





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



def 					__lowerCamelCase       (							a_					:		str							)      ->				YolosConfig:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  YolosConfig()

      # size of the architecture
      if "yolos_ti" in yolos_name:
            __SCREAMING_SNAKE_CASE       :Any		    =  1_92
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  7_68
            __SCREAMING_SNAKE_CASE       :str		    =  12
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  3
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  [8_00, 13_33]
            __SCREAMING_SNAKE_CASE       :Dict		    =  False
      elif yolos_name == "yolos_s_dWr":
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  3_30
            __SCREAMING_SNAKE_CASE       :Tuple		    =  14
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  6
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  13_20
      elif "yolos_s" in yolos_name:
            __SCREAMING_SNAKE_CASE       :int		    =  3_84
            __SCREAMING_SNAKE_CASE       :Any		    =  15_36
            __SCREAMING_SNAKE_CASE       :int		    =  12
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  6
      elif "yolos_b" in yolos_name:
            __SCREAMING_SNAKE_CASE       :Any		    =  [8_00, 13_44]

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

      return config



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



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

      return name



def 					__lowerCamelCase       (							a_					:		dict			,       a_					:		YolosForObjectDetection							)      ->				dict:
      for key in orig_state_dict.copy().keys():
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  orig_state_dict.pop(a_							)

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

      return orig_state_dict



def 					__lowerCamelCase       (							)      ->				torch.Tensor:
      __SCREAMING_SNAKE_CASE       :int		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :List[str]		    =  Image.open(requests.get(a_			,       stream=a_							).raw							)
      return im



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str			,       a_					:		str			,       a_					:		bool = False							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :int		    =  get_yolos_config(a_							)

      # load original state_dict
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.load(a_			,       map_location='''cpu'''							)['''model''']

      # load 🤗 model
      __SCREAMING_SNAKE_CASE       :List[str]		    =  YolosForObjectDetection(a_							)
      model.eval()
      __SCREAMING_SNAKE_CASE       :str		    =  convert_state_dict(a_			,       a_							)
      model.load_state_dict(a_							)

      # Check outputs on an image, prepared by YolosImageProcessor
      __SCREAMING_SNAKE_CASE       :Any		    =  8_00 if yolos_name != '''yolos_ti''' else 5_12
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  YolosImageProcessor(format='''coco_detection'''			,       size=a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  image_processor(images=prepare_img()			,       return_tensors='''pt'''							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  model(**a_							)
      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[Any]		    =  outputs.logits, outputs.pred_boxes

      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[Any]		    =  None, None
      if yolos_name == "yolos_ti":
            __SCREAMING_SNAKE_CASE       :Dict		    =  torch.tensor(
                [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]							)
            __SCREAMING_SNAKE_CASE       :List[str]		    =  torch.tensor(
                [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]							)
      elif yolos_name == "yolos_s_200_pre":
            __SCREAMING_SNAKE_CASE       :int		    =  torch.tensor(
                [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]							)
            __SCREAMING_SNAKE_CASE       :str		    =  torch.tensor(
                [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]							)
      elif yolos_name == "yolos_s_300_pre":
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.tensor(
                [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]							)
            __SCREAMING_SNAKE_CASE       :str		    =  torch.tensor(
                [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]							)
      elif yolos_name == "yolos_s_dWr":
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.tensor(
                [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]							)
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.tensor(
                [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]							)
      elif yolos_name == "yolos_base":
            __SCREAMING_SNAKE_CASE       :List[str]		    =  torch.tensor(
                [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]							)
            __SCREAMING_SNAKE_CASE       :List[str]		    =  torch.tensor(
                [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]							)
      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:
            __SCREAMING_SNAKE_CASE       :int		    =  {
                '''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...'''							)
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model_mapping[yolos_name]
            image_processor.push_to_hub(a_			,       organization='''hustvl'''							)
            model.push_to_hub(a_			,       organization='''hustvl'''							)


if __name__ == "__main__":
  lowerCamelCase_       =		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."
  )

  lowerCamelCase_       =		parser.parse_args()
  convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int = 10			,       a_					:		int = 22							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  range(1			,       a_							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  range(1			,       a_							)
      return sum(
          1 for power in powers for base in bases if len(str(base**power							)							) == power							)


if __name__ == "__main__":
  print(f'{solution(1_0, 2_2) = }')