCamelCase_:							List[str]		=  in_proj_weight[-2_56:, :]
								UpperCamelCase_:							Tuple		=  in_proj_bias[-2_56:]







def 				A__    (  )     ->		Optional[Any]:
				UpperCamelCase_:							Union[str, Any]		=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:							Optional[int]		=  Image.open(requests.get(lowerCamelCase   ,			stream=lowerCamelCase  ).raw  )

				return im







@torch.no_grad()
def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)     ->		Dict:
				UpperCamelCase_:							Optional[Any]		=  DeformableDetrConfig()
				# set config attributes
				if single_scale:
								UpperCamelCase_:							List[str]		=  1
				UpperCamelCase_:							int		=  dilation
				UpperCamelCase_:							Any		=  with_box_refine
				UpperCamelCase_:							Dict		=  two_stage
				# set labels
				UpperCamelCase_:							List[Any]		=  91
				UpperCamelCase_:							int		=  """huggingface/label-files"""
				UpperCamelCase_:							List[Any]		=  """coco-detection-id2label.json"""
				UpperCamelCase_:							List[str]		=  json.load(open(cached_download(hf_hub_url(lowerCamelCase   ,			lowerCamelCase   ,			repo_type="""dataset"""  )  )   ,			"""r"""  )  )
				UpperCamelCase_:							Tuple		=  {int(lowerCamelCase  ): v for k, v in idalabel.items()}
				UpperCamelCase_:							int		=  idalabel
				UpperCamelCase_:							Optional[int]		=  {v: k for k, v in idalabel.items()}

				# load image processor
				UpperCamelCase_:							List[Any]		=  DeformableDetrImageProcessor(format="""coco_detection"""  )

				# prepare image
				UpperCamelCase_:							Union[str, Any]		=  prepare_img()
				UpperCamelCase_:							Any		=  image_processor(images=lowerCamelCase   ,			return_tensors="""pt"""  )
				UpperCamelCase_:							Any		=  encoding["""pixel_values"""]

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

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

				UpperCamelCase_:							List[str]		=  """cuda""" if torch.cuda.is_available() else """cpu"""
				model.to(lowerCamelCase  )
				# verify our conversion
				UpperCamelCase_:							Any		=  model(pixel_values.to(lowerCamelCase  )  )

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

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

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

				# Push to hub
				if push_to_hub:
								UpperCamelCase_:							List[str]		=  """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=lowerCamelCase   ,			organization="""nielsr"""   ,			commit_message="""Add model"""  )


if __name__ == "__main__":
							lowerCamelCase_  :       Union[str, Any]					      =     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."""
							)
							lowerCamelCase_  :       Tuple					      =     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 					_snake_case   (     A ,    A       )						->    float:
							return a + b


if __name__ == "__main__":
		__UpperCAmelCase    				=							5
		__UpperCAmelCase    				=							6
		print(f"""The sum of {a} + {b} is {add(a, b)}""")