






'''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()
A:      str  				=				logging.get_logger(__name__)






def 	_UpperCAmelCase    (  a		:	str							)			->			YolosConfig:





						"""simple docstring"""
						lowercase_      :   str        =			YolosConfig()

						# size of the architecture
						if "yolos_ti" in yolos_name:
												lowercase_      :   str        =			1_9_2
												lowercase_      :   Optional[Any]        =			7_6_8
												lowercase_      :   Optional[Any]        =			1_2
												lowercase_      :   Any        =			3
												lowercase_      :   int        =			[8_0_0, 1_3_3_3]
												lowercase_      :   Tuple        =			False
						elif yolos_name == "yolos_s_dWr":
												lowercase_      :   Dict        =			3_3_0
												lowercase_      :   List[Any]        =			1_4
												lowercase_      :   int        =			6
												lowercase_      :   str        =			1_3_2_0
						elif "yolos_s" in yolos_name:
												lowercase_      :   Union[str, Any]        =			3_8_4
												lowercase_      :   str        =			1_5_3_6
												lowercase_      :   List[str]        =			1_2
												lowercase_      :   Optional[Any]        =			6
						elif "yolos_b" in yolos_name:
												lowercase_      :   Union[str, Any]        =			[8_0_0, 1_3_4_4]

						lowercase_      :   List[Any]        =			9_1
						lowercase_      :   Dict        =			"huggingface/label-files"
						lowercase_      :   str        =			"coco-detection-id2label.json"
						lowercase_      :   Any        =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			repo_type='dataset'							)						,			'r'							)							)
						lowercase_      :   int        =			{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
						lowercase_      :   Optional[Any]        =			idalabel
						lowercase_      :   Dict        =			{v: k for k, v in idalabel.items()}

						return config






def 	_UpperCAmelCase    (  a		:	dict						,			a		:	YolosConfig						,			a		:	bool = False							)			->			Optional[Any]:





						"""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)
												lowercase_      :   Any        =			state_dict.pop(f"blocks.{i}.attn.qkv.weight"							)
												lowercase_      :   Union[str, Any]        =			state_dict.pop(f"blocks.{i}.attn.qkv.bias"							)
												# next, add query, keys and values (in that order) to the state dict
												lowercase_      :   Any        =			in_proj_weight[: config.hidden_size, :]
												lowercase_      :   Union[str, Any]        =			in_proj_bias[: config.hidden_size]
												lowercase_      :   Union[str, Any]        =			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowercase_      :   Any        =			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowercase_      :   int        =			in_proj_weight[-config.hidden_size :, :]
												lowercase_      :   List[Any]        =			in_proj_bias[-config.hidden_size :]






def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""

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

						return name






def 	_UpperCAmelCase    (  a		:	dict						,			a		:	YolosForObjectDetection							)			->			dict:





						"""simple docstring"""

						for key in orig_state_dict.copy().keys():
												lowercase_      :   List[Any]        =			orig_state_dict.pop(SCREAMING_SNAKE_CASE_							)

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

						return orig_state_dict






def 	_UpperCAmelCase    (  )			->			torch.Tensor:





						"""simple docstring"""
						lowercase_      :   Union[str, Any]        =			"http://images.cocodataset.org/val2017/000000039769.jpg"
						lowercase_      :   List[Any]        =			Image.open(requests.get(SCREAMING_SNAKE_CASE_						,			stream=SCREAMING_SNAKE_CASE_							).raw							)
						return im






@torch.no_grad()
def 	_UpperCAmelCase    (  a		:	str						,			a		:	str						,			a		:	str						,			a		:	bool = False							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   str        =			get_yolos_config(SCREAMING_SNAKE_CASE_							)

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

						# load 🤗 model
						lowercase_      :   List[Any]        =			YolosForObjectDetection(SCREAMING_SNAKE_CASE_							)
						model.eval()
						lowercase_      :   int        =			convert_state_dict(SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_							)
						model.load_state_dict(SCREAMING_SNAKE_CASE_							)

						# Check outputs on an image, prepared by YolosImageProcessor
						lowercase_      :   int        =			8_0_0 if yolos_name != "yolos_ti" else 5_1_2
						lowercase_      :   Dict        =			YolosImageProcessor(format='coco_detection'						,			size=SCREAMING_SNAKE_CASE_							)
						lowercase_      :   Union[str, Any]        =			image_processor(images=prepare_img()						,			return_tensors='pt'							)
						lowercase_      :   Union[str, Any]        =			model(**SCREAMING_SNAKE_CASE_							)
						lowercase_      :   List[str]        =			outputs.logits, outputs.pred_boxes

						lowercase_      :   Optional[int]        =			None, None
						if yolos_name == "yolos_ti":
												lowercase_      :   Union[str, Any]        =			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]]							)
												lowercase_      :   Optional[Any]        =			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":
												lowercase_      :   List[Any]        =			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]]							)
												lowercase_      :   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":
												lowercase_      :   List[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]]							)
												lowercase_      :   Optional[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":
												lowercase_      :   List[Any]        =			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]]							)
												lowercase_      :   Tuple        =			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":
												lowercase_      :   List[str]        =			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]]							)
												lowercase_      :   List[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]						,			SCREAMING_SNAKE_CASE_						,			atol=1e-4							)
						assert torch.allclose(pred_boxes[0, :3, :3]						,			SCREAMING_SNAKE_CASE_						,			atol=1e-4							)

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

						if push_to_hub:
												lowercase_      :   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...'							)
												lowercase_      :   Union[str, Any]        =			model_mapping[yolos_name]
												image_processor.push_to_hub(SCREAMING_SNAKE_CASE_						,			organization='hustvl'							)
												model.push_to_hub(SCREAMING_SNAKE_CASE_						,			organization='hustvl'							)


if __name__ == "__main__":
		A:      Optional[int]  				=				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."
		)

		A:      Optional[Any]  				=				parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	int						,			a		:	int							)			->			int:





						"""simple docstring"""
						while second != 0:
												lowercase_      :   Any        =			first & second
												first ^= second
												lowercase_      :   List[str]        =			c << 1
						return first


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A:      Union[str, Any]  				=				int(input("Enter the first number: ").strip())
						A:      Union[str, Any]  				=				int(input("Enter the second number: ").strip())
						print(f"""{add(first, second) = }""")
