





'''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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_		:  List[Any]		 =						logging.get_logger(__name__)



def        UpperCamelCase__		(						__magic_name__  :   str		,       __magic_name__  :   Optional[Any]=False		,       __magic_name__  :   Dict=False      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					int    =  """backbone.""" if is_semantic else """"""

 snake_case__     :					List[Any]    =  []
 for i in range(config.num_hidden_layers      ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight")      )
  rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias")      )
  rename_keys.append(
      (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight")      )
  rename_keys.append(
      (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias")      )
  rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight")      )
  rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias")      )
  rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight")      )
  rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias")      )
  rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight")      )
  rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias")      )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f"{prefix}cls_token", """beit.embeddings.cls_token"""),
         (f"{prefix}patch_embed.proj.weight", """beit.embeddings.patch_embeddings.projection.weight"""),
         (f"{prefix}patch_embed.proj.bias", """beit.embeddings.patch_embeddings.projection.bias"""),
         (f"{prefix}pos_embed", """beit.embeddings.position_embeddings"""),
     ]      )

 if has_lm_head:
  # mask token + layernorm
  rename_keys.extend(
      [
          ("""mask_token""", """beit.embeddings.mask_token"""),
          ("""norm.weight""", """layernorm.weight"""),
          ("""norm.bias""", """layernorm.bias"""),
      ]      )
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
          ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]      )

 return rename_keys



def        UpperCamelCase__		(						__magic_name__  :   int		,       __magic_name__  :   Dict		,       __magic_name__  :   Tuple=False		,       __magic_name__  :   Any=False      )				->			Dict:
 '''simple docstring'''






 for i in range(config.num_hidden_layers      ):
  snake_case__     :					Tuple    =  """backbone.""" if is_semantic else """"""
  # queries, keys and values
  snake_case__     :					int    =  state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"      )
  snake_case__     :					List[str]    =  state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"      )
  snake_case__     :					Any    =  state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"      )

  snake_case__     :					Optional[int]    =  in_proj_weight[
      : config.hidden_size, :
  ]
  snake_case__     :					Optional[int]    =  q_bias
  snake_case__     :					Any    =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  snake_case__     :					int    =  in_proj_weight[
      -config.hidden_size :, :
  ]
  snake_case__     :					List[str]    =  v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  snake_case__     :					Tuple    =  state_dict.pop(f"{prefix}blocks.{i}.gamma_1"      )
  snake_case__     :					str    =  state_dict.pop(f"{prefix}blocks.{i}.gamma_2"      )

  snake_case__     :					int    =  gamma_a
  snake_case__     :					Dict    =  gamma_a



def        UpperCamelCase__		(						__magic_name__  :   str		,       __magic_name__  :   List[Any]		,       __magic_name__  :   Any      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  dct.pop(__magic_name__      )
 snake_case__     :					str    =  val



def        UpperCamelCase__		(						)				->			Dict:
 '''simple docstring'''






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



@torch.no_grad()
def        UpperCamelCase__		(						__magic_name__  :   Union[str, Any]		,       __magic_name__  :   Optional[Any]		,       __magic_name__  :   Any=False      )				->			Any:
 '''simple docstring'''






 snake_case__     :					Any    =  False if """rvlcdip""" in checkpoint_url else True
 snake_case__     :					Optional[Any]    =  BeitConfig(use_absolute_position_embeddings=__magic_name__		,       use_mask_token=__magic_name__      )

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  snake_case__     :					Optional[Any]    =  10_24
  snake_case__     :					Optional[Any]    =  40_96
  snake_case__     :					int    =  24
  snake_case__     :					Tuple    =  16

 # labels
 if "rvlcdip" in checkpoint_url:
  snake_case__     :					Union[str, Any]    =  16
  snake_case__     :					Dict    =  """huggingface/label-files"""
  snake_case__     :					str    =  """rvlcdip-id2label.json"""
  snake_case__     :					Tuple    =  json.load(open(hf_hub_download(__magic_name__		,       __magic_name__		,       repo_type="""dataset"""      )		,       """r"""      )      )
  snake_case__     :					List[str]    =  {int(__magic_name__      ): v for k, v in idalabel.items()}
  snake_case__     :					int    =  idalabel
  snake_case__     :					List[str]    =  {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 snake_case__     :					List[str]    =  torch.hub.load_state_dict_from_url(__magic_name__		,       map_location="""cpu"""      )["""model"""]

 snake_case__     :					Tuple    =  create_rename_keys(__magic_name__		,       has_lm_head=__magic_name__      )
 for src, dest in rename_keys:
  rename_key(__magic_name__		,       __magic_name__		,       __magic_name__      )
 read_in_q_k_v(__magic_name__		,       __magic_name__		,       has_lm_head=__magic_name__      )

 # load HuggingFace model
 snake_case__     :					str    =  BeitForMaskedImageModeling(__magic_name__      ) if has_lm_head else BeitForImageClassification(__magic_name__      )
 model.eval()
 model.load_state_dict(__magic_name__      )

 # Check outputs on an image
 snake_case__     :					Tuple    =  BeitImageProcessor(
     size=config.image_size		,       resample=PILImageResampling.BILINEAR		,       do_center_crop=__magic_name__      )
 snake_case__     :					str    =  prepare_img()

 snake_case__     :					List[Any]    =  image_processor(images=__magic_name__		,       return_tensors="""pt"""      )
 snake_case__     :					Dict    =  encoding["""pixel_values"""]

 snake_case__     :					Union[str, Any]    =  model(__magic_name__      )
 snake_case__     :					Optional[int]    =  outputs.logits

 # verify logits
 snake_case__     :					str    =  [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
 assert logits.shape == torch.Size(__magic_name__      ), "Shape of logits not as expected"

 Path(__magic_name__      ).mkdir(exist_ok=__magic_name__      )
 print(f"Saving model to {pytorch_dump_folder_path}"      )
 model.save_pretrained(__magic_name__      )
 print(f"Saving image processor to {pytorch_dump_folder_path}"      )
 image_processor.save_pretrained(__magic_name__      )

 if push_to_hub:
  if has_lm_head:
   snake_case__     :					Optional[int]    =  """dit-base""" if """base""" in checkpoint_url else """dit-large"""
  else:
   snake_case__     :					Dict    =  """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
  image_processor.push_to_hub(
      repo_path_or_name=Path(__magic_name__		,       __magic_name__      )		,       organization="""nielsr"""		,       commit_message="""Add image processor"""		,       use_temp_dir=__magic_name__		,       )
  model.push_to_hub(
      repo_path_or_name=Path(__magic_name__		,       __magic_name__      )		,       organization="""nielsr"""		,       commit_message="""Add model"""		,       use_temp_dir=__magic_name__		,       )


if __name__ == "__main__":
    A_		:  List[Any]		 =						argparse.ArgumentParser()

    parser.add_argument(
        "--checkpoint_url",
        default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
        type=str,
        help="URL to the original PyTorch checkpoint (.pth file).",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
    )
    A_		:  Optional[Any]		 =						parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase__ :   Any						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[Any]						=     "van"






				def __init__(			self      ,snake_case__=224      ,snake_case__=3      ,snake_case__=[7, 3, 3, 3]      ,snake_case__=[4, 2, 2, 2]      ,snake_case__=[64, 128, 320, 512]      ,snake_case__=[3, 3, 12, 3]      ,snake_case__=[8, 8, 4, 4]      ,snake_case__="gelu"      ,snake_case__=0.02      ,snake_case__=1E-6      ,snake_case__=1E-2      ,snake_case__=0.0      ,snake_case__=0.0      ,**snake_case__      ,):
							super().__init__(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      image_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      num_channels
							SCREAMING_SNAKE_CASE_   : Dict					  =      patch_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      strides
							SCREAMING_SNAKE_CASE_   : str					  =      hidden_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      depths
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      mlp_ratios
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      layer_norm_eps
							SCREAMING_SNAKE_CASE_   : int					  =      layer_scale_init_value
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      drop_path_rate
							SCREAMING_SNAKE_CASE_   : List[str]					  =      dropout_rate



