





"""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 					lowercase__    (					lowercase_			)					->		YolosConfig:




      """simple docstring"""

      _UpperCamelCase      :	str						=       YolosConfig()

      # size of the architecture
      if "yolos_ti" in yolos_name:
            _UpperCamelCase      :	Union[str, Any]						=       192
            _UpperCamelCase      :	Dict						=       768
            _UpperCamelCase      :	str						=       12
            _UpperCamelCase      :	List[Any]						=       3
            _UpperCamelCase      :	List[Any]						=       [800, 1_333]
            _UpperCamelCase      :	Union[str, Any]						=       False
      elif yolos_name == "yolos_s_dWr":
            _UpperCamelCase      :	Optional[int]						=       330
            _UpperCamelCase      :	int						=       14
            _UpperCamelCase      :	int						=       6
            _UpperCamelCase      :	Dict						=       1_320
      elif "yolos_s" in yolos_name:
            _UpperCamelCase      :	Tuple						=       384
            _UpperCamelCase      :	List[Any]						=       1_536
            _UpperCamelCase      :	Dict						=       12
            _UpperCamelCase      :	str						=       6
      elif "yolos_b" in yolos_name:
            _UpperCamelCase      :	int						=       [800, 1_344]

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

      return config




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




def 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

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

      return name




def 					lowercase__    (					lowercase_		,lowercase_			)					->		dict:




      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            _UpperCamelCase      :	Any						=       orig_state_dict.pop(lowercase_			)

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

      return orig_state_dict




def 					lowercase__    (					)					->		torch.Tensor:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       "http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase      :	Optional[int]						=       Image.open(requests.get(lowercase_		,stream=lowercase_			).raw			)
      return im




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




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       get_yolos_config(lowercase_			)

      # load original state_dict
      _UpperCamelCase      :	Tuple						=       torch.load(lowercase_		,map_location="cpu"			)["model"]

      # load 🤗 model
      _UpperCamelCase      :	Dict						=       YolosForObjectDetection(lowercase_			)
      model.eval()
      _UpperCamelCase      :	List[str]						=       convert_state_dict(lowercase_		,lowercase_			)
      model.load_state_dict(lowercase_			)

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

      _UpperCamelCase      :	int						=       None, None
      if yolos_name == "yolos_ti":
            _UpperCamelCase      :	List[Any]						=       torch.tensor(
                [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
            _UpperCamelCase      :	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":
            _UpperCamelCase      :	Union[str, Any]						=       torch.tensor(
                [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
            _UpperCamelCase      :	Optional[Any]						=       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":
            _UpperCamelCase      :	Any						=       torch.tensor(
                [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
            _UpperCamelCase      :	int						=       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":
            _UpperCamelCase      :	int						=       torch.tensor(
                [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
            _UpperCamelCase      :	Optional[Any]						=       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":
            _UpperCamelCase      :	Dict						=       torch.tensor(
                [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
            _UpperCamelCase      :	Optional[Any]						=       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]		,lowercase_		,atol=1e-4			)
      assert torch.allclose(pred_boxes[0, :3, :3]		,lowercase_		,atol=1e-4			)

      Path(lowercase_			).mkdir(exist_ok=lowercase_			)
      print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
      model.save_pretrained(lowercase_			)
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
      image_processor.save_pretrained(lowercase_			)

      if push_to_hub:
            _UpperCamelCase      :	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..."			)
            _UpperCamelCase      :	List[str]						=       model_mapping[yolos_name]
            image_processor.push_to_hub(lowercase_		,organization="hustvl"			)
            model.push_to_hub(lowercase_		,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"""


lowerCamelCase__				  =   "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase__				  =   [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase__				  =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
