





from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
     import torch

if is_tf_available():
     import tensorflow as tf



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			["image_processor"]
 lowercase_		=			"SamImageProcessor"

 def __init__(self				:   Any       , UpperCAmelCase_				:   Dict)       ->Dict:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =self.image_processor
   lowerCamelCase__:      str			  =-10
   lowerCamelCase__:      Tuple			  =self.image_processor.size["longest_edge"]

 def __call__(self				:   Optional[Any]       , UpperCAmelCase_				:   Union[str, Any]=None       , UpperCAmelCase_				:   Union[str, Any]=None       , UpperCAmelCase_				:   Optional[int]=None       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   Optional[Union[str, TensorType]] = None       , **UpperCAmelCase_				:   Union[str, Any]       , )       ->BatchEncoding:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.image_processor(
       UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , **UpperCAmelCase_       , )

   # pop arguments that are not used in the foward but used nevertheless
   lowerCamelCase__:      Tuple			  =encoding_image_processor["original_sizes"]

   if hasattr(UpperCAmelCase_       , "numpy"):  # Checks if Torch or TF tensor
     lowerCamelCase__:      Optional[Any]			  =original_sizes.numpy()

   lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__:      Any			  =self._check_and_preprocess_points(
       input_points=UpperCAmelCase_       , input_labels=UpperCAmelCase_       , input_boxes=UpperCAmelCase_       , )

   lowerCamelCase__:      Optional[int]			  =self._normalize_and_convert(
       UpperCAmelCase_       , UpperCAmelCase_       , input_points=UpperCAmelCase_       , input_labels=UpperCAmelCase_       , input_boxes=UpperCAmelCase_       , return_tensors=UpperCAmelCase_       , )

   return encoding_image_processor

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Tuple=None       , UpperCAmelCase_				:   Dict=None       , UpperCAmelCase_				:   Dict=None       , UpperCAmelCase_				:   Tuple="pt"       , )       ->List[str]:



   '''simple docstring'''



   if input_points is not None:
     if len(UpperCAmelCase_) != len(UpperCAmelCase_):
       lowerCamelCase__:      int			  =[
           self._normalize_coordinates(self.target_size       , UpperCAmelCase_       , original_sizes[0]) for point in input_points
       ]
     else:
       lowerCamelCase__:      Tuple			  =[
           self._normalize_coordinates(self.target_size       , UpperCAmelCase_       , UpperCAmelCase_)
           for point, original_size in zip(UpperCAmelCase_       , UpperCAmelCase_)
       ]
     # check that all arrays have the same shape
     if not all(point.shape == input_points[0].shape for point in input_points):
       if input_labels is not None:
         lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =self._pad_points_and_labels(UpperCAmelCase_       , UpperCAmelCase_)

     lowerCamelCase__:      Optional[Any]			  =np.array(UpperCAmelCase_)

   if input_labels is not None:
     lowerCamelCase__:      Tuple			  =np.array(UpperCAmelCase_)

   if input_boxes is not None:
     if len(UpperCAmelCase_) != len(UpperCAmelCase_):
       lowerCamelCase__:      Union[str, Any]			  =[
           self._normalize_coordinates(self.target_size       , UpperCAmelCase_       , original_sizes[0]       , is_bounding_box=UpperCAmelCase_)
           for box in input_boxes
       ]
     else:
       lowerCamelCase__:      List[Any]			  =[
           self._normalize_coordinates(self.target_size       , UpperCAmelCase_       , UpperCAmelCase_       , is_bounding_box=UpperCAmelCase_)
           for box, original_size in zip(UpperCAmelCase_       , UpperCAmelCase_)
       ]
     lowerCamelCase__:      Optional[int]			  =np.array(UpperCAmelCase_)

   if input_boxes is not None:
     if return_tensors == "pt":
       lowerCamelCase__:      int			  =torch.from_numpy(UpperCAmelCase_)
       # boxes batch size of 1 by default
       lowerCamelCase__:      int			  =input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
     elif return_tensors == "tf":
       lowerCamelCase__:      Tuple			  =tf.convert_to_tensor(UpperCAmelCase_)
       # boxes batch size of 1 by default
       lowerCamelCase__:      Optional[int]			  =tf.expand_dims(UpperCAmelCase_       , 1) if len(input_boxes.shape) != 3 else input_boxes
     encoding_image_processor.update({"input_boxes": input_boxes})
   if input_points is not None:
     if return_tensors == "pt":
       lowerCamelCase__:      Optional[Any]			  =torch.from_numpy(UpperCAmelCase_)
       # point batch size of 1 by default
       lowerCamelCase__:      List[str]			  =input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
     elif return_tensors == "tf":
       lowerCamelCase__:      Tuple			  =tf.convert_to_tensor(UpperCAmelCase_)
       # point batch size of 1 by default
       lowerCamelCase__:      Union[str, Any]			  =tf.expand_dims(UpperCAmelCase_       , 1) if len(input_points.shape) != 4 else input_points
     encoding_image_processor.update({"input_points": input_points})
   if input_labels is not None:
     if return_tensors == "pt":
       lowerCamelCase__:      Optional[int]			  =torch.from_numpy(UpperCAmelCase_)
       # point batch size of 1 by default
       lowerCamelCase__:      Dict			  =input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
     elif return_tensors == "tf":
       lowerCamelCase__:      Union[str, Any]			  =tf.convert_to_tensor(UpperCAmelCase_)
       # point batch size of 1 by default
       lowerCamelCase__:      Optional[int]			  =tf.expand_dims(UpperCAmelCase_       , 1) if len(input_labels.shape) != 3 else input_labels
     encoding_image_processor.update({"input_labels": input_labels})

   return encoding_image_processor

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   List[str])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      int			  =max([point.shape[0] for point in input_points])
   lowerCamelCase__:      Optional[int]			  =[]
   for i, point in enumerate(UpperCAmelCase_):
     if point.shape[0] != expected_nb_points:
       lowerCamelCase__:      int			  =np.concatenate(
           [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value]       , axis=0)
       lowerCamelCase__:      Dict			  =np.append(input_labels[i]       , [self.point_pad_value])
     processed_input_points.append(UpperCAmelCase_)
   lowerCamelCase__:      Union[str, Any]			  =processed_input_points
   return input_points, input_labels

 def 							SCREAMING_SNAKE_CASE_					(self				:   str       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Dict=False)       ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      str			  =original_size
   lowerCamelCase__  ,					lowerCamelCase__:      str			  =self.image_processor._get_preprocess_shape(UpperCAmelCase_       , longest_edge=UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =deepcopy(UpperCAmelCase_).astype(UpperCAmelCase_)

   if is_bounding_box:
     lowerCamelCase__:      Optional[int]			  =coords.reshape(-1       , 2       , 2)

   lowerCamelCase__:      Any			  =coords[..., 0] * (new_w / old_w)
   lowerCamelCase__:      Any			  =coords[..., 1] * (new_h / old_h)

   if is_bounding_box:
     lowerCamelCase__:      str			  =coords.reshape(-1       , 4)

   return coords

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Tuple=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   List[str]=None       , )       ->Optional[Any]:



   '''simple docstring'''



   if input_points is not None:
     if hasattr(UpperCAmelCase_       , "numpy"):  # Checks for TF or Torch tensor
       lowerCamelCase__:      List[str]			  =input_points.numpy().tolist()

     if not isinstance(UpperCAmelCase_       , UpperCAmelCase_) or not isinstance(input_points[0]       , UpperCAmelCase_):
       raise ValueError("Input points must be a list of list of floating points.")
     lowerCamelCase__:      Dict			  =[np.array(UpperCAmelCase_) for input_point in input_points]
   else:
     lowerCamelCase__:      List[str]			  =None

   if input_labels is not None:
     if hasattr(UpperCAmelCase_       , "numpy"):
       lowerCamelCase__:      str			  =input_labels.numpy().tolist()

     if not isinstance(UpperCAmelCase_       , UpperCAmelCase_) or not isinstance(input_labels[0]       , UpperCAmelCase_):
       raise ValueError("Input labels must be a list of list integers.")
     lowerCamelCase__:      Tuple			  =[np.array(UpperCAmelCase_) for label in input_labels]
   else:
     lowerCamelCase__:      Optional[Any]			  =None

   if input_boxes is not None:
     if hasattr(UpperCAmelCase_       , "numpy"):
       lowerCamelCase__:      str			  =input_boxes.numpy().tolist()

     if (
         not isinstance(UpperCAmelCase_       , UpperCAmelCase_)
         or not isinstance(input_boxes[0]       , UpperCAmelCase_)
         or not isinstance(input_boxes[0][0]       , UpperCAmelCase_)
     ):
       raise ValueError("Input boxes must be a list of list of list of floating points.")
     lowerCamelCase__:      int			  =[np.array(UpperCAmelCase_).astype(np.floataa) for box in input_boxes]
   else:
     lowerCamelCase__:      Any			  =None

   return input_points, input_labels, input_boxes

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.image_processor.model_input_names
   return list(dict.fromkeys(UpperCAmelCase_))







 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , *UpperCAmelCase_				:   int       , **UpperCAmelCase_				:   str)       ->Tuple:



   '''simple docstring'''



   return self.image_processor.post_process_masks(*UpperCAmelCase_       , **UpperCAmelCase_)








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      lowerCAmelCase_ (       __a		)     ->		YolosConfig:







  """simple docstring"""





  lowerCamelCase__:      str			  =YolosConfig()

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

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

  return config





def      lowerCAmelCase_ (       __a	,    __a	,    __a = False		)     ->		Dict:







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





def      lowerCAmelCase_ (       __a		)     ->		str:







  """simple docstring"""





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

  return name





def      lowerCAmelCase_ (       __a	,    __a		)     ->		dict:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    lowerCamelCase__:      Any			  =orig_state_dict.pop(__a		)

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

  return orig_state_dict





def      lowerCAmelCase_ (       )     ->		torch.Tensor:







  """simple docstring"""





  lowerCamelCase__:      Any			  ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:      Optional[Any]			  =Image.open(requests.get(__a	,    stream=__a		).raw		)
  return im





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      int			  =get_yolos_config(__a		)

  # load original state_dict
  lowerCamelCase__:      Optional[int]			  =torch.load(__a	,    map_location="cpu"		)["model"]

  # load 🤗 model
  lowerCamelCase__:      int			  =YolosForObjectDetection(__a		)
  model.eval()
  lowerCamelCase__:      Union[str, Any]			  =convert_state_dict(__a	,    __a		)
  model.load_state_dict(__a		)

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

  lowerCamelCase__  ,					lowerCamelCase__:      Any			  =None, None
  if yolos_name == "yolos_ti":
    lowerCamelCase__:      Optional[Any]			  =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__:      List[Any]			  =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__:      Optional[int]			  =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__:      Any			  =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__:      str			  =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__:      Optional[Any]			  =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__:      str			  =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__:      Union[str, Any]			  =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__:      Tuple			  =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__:      Optional[int]			  =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__:      Any			  ={
        "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[int]			  =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)

