





'''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()
__UpperCAmelCase         =   logging.get_logger(__name__)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	DetaConfig(
		    num_queries=9_00	,	encoder_ffn_dim=20_48	,	decoder_ffn_dim=20_48	,	num_feature_levels=5	,	assign_first_stage=__UpperCamelCase	,	with_box_refine=__UpperCamelCase	,	two_stage=__UpperCamelCase	,	)

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

		return config



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							str						=	[]

		# 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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	dct.pop(__UpperCamelCase					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	val



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Dict						=	config.d_model
		for i in range(config.decoder_layers					):
				# read in weights + bias of input projection layer of self-attention
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
				SCREAMING_SNAKE_CASE				:							int						=	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
				SCREAMING_SNAKE_CASE				:							List[Any]						=	in_proj_weight[:hidden_size, :]
				SCREAMING_SNAKE_CASE				:							int						=	in_proj_bias[:hidden_size]
				SCREAMING_SNAKE_CASE				:							List[Any]						=	in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	in_proj_bias[hidden_size : hidden_size * 2]
				SCREAMING_SNAKE_CASE				:							Tuple						=	in_proj_weight[-hidden_size:, :]
				SCREAMING_SNAKE_CASE				:							int						=	in_proj_bias[-hidden_size:]



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE				:							List[str]						=	Image.open(requests.get(__UpperCamelCase	,	stream=__UpperCamelCase					).raw					)

		return im



@torch.no_grad()
def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	get_deta_config()

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

		# rename keys
		SCREAMING_SNAKE_CASE				:							Dict						=	create_rename_keys(__UpperCamelCase					)
		for src, dest in rename_keys:
				rename_key(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase					)
		read_in_decoder_q_k_v(__UpperCamelCase	,	__UpperCamelCase					)

		# fix some prefixes
		for key in state_dict.copy().keys():
				if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
						SCREAMING_SNAKE_CASE				:							List[str]						=	state_dict.pop(__UpperCamelCase					)
						SCREAMING_SNAKE_CASE				:							str						=	val
				if "input_proj" in key:
						SCREAMING_SNAKE_CASE				:							Optional[Any]						=	state_dict.pop(__UpperCamelCase					)
						SCREAMING_SNAKE_CASE				:							List[str]						=	val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						SCREAMING_SNAKE_CASE				:							List[str]						=	state_dict.pop(__UpperCamelCase					)
						SCREAMING_SNAKE_CASE				:							List[str]						=	val

    # finally, create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	DetaForObjectDetection(__UpperCamelCase					)
		model.load_state_dict(__UpperCamelCase					)
		model.eval()

		SCREAMING_SNAKE_CASE				:							Optional[int]						=	"""cuda""" if torch.cuda.is_available() else """cpu"""
		model.to(__UpperCamelCase					)

		# load image processor
		SCREAMING_SNAKE_CASE				:							Tuple						=	DetaImageProcessor(format="""coco_detection"""					)

		# verify our conversion on image
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	prepare_img()
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	processor(images=__UpperCamelCase	,	return_tensors="""pt"""					)
		SCREAMING_SNAKE_CASE				:							str						=	encoding["""pixel_values"""]
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(pixel_values.to(__UpperCamelCase					)					)

		# verify logits
		if model_name == "deta-resnet-50":
				SCREAMING_SNAKE_CASE				:							str						=	torch.tensor(
				    [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]					)
				SCREAMING_SNAKE_CASE				:							Tuple						=	torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]					)
		elif model_name == "deta-resnet-50-24-epochs":
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	torch.tensor(
				    [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]					)
				SCREAMING_SNAKE_CASE				:							Any						=	torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]					)

		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!"""					)

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

		# 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__":
		__UpperCAmelCase         =   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."""
		)
		__UpperCAmelCase         =   parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__UpperCAmelCase         =   logging.get_logger(__name__)




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	Dict     ,		*lowerCamelCase_							:	Optional[int]     ,		**lowerCamelCase_							:	Optional[int]		):

							'''simple docstring'''



							warnings.warn(
							    """The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use DetrImageProcessor instead."""     ,		lowerCamelCase_     ,		)
							super().__init__(*lowerCamelCase_     ,		**lowerCamelCase_		)



