



"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase				:				Union[str, Any]   		=		logging.get_logger(__name__)






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						lowerCamelCase__	:	Any				=192
						lowerCamelCase__	:	Tuple				=768
						lowerCamelCase__	:	Dict				=12
						lowerCamelCase__	:	Optional[Any]				=3
						lowerCamelCase__	:	Any				=[800, 1333]
						lowerCamelCase__	:	Optional[int]				=False
			elif yolos_name == "yolos_s_dWr":
						lowerCamelCase__	:	Any				=330
						lowerCamelCase__	:	int				=14
						lowerCamelCase__	:	Dict				=6
						lowerCamelCase__	:	Optional[int]				=1320
			elif "yolos_s" in yolos_name:
						lowerCamelCase__	:	List[str]				=384
						lowerCamelCase__	:	List[Any]				=1536
						lowerCamelCase__	:	Dict				=12
						lowerCamelCase__	:	Any				=6
			elif "yolos_b" in yolos_name:
						lowerCamelCase__	:	Any				=[800, 1344]

			lowerCamelCase__	:	Optional[Any]				=91
			lowerCamelCase__	:	Optional[int]				='huggingface/label-files'
			lowerCamelCase__	:	Tuple				='coco-detection-id2label.json'
			lowerCamelCase__	:	Dict				=json.load(open(hf_hub_download(__lowerCamelCase							,	__lowerCamelCase							,	repo_type='''dataset'''			)							,	'''r'''			)			)
			lowerCamelCase__	:	List[str]				={int(__lowerCamelCase			): v for k, v in idalabel.items()}
			lowerCamelCase__	:	Union[str, Any]				=idalabel
			lowerCamelCase__	:	List[str]				={v: k for k, v in idalabel.items()}

			return config






def   snake_case__     (	__lowerCamelCase       :					dict							,	__lowerCamelCase       :					YolosConfig							,	__lowerCamelCase       :					bool = False			):
			"""simple docstring"""
			for i in range(config.num_hidden_layers			):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						lowerCamelCase__	:	Any				=state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''			)
						lowerCamelCase__	:	str				=state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''			)
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase__	:	List[Any]				=in_proj_weight[: config.hidden_size, :]
						lowerCamelCase__	:	Any				=in_proj_bias[: config.hidden_size]
						lowerCamelCase__	:	Tuple				=in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase__	:	Tuple				=in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						lowerCamelCase__	:	List[Any]				=in_proj_weight[-config.hidden_size :, :]
						lowerCamelCase__	:	Tuple				=in_proj_bias[-config.hidden_size :]






def   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			if "backbone" in name:
						lowerCamelCase__	:	str				=name.replace('''backbone'''							,	'''vit'''			)
			if "cls_token" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''cls_token'''							,	'''embeddings.cls_token'''			)
			if "det_token" in name:
						lowerCamelCase__	:	Any				=name.replace('''det_token'''							,	'''embeddings.detection_tokens'''			)
			if "mid_pos_embed" in name:
						lowerCamelCase__	:	int				=name.replace('''mid_pos_embed'''							,	'''encoder.mid_position_embeddings'''			)
			if "pos_embed" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''pos_embed'''							,	'''embeddings.position_embeddings'''			)
			if "patch_embed.proj" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''patch_embed.proj'''							,	'''embeddings.patch_embeddings.projection'''			)
			if "blocks" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''blocks'''							,	'''encoder.layer'''			)
			if "attn.proj" in name:
						lowerCamelCase__	:	Any				=name.replace('''attn.proj'''							,	'''attention.output.dense'''			)
			if "attn" in name:
						lowerCamelCase__	:	Any				=name.replace('''attn'''							,	'''attention.self'''			)
			if "norm1" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''norm1'''							,	'''layernorm_before'''			)
			if "norm2" in name:
						lowerCamelCase__	:	List[str]				=name.replace('''norm2'''							,	'''layernorm_after'''			)
			if "mlp.fc1" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''mlp.fc1'''							,	'''intermediate.dense'''			)
			if "mlp.fc2" in name:
						lowerCamelCase__	:	str				=name.replace('''mlp.fc2'''							,	'''output.dense'''			)
			if "class_embed" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''class_embed'''							,	'''class_labels_classifier'''			)
			if "bbox_embed" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''bbox_embed'''							,	'''bbox_predictor'''			)
			if "vit.norm" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''vit.norm'''							,	'''vit.layernorm'''			)

			return name






def   snake_case__     (	__lowerCamelCase       :					dict							,	__lowerCamelCase       :					YolosForObjectDetection			):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__	:	List[str]				=orig_state_dict.pop(__lowerCamelCase			)

						if "qkv" in key:
									lowerCamelCase__	:	Optional[Any]				=key.split('''.'''			)
									lowerCamelCase__	:	Tuple				=int(key_split[2]			)
									lowerCamelCase__	:	Dict				=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
									if "weight" in key:
												lowerCamelCase__	:	str				=val[:dim, :]
												lowerCamelCase__	:	Optional[Any]				=val[
												    dim : dim * 2, :
												]
												lowerCamelCase__	:	List[str]				=val[-dim:, :]
									else:
												lowerCamelCase__	:	Tuple				=val[:dim]
												lowerCamelCase__	:	Union[str, Any]				=val[dim : dim * 2]
												lowerCamelCase__	:	Union[str, Any]				=val[-dim:]
						else:
									lowerCamelCase__	:	str				=val

			return orig_state_dict






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				='http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCamelCase__	:	Tuple				=Image.open(requests.get(__lowerCamelCase							,	stream=__lowerCamelCase			).raw			)
			return im






@torch.no_grad()
def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					bool = False			):
			"""simple docstring"""
			lowerCamelCase__	:	Tuple				=get_yolos_config(__lowerCamelCase			)

			# load original state_dict
			lowerCamelCase__	:	Union[str, Any]				=torch.load(__lowerCamelCase							,	map_location='''cpu'''			)['model']

			# load 🤗 model
			lowerCamelCase__	:	Tuple				=YolosForObjectDetection(__lowerCamelCase			)
			model.eval()
			lowerCamelCase__	:	Tuple				=convert_state_dict(__lowerCamelCase							,	__lowerCamelCase			)
			model.load_state_dict(__lowerCamelCase			)

			# Check outputs on an image, prepared by YolosImageProcessor
			lowerCamelCase__	:	Optional[Any]				=800 if yolos_name != 'yolos_ti' else 512
			lowerCamelCase__	:	Any				=YolosImageProcessor(format='''coco_detection'''							,	size=__lowerCamelCase			)
			lowerCamelCase__	:	Any				=image_processor(images=prepare_img()							,	return_tensors='''pt'''			)
			lowerCamelCase__	:	int				=model(**__lowerCamelCase			)
			lowerCamelCase__	:	str				=outputs.logits, outputs.pred_boxes

			lowerCamelCase__	:	int				=None, None
			if yolos_name == "yolos_ti":
						lowerCamelCase__	:	Tuple				=torch.tensor(
						    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]			)
						lowerCamelCase__	:	Tuple				=torch.tensor(
						    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]			)
			elif yolos_name == "yolos_s_200_pre":
						lowerCamelCase__	:	List[str]				=torch.tensor(
						    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]			)
						lowerCamelCase__	:	List[str]				=torch.tensor(
						    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]			)
			elif yolos_name == "yolos_s_300_pre":
						lowerCamelCase__	:	Union[str, Any]				=torch.tensor(
						    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]			)
						lowerCamelCase__	:	List[Any]				=torch.tensor(
						    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]			)
			elif yolos_name == "yolos_s_dWr":
						lowerCamelCase__	:	List[str]				=torch.tensor(
						    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]			)
						lowerCamelCase__	:	List[Any]				=torch.tensor(
						    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]			)
			elif yolos_name == "yolos_base":
						lowerCamelCase__	:	Dict				=torch.tensor(
						    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]			)
						lowerCamelCase__	:	Optional[Any]				=torch.tensor(
						    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]			)
			else:
						raise ValueError(f'''Unknown yolos_name: {yolos_name}'''			)

			assert torch.allclose(logits[0, :3, :3]							,	__lowerCamelCase							,	atol=1e-4			)
			assert torch.allclose(pred_boxes[0, :3, :3]							,	__lowerCamelCase							,	atol=1e-4			)

			Path(__lowerCamelCase			).mkdir(exist_ok=__lowerCamelCase			)
			print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
			model.save_pretrained(__lowerCamelCase			)
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''			)
			image_processor.save_pretrained(__lowerCamelCase			)

			if push_to_hub:
						lowerCamelCase__	:	List[str]				={
						    'yolos_ti': 'yolos-tiny',
						    'yolos_s_200_pre': 'yolos-small',
						    'yolos_s_300_pre': 'yolos-small-300',
						    'yolos_s_dWr': 'yolos-small-dwr',
						    'yolos_base': 'yolos-base',
						}

						print('''Pushing to the hub...'''			)
						lowerCamelCase__	:	Optional[Any]				=model_mapping[yolos_name]
						image_processor.push_to_hub(__lowerCamelCase							,	organization='''hustvl'''			)
						model.push_to_hub(__lowerCamelCase							,	organization='''hustvl'''			)


if __name__ == "__main__":
		_lowercase				:				List[str]   		=		argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--yolos_name",
		    default="yolos_s_200_pre",
		    type=str,
		    help=(
		        "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
		        " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
		    ),
		)
		parser.add_argument(
		    "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
		)

		_lowercase				:				Dict   		=		parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""






from __future__ import annotations

from collections.abc import Iterator

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						str,						lowerCamelCase							:						int  )->      None:
								lowerCamelCase__	:	str				=value
								lowerCamelCase__	:	Node | None				=None
								lowerCamelCase__	:	Node | None				=None





class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						Node  )->      None:
								lowerCamelCase__	:	Any				=tree



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Node | None  )->      int:
								if node is None:
											return 0
								return node.value + (
								    self.depth_first_search(node.left  ) + self.depth_first_search(node.right  )
								)




					def __iter__( self							:						Dict  )->      Iterator[int]:
								yield self.depth_first_search(self.tree  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




