




"""simple docstring"""


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					["image_processor", "tokenizer"]
 lowercase__ 					=					"AutoImageProcessor"
 lowercase__ 					=					"AutoTokenizer"


 def __init__(				self:		List[str],      a_:		List[str]=None,      a_:		Tuple=None,      **a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		str								=  None
   if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead.""",      a_,      )
     _snake_case							:		str								=  kwargs.pop("""feature_extractor"""		)

   _snake_case							:		Union[str, Any]								=  image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`."""		)
   if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`."""		)

   super().__init__(a_,      a_		)
   _snake_case							:		Dict								=  self.image_processor
   _snake_case							:		Any								=  False


 def __call__(				self:		Any,      *a_:		Any,      **a_:		Tuple		):




   '''simple docstring'''



   if self._in_target_context_manager:
     return self.current_processor(*a_,      **a_		)

   _snake_case							:		Dict								=  kwargs.pop("""images""",      a_		)
   _snake_case							:		Optional[Any]								=  kwargs.pop("""text""",      a_		)
   if len(a_		) > 0:
     _snake_case							:		Optional[int]								=  args[0]
     _snake_case							:		Tuple								=  args[1:]

   if images is None and text is None:
     raise ValueError("""You need to specify either an `images` or `text` input to process."""		)

   if images is not None:
     _snake_case							:		Tuple								=  self.image_processor(a_,      *a_,      **a_		)
   if text is not None:
     _snake_case							:		Tuple								=  self.tokenizer(a_,      **a_		)

   if text is None:
     return inputs
   elif images is None:
     return encodings
   else:
     _snake_case							:		List[str]								=  encodings["""input_ids"""]
     return inputs


 def  UpperCamelCase_   (				self:		Optional[int],      *a_:		Tuple,      **a_:		List[str]		):




   '''simple docstring'''



   return self.tokenizer.batch_decode(*a_,      **a_		)


 def  UpperCamelCase_   (				self:		int,      *a_:		List[str],      **a_:		int		):




   '''simple docstring'''



   return self.tokenizer.decode(*a_,      **a_		)


 @contextmanager
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   warnings.warn(
       """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
       """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
       """your images inputs, or in a separate call."""		)
   _snake_case							:		Any								=  True
   _snake_case							:		Optional[int]								=  self.tokenizer
   yield
   _snake_case							:		int								=  self.image_processor
   _snake_case							:		Optional[int]								=  False


 def  UpperCamelCase_   (				self:		Dict,      a_:		Optional[Any],      a_:		str=False,      a_:		Optional[Any]=None		):




   '''simple docstring'''



   if added_vocab is None:
     _snake_case							:		Dict								=  self.tokenizer.get_added_vocab()

   _snake_case							:		str								=  {}

   while tokens:
     _snake_case							:		Union[str, Any]								=  re.search(r"""<s_(.*?)>""",      a_,      re.IGNORECASE		)
     if start_token is None:
       break
     _snake_case							:		List[Any]								=  start_token.group(1		)
     _snake_case							:		str								=  re.search(rf"</s_{key}>",      a_,      re.IGNORECASE		)
     _snake_case							:		Dict								=  start_token.group()
     if end_token is None:
       _snake_case							:		List[Any]								=  tokens.replace(a_,      """"""		)
     else:
       _snake_case							:		List[str]								=  end_token.group()
       _snake_case							:		str								=  re.escape(a_		)
       _snake_case							:		str								=  re.escape(a_		)
       _snake_case							:		Union[str, Any]								=  re.search(f"{start_token_escaped}(.*?){end_token_escaped}",      a_,      re.IGNORECASE		)
       if content is not None:
         _snake_case							:		int								=  content.group(1		).strip()
         if r"<s_" in content and r"</s_" in content:  # non-leaf node
           _snake_case							:		List[Any]								=  self.tokenajson(a_,      is_inner_value=a_,      added_vocab=a_		)
           if value:
             if len(a_		) == 1:
               _snake_case							:		List[str]								=  value[0]
             _snake_case							:		List[str]								=  value
         else:  # leaf nodes
           _snake_case							:		Tuple								=  []
           for leaf in content.split(r"""<sep/>"""		):
             _snake_case							:		Tuple								=  leaf.strip()
             if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
               _snake_case							:		int								=  leaf[1:-2]  # for categorical special tokens
             output[key].append(a_		)
           if len(output[key]		) == 1:
             _snake_case							:		int								=  output[key][0]

       _snake_case							:		Any								=  tokens[tokens.find(a_		) + len(a_		) :].strip()
       if tokens[:6] == r"<sep/>":  # non-leaf nodes
         return [output] + self.tokenajson(tokens[6:],      is_inner_value=a_,      added_vocab=a_		)

   if len(a_		):
     return [output] if is_inner_value else output
   else:
     return [] if is_inner_value else {"text_sequence": tokens}


 @property
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   warnings.warn(
       """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""",      a_,      )
   return self.image_processor_class


 @property
 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   warnings.warn(
       """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",      a_,      )
   return self.image_processor








"""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 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    _snake_case							:		Tuple								=  1_92
    _snake_case							:		Any								=  7_68
    _snake_case							:		Any								=  12
    _snake_case							:		List[Any]								=  3
    _snake_case							:		int								=  [8_00, 13_33]
    _snake_case							:		Tuple								=  False
  elif yolos_name == "yolos_s_dWr":
    _snake_case							:		Tuple								=  3_30
    _snake_case							:		List[str]								=  14
    _snake_case							:		List[str]								=  6
    _snake_case							:		Union[str, Any]								=  13_20
  elif "yolos_s" in yolos_name:
    _snake_case							:		Union[str, Any]								=  3_84
    _snake_case							:		List[str]								=  15_36
    _snake_case							:		Any								=  12
    _snake_case							:		Optional[int]								=  6
  elif "yolos_b" in yolos_name:
    _snake_case							:		Dict								=  [8_00, 13_44]

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

  return config


def 					UpperCAmelCase__       (snake_case__							:					dict		,   snake_case__							:					YolosConfig		,   snake_case__							:					bool = False    ):
  """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)
    _snake_case							:		int								=  state_dict.pop(F"blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		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
    _snake_case							:		Any								=  in_proj_weight[: config.hidden_size, :]
    _snake_case							:		Optional[Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Optional[int]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		int								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Tuple								=  in_proj_weight[-config.hidden_size :, :]
    _snake_case							:		List[Any]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

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

  return name


def 					UpperCAmelCase__       (snake_case__							:					dict		,   snake_case__							:					YolosForObjectDetection    ):
  """simple docstring"""

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

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

  return orig_state_dict


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		str								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _snake_case							:		Union[str, Any]								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  return im


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					str		,   snake_case__							:					bool = False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  get_yolos_config(snake_case__    )

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

  # load 🤗 model
  _snake_case							:		Optional[Any]								=  YolosForObjectDetection(snake_case__    )
  model.eval()
  _snake_case							:		Optional[Any]								=  convert_state_dict(snake_case__		,   snake_case__    )
  model.load_state_dict(snake_case__    )

  # Check outputs on an image, prepared by YolosImageProcessor
  _snake_case							:		List[str]								=  8_00 if yolos_name != """yolos_ti""" else 5_12
  _snake_case							:		Optional[int]								=  YolosImageProcessor(format="""coco_detection"""		,   size=snake_case__    )
  _snake_case							:		Optional[Any]								=  image_processor(images=prepare_img()		,   return_tensors="""pt"""    )
  _snake_case							:		Optional[Any]								=  model(**snake_case__    )
  _snake_case		,					_snake_case							:		Optional[int]								=  outputs.logits, outputs.pred_boxes

  _snake_case		,					_snake_case							:		Dict								=  None, None
  if yolos_name == "yolos_ti":
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
    _snake_case							:		Tuple								=  torch.tensor(
        [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
  elif yolos_name == "yolos_s_200_pre":
    _snake_case							:		List[str]								=  torch.tensor(
        [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
    _snake_case							:		List[str]								=  torch.tensor(
        [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
  elif yolos_name == "yolos_s_300_pre":
    _snake_case							:		Dict								=  torch.tensor(
        [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
    _snake_case							:		Union[str, Any]								=  torch.tensor(
        [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
  elif yolos_name == "yolos_s_dWr":
    _snake_case							:		Tuple								=  torch.tensor(
        [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
    _snake_case							:		Optional[Any]								=  torch.tensor(
        [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
  elif yolos_name == "yolos_base":
    _snake_case							:		int								=  torch.tensor(
        [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
    _snake_case							:		Optional[int]								=  torch.tensor(
        [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
  else:
    raise ValueError(F"Unknown yolos_name: {yolos_name}"    )

  assert torch.allclose(logits[0, :3, :3]		,   snake_case__		,   atol=1e-4    )
  assert torch.allclose(pred_boxes[0, :3, :3]		,   snake_case__		,   atol=1e-4    )

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

  if push_to_hub:
    _snake_case							:		Dict								=  {
        """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..."""    )
    _snake_case							:		str								=  model_mapping[yolos_name]
    image_processor.push_to_hub(snake_case__		,   organization="""hustvl"""    )
    model.push_to_hub(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)


