


'''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()
_SCREAMING_SNAKE_CASE       :   Any        = logging.get_logger(__name__)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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

				return orig_key



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				for i in range(6		):
								# read in weights + bias of input projection layer of self-attention
								__magic_name__				:					Union[str, Any]						       =       state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""		)
								__magic_name__				:					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
								__magic_name__				:					Any						       =       in_proj_weight[:256, :]
								__magic_name__				:					Tuple						       =       in_proj_bias[:256]
								__magic_name__				:					Any						       =       in_proj_weight[256:512, :]
								__magic_name__				:					Optional[int]						       =       in_proj_bias[256:512]
								__magic_name__				:					str						       =       in_proj_weight[-256:, :]
								__magic_name__				:					str						       =       in_proj_bias[-256:]



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       "http://images.cocodataset.org/val2017/000000039769.jpg"
				__magic_name__				:					List[Any]						       =       Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__		).raw		)

				return im



@torch.no_grad()
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):






				"""simple docstring"""



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

				# load image processor
				__magic_name__				:					Tuple						       =       DeformableDetrImageProcessor(format="coco_detection"		)

				# prepare image
				__magic_name__				:					str						       =       prepare_img()
				__magic_name__				:					List[str]						       =       image_processor(images=UpperCamelCase__     ,   return_tensors="pt"		)
				__magic_name__				:					Tuple						       =       encoding["pixel_values"]

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

				# load original state dict
				__magic_name__				:					Any						       =       torch.load(UpperCamelCase__     ,   map_location="cpu"		)["model"]
				# rename keys
				for key in state_dict.copy().keys():
								__magic_name__				:					Optional[int]						       =       state_dict.pop(UpperCamelCase__		)
								__magic_name__				:					Union[str, Any]						       =       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
				__magic_name__				:					Union[str, Any]						       =       "model."
				for key in state_dict.copy().keys():
								if not key.startswith("class_embed"		) and not key.startswith("bbox_embed"		):
												__magic_name__				:					int						       =       state_dict.pop(UpperCamelCase__		)
												__magic_name__				:					Optional[Any]						       =       val
    # finally, create HuggingFace model and load state dict
				__magic_name__				:					str						       =       DeformableDetrForObjectDetection(UpperCamelCase__		)
				model.load_state_dict(UpperCamelCase__		)
				model.eval()

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

				__magic_name__				:					str						       =       torch.tensor(
				    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]		)
				__magic_name__				:					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:
								__magic_name__				:					Optional[Any]						       =       torch.tensor(
								    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]		)
								__magic_name__				:					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:
								__magic_name__				:					Optional[int]						       =       torch.tensor(
								    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]		)
								__magic_name__				:					List[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:
								__magic_name__				:					List[str]						       =       torch.tensor(
								    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]		)
								__magic_name__				:					List[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:
								__magic_name__				:					Optional[int]						       =       torch.tensor(
								    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]		)
								__magic_name__				:					Union[str, Any]						       =       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:
								__magic_name__				:					Dict						       =       "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__":
		_SCREAMING_SNAKE_CASE       :   Optional[int]        = 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."
		)
		_SCREAMING_SNAKE_CASE       :   int        = 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 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				if not isinstance(UpperCamelCase__     ,   UpperCamelCase__		) or number < 0:
								raise ValueError("Input must be a non-negative integer"		)

				__magic_name__				:					str						       =       number & ~(number - 1)
				__magic_name__				:					Optional[int]						       =       0
				while intermediate:
								intermediate >>= 1
								index += 1
				return index - 1


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)