




'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase							:						str							     =				logging.get_logger(__name__)






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	DetaConfig(
		    num_queries=900						,							encoder_ffn_dim=2048						,							decoder_ffn_dim=2048						,							num_feature_levels=5						,							assign_first_stage=_lowerCamelCase						,							with_box_refine=_lowerCamelCase						,							two_stage=_lowerCamelCase						,							)

		# set labels
		_snake_case     	=	91
		_snake_case     	=	"""huggingface/label-files"""
		_snake_case     	=	"""coco-detection-id2label.json"""
		_snake_case     	=	json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,							_lowerCamelCase						,							repo_type="dataset"  )  )						,							"r"  )  )
		_snake_case     	=	{int(_lowerCamelCase  ): v for k, v in idalabel.items()}
		_snake_case     	=	idalabel
		_snake_case     	=	{v: k for k, v in idalabel.items()}

		return config






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	[]

		# stem
		# fmt: off
		rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")  )
		rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")  )
		rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")  )
		rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")  )
		rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")  )
		# stages
		for stage_idx in range(len(config.backbone_config.depths  )  ):
				for layer_idx in range(config.backbone_config.depths[stage_idx]  ):
						# shortcut
						if layer_idx == 0:
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
								    )  )
						# 3 convs
						for i in range(3  ):
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
								    )  )
								rename_keys.append(
								    (
								        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
								        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
								    )  )
				# transformer encoder
		for i in range(config.encoder_layers  ):
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')  )
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')  )

		# transformer decoder
		for i in range(config.decoder_layers  ):
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')  )
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')  )

		# fmt: on

		return rename_keys






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	dct.pop(_lowerCamelCase  )
		_snake_case     	=	val






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	config.d_model
		for i in range(config.decoder_layers  ):
				# read in weights + bias of input projection layer of self-attention
				_snake_case     	=	state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''  )
				_snake_case     	=	state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''  )
				# next, add query, keys and values (in that order) to the state dict
				_snake_case     	=	in_proj_weight[:hidden_size, :]
				_snake_case     	=	in_proj_bias[:hidden_size]
				_snake_case     	=	in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				_snake_case     	=	in_proj_bias[hidden_size : hidden_size * 2]
				_snake_case     	=	in_proj_weight[-hidden_size:, :]
				_snake_case     	=	in_proj_bias[-hidden_size:]






def 		_lowercase		(     ):



		'''simple docstring'''







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

		return im






@torch.no_grad()
def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	get_deta_config()

		# load original state dict
		if model_name == "deta-resnet-50":
				_snake_case     	=	"""adet_checkpoint0011.pth"""
		elif model_name == "deta-resnet-50-24-epochs":
				_snake_case     	=	"""adet_2x_checkpoint0023.pth"""
		else:
				raise ValueError(f'''Model name {model_name} not supported'''  )
		_snake_case     	=	hf_hub_download(repo_id="nielsr/deta-checkpoints"						,							filename=_lowerCamelCase  )
		_snake_case     	=	torch.load(_lowerCamelCase						,							map_location="cpu"  )["""model"""]

		# rename keys
		_snake_case     	=	create_rename_keys(_lowerCamelCase  )
		for src, dest in rename_keys:
				rename_key(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase  )
		read_in_decoder_q_k_v(_lowerCamelCase						,							_lowerCamelCase  )

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						_snake_case     	=	state_dict.pop(_lowerCamelCase  )
						_snake_case     	=	val
				if "input_proj" in key:
						_snake_case     	=	state_dict.pop(_lowerCamelCase  )
						_snake_case     	=	val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						_snake_case     	=	state_dict.pop(_lowerCamelCase  )
						_snake_case     	=	val

				# finally, create HuggingFace model and load state dict
		_snake_case     	=	DetaForObjectDetection(_lowerCamelCase  )
		model.load_state_dict(_lowerCamelCase  )
		model.eval()

		_snake_case     	=	"""cuda""" if torch.cuda.is_available() else """cpu"""
		model.to(_lowerCamelCase  )

		# load image processor
		_snake_case     	=	DetaImageProcessor(format="coco_detection"  )

		# verify our conversion on image
		_snake_case     	=	prepare_img()
		_snake_case     	=	processor(images=_lowerCamelCase						,							return_tensors="pt"  )
		_snake_case     	=	encoding["""pixel_values"""]
		_snake_case     	=	model(pixel_values.to(_lowerCamelCase  )  )

		# verify logits
		if model_name == "deta-resnet-50":
				_snake_case     	=	torch.tensor(
				    [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]  )
				_snake_case     	=	torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]  )
		elif model_name == "deta-resnet-50-24-epochs":
				_snake_case     	=	torch.tensor(
				    [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]  )
				_snake_case     	=	torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]  )

		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!"  )

		if pytorch_dump_folder_path:
				# Save model and processor
				logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''  )
				Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
				model.save_pretrained(_lowerCamelCase  )
				processor.save_pretrained(_lowerCamelCase  )

		# Push to hub
		if push_to_hub:
				print("Pushing model and processor to hub..."  )
				model.push_to_hub(f'''jozhang97/{model_name}'''  )
				processor.push_to_hub(f'''jozhang97/{model_name}'''  )


if __name__ == "__main__":
						_lowerCamelCase							:						Optional[int]							     =				argparse.ArgumentParser()

						parser.add_argument(
						    '--model_name',
						    type=str,
						    default='deta-resnet-50',
						    choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
						    help='Name of the model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=str,
						    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							:						Any							     =				parser.parse_args()
						convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''







def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5) + 32						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) + 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5) + 491.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) - 32) * 5 / 9						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(((float(lowercase_  ) - 32) * 5 / 9) + 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) + 459.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) - 273.15						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(((float(lowercase_  ) - 273.15) * 9 / 5) + 32						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 9 / 5)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) - 491.67) * 5 / 9						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round(float(lowercase_  ) - 459.67						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 5 / 9)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 1.25 + 273.15)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 2.25 + 32)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 1.25)						,							lowercase_  )






def 		_lowercase		(     lowercase_						,							lowercase_ = 2  ):



		'''simple docstring'''







		return round((float(lowercase_  ) * 2.25 + 32 + 459.67)						,							lowercase_  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





