


"""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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase						:				Dict             =      logging.get_logger(__name__)







def  __snake_case       (   UpperCamelCase__	)      ->		List[str]:







		"""simple docstring"""





		A					       = SwinConfig(
		    embed_dim=192 ,			depths=(2, 2, 18, 2) ,			num_heads=(6, 12, 24, 48) ,			window_size=12 ,			out_features=['stage2', 'stage3', 'stage4'] ,			)

		A					       = DetaConfig(
		    backbone_config=UpperCamelCase__ ,			num_queries=900 ,			encoder_ffn_dim=2048 ,			decoder_ffn_dim=2048 ,			num_feature_levels=5 ,			assign_first_stage=UpperCamelCase__ ,			with_box_refine=UpperCamelCase__ ,			two_stage=UpperCamelCase__ ,			)

		# set labels
		A					       = 'huggingface/label-files'
		if "o365" in model_name:
				A					       = 366
				A					       = 'object365-id2label.json'
		else:
				A					       = 91
				A					       = 'coco-detection-id2label.json'

		A					       = num_labels
		A					       = json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,			UpperCamelCase__ ,			repo_type='dataset'	)	) ,			'r'	)	)
		A					       = {int(UpperCamelCase__	): v for k, v in idalabel.items()}
		A					       = idalabel
		A					       = {v: k for k, v in idalabel.items()}

		return config







def  __snake_case       (   UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = []

		# stem
		# fmt: off
		rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight')	)
		rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias')	)
		rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight')	)
		rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias')	)
		# stages
		for i in range(len(config.backbone_config.depths	)	):
				for j in range(config.backbone_config.depths[i]	):
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')	)

				if i < 3:
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')	)
						rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')	)

		rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight')	)
		rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias')	)
		rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight')	)
		rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias')	)
		rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight')	)
		rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias')	)

		# 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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Any:







		"""simple docstring"""





		A					       = dct.pop(UpperCamelCase__	)
		A					       = val







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
		for i in range(len(backbone_config.depths	)	):
				A					       = num_features[i]
				for j in range(backbone_config.depths[i]	):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						A					       = state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'	)
						A					       = state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'	)
						# next, add query, keys and values (in that order) to the state dict
						A					       = in_proj_weight[:dim, :]
						A					       = in_proj_bias[: dim]
						A					       = in_proj_weight[
						    dim : dim * 2, :
						]
						A					       = in_proj_bias[
						    dim : dim * 2
						]
						A					       = in_proj_weight[
						    -dim :, :
						]
						A					       = in_proj_bias[-dim :]
						# fmt: on







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





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







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





		A					       = 'http://images.cocodataset.org/val2017/000000039769.jpg'
		A					       = Image.open(requests.get(UpperCamelCase__ ,			stream=UpperCamelCase__	).raw	)

		return im







@torch.no_grad()
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = get_deta_config(UpperCamelCase__	)

		# load original state dict
		if model_name == "deta-swin-large":
				A					       = hf_hub_download(repo_id='nielsr/deta-checkpoints' ,			filename='adet_swin_ft.pth'	)
		elif model_name == "deta-swin-large-o365":
				A					       = hf_hub_download(repo_id='jozhang97/deta-swin-l-o365' ,			filename='deta_swin_pt_o365.pth'	)
		else:
				raise ValueError(f'Model name {model_name} not supported'	)

		A					       = torch.load(UpperCamelCase__ ,			map_location='cpu'	)['model']

		# original state dict
		for name, param in state_dict.items():
				print(UpperCamelCase__ ,			param.shape	)

		# rename keys
		A					       = create_rename_keys(UpperCamelCase__	)
		for src, dest in rename_keys:
				rename_key(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)
		read_in_swin_q_k_v(UpperCamelCase__ ,			config.backbone_config	)
		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:
						A					       = state_dict.pop(UpperCamelCase__	)
						A					       = val
				if "input_proj" in key:
						A					       = state_dict.pop(UpperCamelCase__	)
						A					       = val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						A					       = state_dict.pop(UpperCamelCase__	)
						A					       = val

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

		A					       = 'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(UpperCamelCase__	)

		# load image processor
		A					       = DetaImageProcessor(format='coco_detection'	)

		# verify our conversion on image
		A					       = prepare_img()
		A					       = processor(images=UpperCamelCase__ ,			return_tensors='pt'	)
		A					       = encoding['pixel_values']
		A					       = model(pixel_values.to(UpperCamelCase__	)	)

		# verify logits
		print('Logits:' ,			outputs.logits[0, :3, :3]	)
		print('Boxes:' ,			outputs.pred_boxes[0, :3, :3]	)
		if model_name == "deta-swin-large":
				A					       = torch.tensor(
				    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]]	)
				A					       = torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]]	)
		elif model_name == "deta-swin-large-o365":
				A					       = torch.tensor(
				    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]]	)
				A					       = torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]]	)
		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						:				List[str]             =      argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    type=str,
		    default="deta-swin-large",
		    choices=["deta-swin-large", "deta-swin-large-o365"],
		    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						:				Tuple             =      parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""


def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = " "	)      ->		list:







		"""simple docstring"""





		A					       = []

		A					       = 0
		for index, char in enumerate(UpperCamelCase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						A					       = index + 1
				elif index + 1 == len(UpperCamelCase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()





