






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_		:		Dict		     =			logging.get_logger(__name__)

def   snake_case (UpperCAmelCase__  )	->    YolosConfig:
  UpperCamelCase_: Dict   				=    YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    UpperCamelCase_: str   				=    1_9_2
    UpperCamelCase_: int   				=    7_6_8
    UpperCamelCase_: Optional[int]   				=    1_2
    UpperCamelCase_: Tuple   				=    3
    UpperCamelCase_: Optional[int]   				=    [8_0_0, 1_3_3_3]
    UpperCamelCase_: Any   				=    False
  elif yolos_name == "yolos_s_dWr":
    UpperCamelCase_: Optional[Any]   				=    3_3_0
    UpperCamelCase_: List[Any]   				=    1_4
    UpperCamelCase_: Union[str, Any]   				=    6
    UpperCamelCase_: List[Any]   				=    1_3_2_0
  elif "yolos_s" in yolos_name:
    UpperCamelCase_: int   				=    3_8_4
    UpperCamelCase_: Dict   				=    1_5_3_6
    UpperCamelCase_: Any   				=    1_2
    UpperCamelCase_: Union[str, Any]   				=    6
  elif "yolos_b" in yolos_name:
    UpperCamelCase_: str   				=    [8_0_0, 1_3_4_4]

  UpperCamelCase_: List[str]   				=    9_1
  UpperCamelCase_: Any   				=    "huggingface/label-files"
  UpperCamelCase_: Optional[int]   				=    "coco-detection-id2label.json"
  UpperCamelCase_: Tuple   				=    json.load(open(hf_hub_download(_lowerCamelCase     ,   _lowerCamelCase     ,   repo_type='dataset'  )     ,   'r'  )  )
  UpperCamelCase_: Dict   				=    {int(_lowerCamelCase  ): v for k, v in idalabel.items()}
  UpperCamelCase_: Any   				=    idalabel
  UpperCamelCase_: List[str]   				=    {v: k for k, v in idalabel.items()}

  return config

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    int:

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

def   snake_case (UpperCAmelCase__  )	->    str:

  if "backbone" in name:
    UpperCamelCase_: List[Any]   				=    name.replace('backbone'     ,   'vit'  )
  if "cls_token" in name:
    UpperCamelCase_: Tuple   				=    name.replace('cls_token'     ,   'embeddings.cls_token'  )
  if "det_token" in name:
    UpperCamelCase_: List[str]   				=    name.replace('det_token'     ,   'embeddings.detection_tokens'  )
  if "mid_pos_embed" in name:
    UpperCamelCase_: Union[str, Any]   				=    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_: 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_: Tuple   				=    name.replace('attn.proj'     ,   'attention.output.dense'  )
  if "attn" in name:
    UpperCamelCase_: Dict   				=    name.replace('attn'     ,   'attention.self'  )
  if "norm1" in name:
    UpperCamelCase_: List[str]   				=    name.replace('norm1'     ,   'layernorm_before'  )
  if "norm2" in name:
    UpperCamelCase_: Any   				=    name.replace('norm2'     ,   'layernorm_after'  )
  if "mlp.fc1" in name:
    UpperCamelCase_: List[str]   				=    name.replace('mlp.fc1'     ,   'intermediate.dense'  )
  if "mlp.fc2" in name:
    UpperCamelCase_: Optional[Any]   				=    name.replace('mlp.fc2'     ,   'output.dense'  )
  if "class_embed" in name:
    UpperCamelCase_: int   				=    name.replace('class_embed'     ,   'class_labels_classifier'  )
  if "bbox_embed" in name:
    UpperCamelCase_: Optional[int]   				=    name.replace('bbox_embed'     ,   'bbox_predictor'  )
  if "vit.norm" in name:
    UpperCamelCase_: Optional[int]   				=    name.replace('vit.norm'     ,   'vit.layernorm'  )

  return name

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    dict:

  for key in orig_state_dict.copy().keys():
    UpperCamelCase_: List[str]   				=    orig_state_dict.pop(_lowerCamelCase  )

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

  return orig_state_dict

def   snake_case ()	->    torch.Tensor:
  UpperCamelCase_: str   				=    "http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCamelCase_: Optional[Any]   				=    Image.open(requests.get(_lowerCamelCase     ,   stream=_lowerCamelCase  ).raw  )
  return im

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    Any:
  UpperCamelCase_: Optional[int]   				=    get_yolos_config(_lowerCamelCase  )

  # load original state_dict
  UpperCamelCase_: Any   				=    torch.load(_lowerCamelCase     ,   map_location='cpu'  )["model"]

  # load 🤗 model
  UpperCamelCase_: List[Any]   				=    YolosForObjectDetection(_lowerCamelCase  )
  model.eval()
  UpperCamelCase_: Optional[int]   				=    convert_state_dict(_lowerCamelCase     ,   _lowerCamelCase  )
  model.load_state_dict(_lowerCamelCase  )

  # Check outputs on an image, prepared by YolosImageProcessor
  UpperCamelCase_: List[str]   				=    8_0_0 if yolos_name != "yolos_ti" else 5_1_2
  UpperCamelCase_: Optional[int]   				=    YolosImageProcessor(format='coco_detection'     ,   size=_lowerCamelCase  )
  UpperCamelCase_: Any   				=    image_processor(images=prepare_img()     ,   return_tensors='pt'  )
  UpperCamelCase_: Union[str, Any]   				=    model(**_lowerCamelCase  )
  UpperCamelCase_: List[Any]   				=    outputs.logits, outputs.pred_boxes

  UpperCamelCase_: Optional[int]   				=    None, None
  if yolos_name == "yolos_ti":
    UpperCamelCase_: Dict   				=    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_: Optional[int]   				=    torch.tensor(
        [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]  )
    UpperCamelCase_: Union[str, 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_: str   				=    torch.tensor(
        [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]  )
    UpperCamelCase_: Union[str, Any]   				=    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_: 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":
    UpperCamelCase_: Any   				=    torch.tensor(
        [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]  )
    UpperCamelCase_: int   				=    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]     ,   _lowerCamelCase     ,   atol=1E-4  )
  assert torch.allclose(pred_boxes[0, :3, :3]     ,   _lowerCamelCase     ,   atol=1E-4  )

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

  if push_to_hub:
    UpperCamelCase_: List[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...'  )
    UpperCamelCase_: Tuple   				=    model_mapping[yolos_name]
    image_processor.push_to_hub(_lowerCamelCase     ,   organization='hustvl'  )
    model.push_to_hub(_lowerCamelCase     ,   organization='hustvl'  )


if __name__ == "__main__":
   A_		:		int		     =			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_		:		List[str]		     =			parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_		:		List[str]		     =			'.'


if __name__ == "__main__":
   A_		:		Dict		     =			os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
   A_		:		Dict		     =			[]
   A_		:		Optional[Any]		     =			[]
   with open(doctest_file_path) as fp:
      for line in fp:
         A_		:		Tuple		     =			line.strip()
         A_		:		Any		     =			os.path.join(REPO_PATH, line)
         if not (os.path.isfile(path) or os.path.isdir(path)):
            non_existent_paths.append(line)
         all_paths.append(path)
   if len(non_existent_paths) > 0:
      A_		:		str		     =			'\n'.join(non_existent_paths)
      raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
   if all_paths != sorted(all_paths):
      raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')