


import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 	_lowerCAmelCase  (							lowerCAmelCase_							:Optional[Any]							)->Union[str, Any]:
   '''simple docstring'''






   if "model" in orig_key:
      snake_case_    			=    orig_key.replace("model."						,  ""							)
   if "norm1" in orig_key:
      snake_case_    			=    orig_key.replace("norm1"						,  "attention.output.LayerNorm"							)
   if "norm2" in orig_key:
      snake_case_    			=    orig_key.replace("norm2"						,  "output.LayerNorm"							)
   if "norm" in orig_key:
      snake_case_    			=    orig_key.replace("norm"						,  "LayerNorm"							)
   if "transformer" in orig_key:
      snake_case_    			=    orig_key.split("."							)[0].split("_"							)[-1]
      snake_case_    			=    orig_key.replace(F'''transformer_{layer_num}'''						,  F'''encoder.layer.{layer_num}'''							)
   if "mha.attn" in orig_key:
      snake_case_    			=    orig_key.replace("mha.attn"						,  "attention.self"							)
   if "mha" in orig_key:
      snake_case_    			=    orig_key.replace("mha"						,  "attention"							)
   if "W_q" in orig_key:
      snake_case_    			=    orig_key.replace("W_q"						,  "self.query"							)
   if "W_k" in orig_key:
      snake_case_    			=    orig_key.replace("W_k"						,  "self.key"							)
   if "W_v" in orig_key:
      snake_case_    			=    orig_key.replace("W_v"						,  "self.value"							)
   if "ff1" in orig_key:
      snake_case_    			=    orig_key.replace("ff1"						,  "intermediate.dense"							)
   if "ff2" in orig_key:
      snake_case_    			=    orig_key.replace("ff2"						,  "output.dense"							)
   if "ff" in orig_key:
      snake_case_    			=    orig_key.replace("ff"						,  "output.dense"							)
   if "mlm_class" in orig_key:
      snake_case_    			=    orig_key.replace("mlm.mlm_class"						,  "cls.predictions.decoder"							)
   if "mlm" in orig_key:
      snake_case_    			=    orig_key.replace("mlm"						,  "cls.predictions.transform"							)
   if "cls" not in orig_key:
      snake_case_    			=    "yoso." + orig_key

   return orig_key



def 	_lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:Dict							)->Optional[int]:
   '''simple docstring'''






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

      if ("pooler" in key) or ("sen_class" in key):
         continue
      else:
         snake_case_    			=    val

   snake_case_    			=    orig_state_dict["cls.predictions.decoder.bias"]
   snake_case_    			=    torch.arange(__a							).expand((1, -1)							) + 2

   return orig_state_dict



def 	_lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[str]							)->List[Any]:
   '''simple docstring'''






   snake_case_    			=    torch.load(__a						,  map_location="cpu"							)["model_state_dict"]
   snake_case_    			=    YosoConfig.from_json_file(__a							)
   snake_case_    			=    YosoForMaskedLM(__a							)

   snake_case_    			=    convert_checkpoint_helper(config.max_position_embeddings						,  __a							)

   print(model.load_state_dict(__a							)							)
   model.eval()
   model.save_pretrained(__a							)

   print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE		:List[str]     						=   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   SCREAMING_SNAKE_CASE		:str     						=   parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











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)

