





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()
_UpperCAmelCase             =							logging.get_logger(__name__)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[str]=False   ,   SCREAMING_SNAKE_CASE		:Tuple=False   )      ->   Tuple:
 __lowerCAmelCase					:	int      =       """backbone.""" if is_semantic else """"""

 __lowerCAmelCase					:	List[str]      =       []
 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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[str]=False   ,   SCREAMING_SNAKE_CASE		:Any=False   )      ->   int:
 for i in range(config.num_hidden_layers   ):
  __lowerCAmelCase					:	Union[str, Any]      =       """backbone.""" if is_semantic else """"""
  # queries, keys and values
  __lowerCAmelCase					:	List[Any]      =       state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''   )
  __lowerCAmelCase					:	Any      =       state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''   )
  __lowerCAmelCase					:	Optional[int]      =       state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''   )

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

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __lowerCAmelCase					:	int      =       state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''   )
  __lowerCAmelCase					:	List[str]      =       state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''   )

  __lowerCAmelCase					:	List[Any]      =       gamma_a
  __lowerCAmelCase					:	Dict      =       gamma_a






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   Dict:
 __lowerCAmelCase					:	Dict      =       dct.pop(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Union[str, Any]      =       val






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[int]:
 __lowerCAmelCase					:	int      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
 __lowerCAmelCase					:	List[Any]      =       Image.open(requests.get(SCREAMING_SNAKE_CASE   ,   stream=SCREAMING_SNAKE_CASE   ).raw   )
 return im






@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:Any=False   )      ->   List[Any]:
 __lowerCAmelCase					:	Dict      =       False if """rvlcdip""" in checkpoint_url else True
 __lowerCAmelCase					:	List[Any]      =       BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE   ,   use_mask_token=SCREAMING_SNAKE_CASE   )

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  __lowerCAmelCase					:	Optional[int]      =       1_024
  __lowerCAmelCase					:	str      =       4_096
  __lowerCAmelCase					:	Optional[int]      =       24
  __lowerCAmelCase					:	List[Any]      =       16

 # labels
 if "rvlcdip" in checkpoint_url:
  __lowerCAmelCase					:	List[Any]      =       16
  __lowerCAmelCase					:	str      =       """huggingface/label-files"""
  __lowerCAmelCase					:	Optional[Any]      =       """rvlcdip-id2label.json"""
  __lowerCAmelCase					:	List[Any]      =       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					:	Optional[Any]      =       idalabel
  __lowerCAmelCase					:	Union[str, Any]      =       {v: k for k, v in idalabel.items()}

 # load state_dict of original model, remove and rename some keys
 __lowerCAmelCase					:	Any      =       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE   ,   map_location="""cpu"""   )["""model"""]

 __lowerCAmelCase					:	str      =       create_rename_keys(SCREAMING_SNAKE_CASE   ,   has_lm_head=SCREAMING_SNAKE_CASE   )
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 read_in_q_k_v(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   has_lm_head=SCREAMING_SNAKE_CASE   )

 # load HuggingFace model
 __lowerCAmelCase					:	int      =       BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE   ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE   )
 model.eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE   )

 # Check outputs on an image
 __lowerCAmelCase					:	List[str]      =       BeitImageProcessor(
     size=config.image_size   ,   resample=PILImageResampling.BILINEAR   ,   do_center_crop=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[Any]      =       prepare_img()

 __lowerCAmelCase					:	Optional[Any]      =       image_processor(images=SCREAMING_SNAKE_CASE   ,   return_tensors="""pt"""   )
 __lowerCAmelCase					:	Optional[Any]      =       encoding["""pixel_values"""]

 __lowerCAmelCase					:	Optional[int]      =       model(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       outputs.logits

 # verify logits
 __lowerCAmelCase					:	int      =       [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8_192]
 assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE   ), "Shape of logits not as expected"

 Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
 print(F'''Saving model 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:
  if has_lm_head:
   __lowerCAmelCase					:	str      =       """dit-base""" if """base""" in checkpoint_url else """dit-large"""
  else:
   __lowerCAmelCase					:	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(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   organization="""nielsr"""   ,   commit_message="""Add image processor"""   ,   use_temp_dir=SCREAMING_SNAKE_CASE   ,   )
  model.push_to_hub(
      repo_path_or_name=Path(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   organization="""nielsr"""   ,   commit_message="""Add model"""   ,   use_temp_dir=SCREAMING_SNAKE_CASE   ,   )


if __name__ == "__main__":
      _UpperCAmelCase             =							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',
      )
      _UpperCAmelCase             =							parser.parse_args()
      convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        snake_case_    (		__lowercase						):
  A_      							=   ['image_processor', 'tokenizer']
  A_      							=   'ChineseCLIPImageProcessor'
  A_      							=   ('BertTokenizer', 'BertTokenizerFast')






  def __init__(							self			:					Optional[Any]       ,  _snake_case			:					List[Any]=None       ,  _snake_case			:					str=None       ,  **_snake_case			:					int      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	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."""       ,  _snake_case       ,  )
    __lowerCAmelCase					:	List[str]      =       kwargs.pop("""feature_extractor"""      )

   __lowerCAmelCase					:	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__(_snake_case       ,  _snake_case      )
   __lowerCAmelCase					:	Any      =       self.image_processor






  def __call__(							self			:					Optional[Any]       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					List[str]=None       ,  **_snake_case			:					Any      )->Union[str, Any]:






   '''simple docstring'''

   if text is None and images is None:
    raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

   if text is not None:
    __lowerCAmelCase					:	List[Any]      =       self.tokenizer(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if images is not None:
    __lowerCAmelCase					:	Tuple      =       self.image_processor(_snake_case       ,  return_tensors=_snake_case       ,  **_snake_case      )

   if text is not None and images is not None:
    __lowerCAmelCase					:	Optional[Any]      =       image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**_snake_case      )       ,  tensor_type=_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  *_snake_case			:					Union[str, Any]       ,  **_snake_case			:					int      )->Optional[Any]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Dict       ,  *_snake_case			:					Dict       ,  **_snake_case			:					Any      )->Dict:






   '''simple docstring'''

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






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.tokenizer.model_input_names
   __lowerCAmelCase					:	Tuple      =       self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->Union[str, Any]:






   '''simple docstring'''

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