



"""simple docstring"""
import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
 import bsa
 from bsa import BeautifulSoup


_UpperCAmelCase     =			logging.get_logger(__name__)
class 					a	(  _UpperCamelCase       ):



  def __init__(						self :					Any       ,		**lowerCAmelCase :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     requires_backends(self       ,		["""bs4"""]		)
     super().__init__(**_UpperCAmelCase		)



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=[]
     SCREAMING_SNAKE_CASE_: Dict     			=[]
     SCREAMING_SNAKE_CASE_: int     			=element if element.name else element.parent
     for parent in child.parents:  # type: bs4.element.Tag
        SCREAMING_SNAKE_CASE_: Dict     			=parent.find_all(child.name       ,		recursive=_UpperCAmelCase		)
        xpath_tags.append(child.name		)
        xpath_subscripts.append(
            0 if 1 == len(_UpperCAmelCase		) else next(i for i, s in enumerate(_UpperCAmelCase       ,		1		) if s is child		)		)
        SCREAMING_SNAKE_CASE_: Any     			=parent
     xpath_tags.reverse()
     xpath_subscripts.reverse()
     return xpath_tags, xpath_subscripts



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=BeautifulSoup(_UpperCAmelCase       ,		"""html.parser"""		)

     SCREAMING_SNAKE_CASE_: Any     			=[]
     SCREAMING_SNAKE_CASE_: Dict     			=[]
     SCREAMING_SNAKE_CASE_: Optional[int]     			=[]

     for element in html_code.descendants:
        if type(_UpperCAmelCase		) == bsa.element.NavigableString:
           if type(element.parent		) != bsa.element.Tag:
              continue

           SCREAMING_SNAKE_CASE_: Dict     			=html.unescape(_UpperCAmelCase		).strip()
           if not text_in_this_tag:
              continue

           all_doc_strings.append(_UpperCAmelCase		)

           SCREAMING_SNAKE_CASE_: List[Any]     			=self.xpath_soup(_UpperCAmelCase		)
           stringaxtag_seq.append(_UpperCAmelCase		)
           stringaxsubs_seq.append(_UpperCAmelCase		)

     if len(_UpperCAmelCase		) != len(_UpperCAmelCase		):
        raise ValueError("""Number of doc strings and xtags does not correspond"""		)
     if len(_UpperCAmelCase		) != len(_UpperCAmelCase		):
        raise ValueError("""Number of doc strings and xsubs does not correspond"""		)

     return all_doc_strings, stringaxtag_seq, stringaxsubs_seq



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Tuple		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=''''''
     for tagname, subs in zip(_UpperCAmelCase       ,		_UpperCAmelCase		):
        xpath += f'''/{tagname}'''
        if subs != 0:
           xpath += f'''[{subs}]'''
     return xpath





  def __call__(						self :					str       ,		lowerCAmelCase :					Dict		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=False

     # Check that strings has a valid type
     if isinstance(_UpperCAmelCase       ,		_UpperCAmelCase		):
        SCREAMING_SNAKE_CASE_: str     			=True
     elif isinstance(_UpperCAmelCase       ,		(list, tuple)		):
        if len(_UpperCAmelCase		) == 0 or isinstance(html_strings[0]       ,		_UpperCAmelCase		):
           SCREAMING_SNAKE_CASE_: Optional[Any]     			=True

     if not valid_strings:
        raise ValueError(
            """HTML strings must of type `str`, `List[str]` (batch of examples), """
            f'''but is of type {type(_UpperCAmelCase		)}.'''		)

     SCREAMING_SNAKE_CASE_: List[str]     			=bool(isinstance(_UpperCAmelCase       ,		(list, tuple)		) and (isinstance(html_strings[0]       ,		_UpperCAmelCase		))		)

     if not is_batched:
        SCREAMING_SNAKE_CASE_: List[Any]     			=[html_strings]

     # Get nodes + xpaths
     SCREAMING_SNAKE_CASE_: Tuple     			=[]
     SCREAMING_SNAKE_CASE_: int     			=[]
     for html_string in html_strings:
        SCREAMING_SNAKE_CASE_: Any     			=self.get_three_from_single(_UpperCAmelCase		)
        nodes.append(_UpperCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=[]
        for node, tag_list, sub_list in zip(_UpperCAmelCase       ,		_UpperCAmelCase       ,		_UpperCAmelCase		):
           SCREAMING_SNAKE_CASE_: Optional[int]     			=self.construct_xpath(_UpperCAmelCase       ,		_UpperCAmelCase		)
           xpath_strings.append(_UpperCAmelCase		)
        xpaths.append(_UpperCAmelCase		)

     # return as Dict
     SCREAMING_SNAKE_CASE_: int     			={'''nodes''': nodes, '''xpaths''': xpaths}
     SCREAMING_SNAKE_CASE_: Any     			=BatchFeature(data=_UpperCAmelCase       ,		tensor_type=_UpperCAmelCase		)

     return encoded_inputs








"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def      __magic_name__				(							lowercase ):
   if "cls_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""cls_token"""							,			"""vit.embeddings.cls_token""" )
   if "mask_token" in name:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=name.replace("""mask_token"""							,			"""decoder.mask_token""" )
   if "decoder_pos_embed" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""decoder_pos_embed"""							,			"""decoder.decoder_pos_embed""" )
   if "pos_embed" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""pos_embed"""							,			"""vit.embeddings.position_embeddings""" )
   if "patch_embed.proj" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""patch_embed.proj"""							,			"""vit.embeddings.patch_embeddings.projection""" )
   if "patch_embed.norm" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""patch_embed.norm"""							,			"""vit.embeddings.norm""" )
   if "decoder_blocks" in name:
      SCREAMING_SNAKE_CASE_: List[Any]     			=name.replace("""decoder_blocks"""							,			"""decoder.decoder_layers""" )
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_: str     			=name.replace("""blocks"""							,			"""vit.encoder.layer""" )
   if "attn.proj" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""attn.proj"""							,			"""attention.output.dense""" )
   if "attn" in name:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=name.replace("""attn"""							,			"""attention.self""" )
   if "norm1" in name:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=name.replace("""norm1"""							,			"""layernorm_before""" )
   if "norm2" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""norm2"""							,			"""layernorm_after""" )
   if "mlp.fc1" in name:
      SCREAMING_SNAKE_CASE_: int     			=name.replace("""mlp.fc1"""							,			"""intermediate.dense""" )
   if "mlp.fc2" in name:
      SCREAMING_SNAKE_CASE_: Dict     			=name.replace("""mlp.fc2"""							,			"""output.dense""" )
   if "decoder_embed" in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""decoder_embed"""							,			"""decoder.decoder_embed""" )
   if "decoder_norm" in name:
      SCREAMING_SNAKE_CASE_: Tuple     			=name.replace("""decoder_norm"""							,			"""decoder.decoder_norm""" )
   if "decoder_pred" in name:
      SCREAMING_SNAKE_CASE_: Any     			=name.replace("""decoder_pred"""							,			"""decoder.decoder_pred""" )
   if "norm.weight" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.weight"""							,			"""vit.layernorm.weight""" )
   if "norm.bias" in name and "decoder" not in name:
      SCREAMING_SNAKE_CASE_: List[str]     			=name.replace("""norm.bias"""							,			"""vit.layernorm.bias""" )

   return name





def      __magic_name__				(							lowercase							,			lowercase ):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Optional[int]     			=orig_state_dict.pop(lowercase )

      if "qkv" in key:
         SCREAMING_SNAKE_CASE_: Dict     			=key.split(""".""" )
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=int(key_split[1] )
         if "decoder_blocks" in key:
            SCREAMING_SNAKE_CASE_: int     			=config.decoder_hidden_size
            SCREAMING_SNAKE_CASE_: Optional[int]     			="""decoder.decoder_layers."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Dict     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: str     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Tuple     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: List[Any]     			=val[-dim:]
         else:
            SCREAMING_SNAKE_CASE_: Any     			=config.hidden_size
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""vit.encoder.layer."""
            if "weight" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim, :]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[dim : dim * 2, :]
               SCREAMING_SNAKE_CASE_: Dict     			=val[-dim:, :]
            elif "bias" in key:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[:dim]
               SCREAMING_SNAKE_CASE_: Any     			=val[dim : dim * 2]
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=val[-dim:]

      else:
         SCREAMING_SNAKE_CASE_: Tuple     			=val

   return orig_state_dict





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=ViTMAEConfig()
   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: List[Any]     			=1024
      SCREAMING_SNAKE_CASE_: Dict     			=4096
      SCREAMING_SNAKE_CASE_: Tuple     			=24
      SCREAMING_SNAKE_CASE_: int     			=16
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=14
      SCREAMING_SNAKE_CASE_: Any     			=1280
      SCREAMING_SNAKE_CASE_: Dict     			=5120
      SCREAMING_SNAKE_CASE_: Optional[int]     			=32
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=16

   SCREAMING_SNAKE_CASE_: Tuple     			=ViTMAEForPreTraining(lowercase )

   SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.hub.load_state_dict_from_url(lowercase							,			map_location="""cpu""" )["""model"""]

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=ViTMAEImageProcessor(size=config.image_size )

   SCREAMING_SNAKE_CASE_: str     			=convert_state_dict(lowercase							,			lowercase )

   model.load_state_dict(lowercase )
   model.eval()

   SCREAMING_SNAKE_CASE_: Tuple     			="""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   SCREAMING_SNAKE_CASE_: int     			=ViTMAEImageProcessor(size=config.image_size )
   SCREAMING_SNAKE_CASE_: int     			=image_processor(images=lowercase							,			return_tensors="""pt""" )

   # forward pass
   torch.manual_seed(2 )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(**lowercase )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=outputs.logits

   if "large" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Dict     			=torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
   elif "huge" in checkpoint_url:
      SCREAMING_SNAKE_CASE_: Tuple     			=torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
   else:
      SCREAMING_SNAKE_CASE_: Any     			=torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )

   # verify logits
   assert torch.allclose(logits[0, :3, :3]							,			lowercase							,			atol=1e-4 )

   print(f'''Saving model 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 __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--checkpoint_url""",
       default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
       type=str,
       help="""URL of the checkpoint you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



