





"""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()
__A											=						logging.get_logger(__name__)
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->YolosConfig:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				YolosConfig()

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

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

  return config
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = False     )   ->List[str]:





  """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[Any]											=				state_dict.pop(F"blocks.{i}.attn.qkv.weight"     )
    lowerCAmelCase__     :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
    lowerCAmelCase__     :int											=				in_proj_weight[: config.hidden_size, :]
    lowerCAmelCase__     :int											=				in_proj_bias[: config.hidden_size]
    lowerCAmelCase__     :Any											=				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase__     :Optional[Any]											=				in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase__     :Dict											=				in_proj_weight[-config.hidden_size :, :]
    lowerCAmelCase__     :List[str]											=				in_proj_bias[-config.hidden_size :]
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


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

  return name
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->dict:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    lowerCAmelCase__     :Any											=				orig_state_dict.pop(_SCREAMING_SNAKE_CASE     )

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

  return orig_state_dict
def 					__A  ()   ->torch.Tensor:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase__     :Union[str, Any]											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     )
  return im
@torch.no_grad()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = False     )   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :str											=				get_yolos_config(_SCREAMING_SNAKE_CASE     )

  # load original state_dict
  lowerCAmelCase__     :Optional[Any]											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['model']

  # load 🤗 model
  lowerCAmelCase__     :Optional[Any]											=				YolosForObjectDetection(_SCREAMING_SNAKE_CASE     )
  model.eval()
  lowerCAmelCase__     :str											=				convert_state_dict(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

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

  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				None, None
  if yolos_name == "yolos_ti":
    lowerCAmelCase__     :List[str]											=				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__     :Union[str, 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__     :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__     :Tuple											=				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__     :List[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__     :Dict											=				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__     :Dict											=				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__     :Optional[int]											=				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__     :str											=				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__     :Any											=				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]	,  _SCREAMING_SNAKE_CASE	,  atol=1e-4     )
  assert torch.allclose(pred_boxes[0, :3, :3]	,  _SCREAMING_SNAKE_CASE	,  atol=1e-4     )

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

  if push_to_hub:
    lowerCAmelCase__     :Union[str, 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__     :Union[str, Any]											=				model_mapping[yolos_name]
    image_processor.push_to_hub(_SCREAMING_SNAKE_CASE	,  organization='hustvl'     )
    model.push_to_hub(_SCREAMING_SNAKE_CASE	,  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)







"""simple docstring"""





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A											=						{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  if got_ver is None or want_ver is None:
    raise ValueError(
        F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
        F" reinstalling {pkg}."     )
  if not ops[op](version.parse(_SCREAMING_SNAKE_CASE     )	,  version.parse(_SCREAMING_SNAKE_CASE     )     ):
    raise ImportError(
        F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = None     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				F"\n{hint}" if hint is not None else ''

  # non-versioned check
  if re.match(r'^[\w_\-\d]+$'	,  _SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				requirement, None, None
  else:
    lowerCAmelCase__     :List[str]											=				re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'	,  _SCREAMING_SNAKE_CASE     )
    if not match:
      raise ValueError(
          'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
          F" got {requirement}"     )
    lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				match[0]
    lowerCAmelCase__     :List[Any]											=				want_full.split(','     )  # there could be multiple requirements
    lowerCAmelCase__     :Any											=				{}
    for w in want_range:
      lowerCAmelCase__     :Tuple											=				re.findall(r'^([\s!=<>]{1,2})(.+)'	,  _SCREAMING_SNAKE_CASE     )
      if not match:
        raise ValueError(
            'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
            F" but got {requirement}"     )
      lowerCAmelCase__				,							lowerCAmelCase__     :int											=				match[0]
      lowerCAmelCase__     :str											=				want_ver
      if op not in ops:
        raise ValueError(F"{requirement}: need one of {list(ops.keys()     )}, but got {op}"     )

    # special case
  if pkg == "python":
    lowerCAmelCase__     :Any											=				'.'.join([str(_SCREAMING_SNAKE_CASE     ) for x in sys.version_info[:3]]     )
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    return

  # check if any version is installed
  try:
    lowerCAmelCase__     :List[Any]											=				importlib.metadata.version(_SCREAMING_SNAKE_CASE     )
  except importlib.metadata.PackageNotFoundError:
    raise importlib.metadata.PackageNotFoundError(
        F"The '{requirement}' distribution was not found and is required by this application. {hint}"     )

  # check that the right version is installed if version number or a range was provided
  if want_ver is not None:
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )




def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
  return require_version(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
