




_A     =			{
    0: '''0''',
    1: '''1''',
    2: '''2''',
    3: '''3''',
    4: '''4''',
    5: '''5''',
    6: '''6''',
    7: '''7''',
    8: '''8''',
    9: '''9''',
    10: '''a''',
    11: '''b''',
    12: '''c''',
    13: '''d''',
    14: '''e''',
    15: '''f''',
}





def __UpperCamelCase			(       _A  ):
						assert type(_A  ) in (int, float) and decimal == int(_A  )
						lowerCAmelCase_      			=  int(_A  )
						lowerCAmelCase_      			=  ''''''
						lowerCAmelCase_      			=  False
						if decimal < 0:
												lowerCAmelCase_      			=  True
												decimal *= -1
						while decimal > 0:
												lowerCAmelCase_			,					lowerCAmelCase_      			=  divmod(_A ,  16  )
												lowerCAmelCase_      			=  values[remainder] + hexadecimal
						lowerCAmelCase_      			=  '''0x''' + hexadecimal
						if negative:
												lowerCAmelCase_      			=  '''-''' + hexadecimal
						return hexadecimal


if __name__ == "__main__":
						import doctest

						doctest.testmod()










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     =			logging.get_logger(__name__)





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  YolosConfig()

						# size of the architecture
						if "yolos_ti" in yolos_name:
												lowerCAmelCase_      			=  192
												lowerCAmelCase_      			=  768
												lowerCAmelCase_      			=  12
												lowerCAmelCase_      			=  3
												lowerCAmelCase_      			=  [800, 1333]
												lowerCAmelCase_      			=  False
						elif yolos_name == "yolos_s_dWr":
												lowerCAmelCase_      			=  330
												lowerCAmelCase_      			=  14
												lowerCAmelCase_      			=  6
												lowerCAmelCase_      			=  1320
						elif "yolos_s" in yolos_name:
												lowerCAmelCase_      			=  384
												lowerCAmelCase_      			=  1536
												lowerCAmelCase_      			=  12
												lowerCAmelCase_      			=  6
						elif "yolos_b" in yolos_name:
												lowerCAmelCase_      			=  [800, 1344]

						lowerCAmelCase_      			=  91
						lowerCAmelCase_      			=  '''huggingface/label-files'''
						lowerCAmelCase_      			=  '''coco-detection-id2label.json'''
						lowerCAmelCase_      			=  json.load(open(hf_hub_download(_A ,  _A ,  repo_type='''dataset'''  ) ,  '''r'''  )  )
						lowerCAmelCase_      			=  {int(_A  ): v for k, v in idalabel.items()}
						lowerCAmelCase_      			=  idalabel
						lowerCAmelCase_      			=  {v: k for k, v in idalabel.items()}

						return config





def __UpperCamelCase			(       _A ,  _A ,  _A = False  ):
						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_      			=  state_dict.pop(f"blocks.{i}.attn.qkv.weight"  )
												lowerCAmelCase_      			=  state_dict.pop(f"blocks.{i}.attn.qkv.bias"  )
												# next, add query, keys and values (in that order) to the state dict
												lowerCAmelCase_      			=  in_proj_weight[: config.hidden_size, :]
												lowerCAmelCase_      			=  in_proj_bias[: config.hidden_size]
												lowerCAmelCase_      			=  in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase_      			=  in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCAmelCase_      			=  in_proj_weight[-config.hidden_size :, :]
												lowerCAmelCase_      			=  in_proj_bias[-config.hidden_size :]





def __UpperCamelCase			(       _A  ):
						if "backbone" in name:
												lowerCAmelCase_      			=  name.replace('''backbone''' ,  '''vit'''  )
						if "cls_token" in name:
												lowerCAmelCase_      			=  name.replace('''cls_token''' ,  '''embeddings.cls_token'''  )
						if "det_token" in name:
												lowerCAmelCase_      			=  name.replace('''det_token''' ,  '''embeddings.detection_tokens'''  )
						if "mid_pos_embed" in name:
												lowerCAmelCase_      			=  name.replace('''mid_pos_embed''' ,  '''encoder.mid_position_embeddings'''  )
						if "pos_embed" in name:
												lowerCAmelCase_      			=  name.replace('''pos_embed''' ,  '''embeddings.position_embeddings'''  )
						if "patch_embed.proj" in name:
												lowerCAmelCase_      			=  name.replace('''patch_embed.proj''' ,  '''embeddings.patch_embeddings.projection'''  )
						if "blocks" in name:
												lowerCAmelCase_      			=  name.replace('''blocks''' ,  '''encoder.layer'''  )
						if "attn.proj" in name:
												lowerCAmelCase_      			=  name.replace('''attn.proj''' ,  '''attention.output.dense'''  )
						if "attn" in name:
												lowerCAmelCase_      			=  name.replace('''attn''' ,  '''attention.self'''  )
						if "norm1" in name:
												lowerCAmelCase_      			=  name.replace('''norm1''' ,  '''layernorm_before'''  )
						if "norm2" in name:
												lowerCAmelCase_      			=  name.replace('''norm2''' ,  '''layernorm_after'''  )
						if "mlp.fc1" in name:
												lowerCAmelCase_      			=  name.replace('''mlp.fc1''' ,  '''intermediate.dense'''  )
						if "mlp.fc2" in name:
												lowerCAmelCase_      			=  name.replace('''mlp.fc2''' ,  '''output.dense'''  )
						if "class_embed" in name:
												lowerCAmelCase_      			=  name.replace('''class_embed''' ,  '''class_labels_classifier'''  )
						if "bbox_embed" in name:
												lowerCAmelCase_      			=  name.replace('''bbox_embed''' ,  '''bbox_predictor'''  )
						if "vit.norm" in name:
												lowerCAmelCase_      			=  name.replace('''vit.norm''' ,  '''vit.layernorm'''  )

						return name





def __UpperCamelCase			(       _A ,  _A  ):
						for key in orig_state_dict.copy().keys():
												lowerCAmelCase_      			=  orig_state_dict.pop(_A  )

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

						return orig_state_dict





def __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowerCAmelCase_      			=  Image.open(requests.get(_A ,  stream=_A  ).raw  )
						return im





@torch.no_grad()
def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A = False  ):
						lowerCAmelCase_      			=  get_yolos_config(_A  )

						# load original state_dict
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )['''model''']

						# load 🤗 model
						lowerCAmelCase_      			=  YolosForObjectDetection(_A  )
						model.eval()
						lowerCAmelCase_      			=  convert_state_dict(_A ,  _A  )
						model.load_state_dict(_A  )

						# Check outputs on an image, prepared by YolosImageProcessor
						lowerCAmelCase_      			=  800 if yolos_name != '''yolos_ti''' else 512
						lowerCAmelCase_      			=  YolosImageProcessor(format='''coco_detection''' ,  size=_A  )
						lowerCAmelCase_      			=  image_processor(images=prepare_img() ,  return_tensors='''pt'''  )
						lowerCAmelCase_      			=  model(**_A  )
						lowerCAmelCase_			,					lowerCAmelCase_      			=  outputs.logits, outputs.pred_boxes

						lowerCAmelCase_			,					lowerCAmelCase_      			=  None, None
						if yolos_name == "yolos_ti":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]  )
						elif yolos_name == "yolos_s_200_pre":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]  )
						elif yolos_name == "yolos_s_300_pre":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]  )
						elif yolos_name == "yolos_s_dWr":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]  )
						elif yolos_name == "yolos_base":
												lowerCAmelCase_      			=  torch.tensor(
												    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]  )
												lowerCAmelCase_      			=  torch.tensor(
												    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]  )
						else:
												raise ValueError(f"Unknown yolos_name: {yolos_name}"  )

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

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

						if push_to_hub:
												lowerCAmelCase_      			=  {
												    '''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_      			=  model_mapping[yolos_name]
												image_processor.push_to_hub(_A ,  organization='''hustvl'''  )
												model.push_to_hub(_A ,  organization='''hustvl'''  )


if __name__ == "__main__":
						_A     =			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     =			parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




