


"""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()
_lowerCamelCase     =  logging.get_logger(__name__)


def __lowercase     (      lowerCamelCase_  :      Dict  ):
				SCREAMING_SNAKE_CASE__     							=				SwinConfig(
				    embed_dim=192						,					depths=(2, 2, 18, 2)						,					num_heads=(6, 12, 24, 48)						,					window_size=12						,					out_features=["stage2", "stage3", "stage4"]						,					)

				SCREAMING_SNAKE_CASE__     							=				DetaConfig(
				    backbone_config=lowerCamelCase_						,					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
				SCREAMING_SNAKE_CASE__     							=				"huggingface/label-files"
				if "o365" in model_name:
								SCREAMING_SNAKE_CASE__     							=				366
								SCREAMING_SNAKE_CASE__     							=				"object365-id2label.json"
				else:
								SCREAMING_SNAKE_CASE__     							=				91
								SCREAMING_SNAKE_CASE__     							=				"coco-detection-id2label.json"

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

				return config


def __lowercase     (      lowerCamelCase_  :      Optional[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# 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 __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dct.pop(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				val


def __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      str  ):
				SCREAMING_SNAKE_CASE__     							=				[int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
				for i in range(len(backbone_config.depths  )  ):
								SCREAMING_SNAKE_CASE__     							=				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)
												SCREAMING_SNAKE_CASE__     							=				state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''  )
												SCREAMING_SNAKE_CASE__     							=				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
												SCREAMING_SNAKE_CASE__     							=				in_proj_weight[:dim, :]
												SCREAMING_SNAKE_CASE__     							=				in_proj_bias[: dim]
												SCREAMING_SNAKE_CASE__     							=				in_proj_weight[
												    dim : dim * 2, :
												]
												SCREAMING_SNAKE_CASE__     							=				in_proj_bias[
												    dim : dim * 2
												]
												SCREAMING_SNAKE_CASE__     							=				in_proj_weight[
												    -dim :, :
												]
												SCREAMING_SNAKE_CASE__     							=				in_proj_bias[-dim :]
												# fmt: on


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


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
				SCREAMING_SNAKE_CASE__     							=				Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_  ).raw  )

				return im


@torch.no_grad()
def __lowercase     (      lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Union[str, Any]  ):
				SCREAMING_SNAKE_CASE__     							=				get_deta_config(lowerCamelCase_  )

				# load original state dict
				if model_name == "deta-swin-large":
								SCREAMING_SNAKE_CASE__     							=				hf_hub_download(repo_id="nielsr/deta-checkpoints"						,					filename="adet_swin_ft.pth"  )
				elif model_name == "deta-swin-large-o365":
								SCREAMING_SNAKE_CASE__     							=				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'''  )

				SCREAMING_SNAKE_CASE__     							=				torch.load(lowerCamelCase_						,					map_location="cpu"  )["model"]

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

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

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

				SCREAMING_SNAKE_CASE__     							=				"cuda" if torch.cuda.is_available() else "cpu"
				model.to(lowerCamelCase_  )

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

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

				# verify logits
				print("Logits:"						,					outputs.logits[0, :3, :3]  )
				print("Boxes:"						,					outputs.pred_boxes[0, :3, :3]  )
				if model_name == "deta-swin-large":
								SCREAMING_SNAKE_CASE__     							=				torch.tensor(
								    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
				elif model_name == "deta-swin-large-o365":
								SCREAMING_SNAKE_CASE__     							=				torch.tensor(
								    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
								SCREAMING_SNAKE_CASE__     							=				torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
				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     =  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.'
	)
	_lowerCamelCase     =  parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
