





"""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()
lowerCamelCase_       =		logging.get_logger(__name__)



def 					__lowerCamelCase       (							a_					:		List[str]			,       a_					:		Optional[int]=False			,       a_					:		str=False							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''backbone.''' if is_semantic else ''''''

      __SCREAMING_SNAKE_CASE       :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 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Dict			,       a_					:		Dict=False			,       a_					:		List[str]=False							)      ->				Any:
      for i in range(config.num_hidden_layers							):
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  '''backbone.''' if is_semantic else ''''''
            # queries, keys and values
            __SCREAMING_SNAKE_CASE       :str		    =  state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''							)
            __SCREAMING_SNAKE_CASE       :Dict		    =  state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''							)
            __SCREAMING_SNAKE_CASE       :Tuple		    =  state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''							)

            __SCREAMING_SNAKE_CASE       :int		    =  in_proj_weight[
                : config.hidden_size, :
            ]
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  q_bias
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            __SCREAMING_SNAKE_CASE       :Tuple		    =  in_proj_weight[
                -config.hidden_size :, :
            ]
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            __SCREAMING_SNAKE_CASE       :str		    =  state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''							)
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''							)

            __SCREAMING_SNAKE_CASE       :Any		    =  gamma_a
            __SCREAMING_SNAKE_CASE       :Tuple		    =  gamma_a



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Optional[int]			,       a_					:		str							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  dct.pop(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  val



def 					__lowerCamelCase       (							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :str		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :str		    =  Image.open(requests.get(a_			,       stream=a_							).raw							)
      return im



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Optional[int]			,       a_					:		int=False							)      ->				Optional[Any]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  False if '''rvlcdip''' in checkpoint_url else True
      __SCREAMING_SNAKE_CASE       :Dict		    =  BeitConfig(use_absolute_position_embeddings=a_			,       use_mask_token=a_							)

      # size of the architecture
      if "large" in checkpoint_url or "dit-l" in checkpoint_url:
            __SCREAMING_SNAKE_CASE       :int		    =  10_24
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  40_96
            __SCREAMING_SNAKE_CASE       :Any		    =  24
            __SCREAMING_SNAKE_CASE       :str		    =  16

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

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

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  create_rename_keys(a_			,       has_lm_head=a_							)
      for src, dest in rename_keys:
            rename_key(a_			,       a_			,       a_							)
      read_in_q_k_v(a_			,       a_			,       has_lm_head=a_							)

      # load HuggingFace model
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  BeitForMaskedImageModeling(a_							) if has_lm_head else BeitForImageClassification(a_							)
      model.eval()
      model.load_state_dict(a_							)

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

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  image_processor(images=a_			,       return_tensors='''pt'''							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  encoding['''pixel_values''']

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model(a_							)
      __SCREAMING_SNAKE_CASE       :Dict		    =  outputs.logits

      # verify logits
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 1_96, 81_92]
      assert logits.shape == torch.Size(a_							), "Shape of logits not as expected"

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

      if push_to_hub:
            if has_lm_head:
                  __SCREAMING_SNAKE_CASE       :Tuple		    =  '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
            else:
                  __SCREAMING_SNAKE_CASE       :str		    =  '''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(a_			,       a_							)			,       organization='''nielsr'''			,       commit_message='''Add image processor'''			,       use_temp_dir=a_			,       )
            model.push_to_hub(
                repo_path_or_name=Path(a_			,       a_							)			,       organization='''nielsr'''			,       commit_message='''Add model'''			,       use_temp_dir=a_			,       )


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






"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_       =		logging.get_logger(__name__)

lowerCamelCase_       =		{"vocab_file": "spiece.model"}

lowerCamelCase_       =		{
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

lowerCamelCase_       =		{"bert_for_seq_generation": 5_1_2}






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_       :				List[int]     						=  []
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  ['''input_ids''', '''attention_mask''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="<s>"		,SCREAMING_SNAKE_CASE__="</s>"		,SCREAMING_SNAKE_CASE__="<unk>"		,SCREAMING_SNAKE_CASE__="<pad>"		,SCREAMING_SNAKE_CASE__="<::::>"		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  {} if sp_model_kwargs is None else sp_model_kwargs

             # Add extra_ids to the special token list
             super().__init__(
                 bos_token=SCREAMING_SNAKE_CASE__		,eos_token=SCREAMING_SNAKE_CASE__		,unk_token=SCREAMING_SNAKE_CASE__		,pad_token=SCREAMING_SNAKE_CASE__		,sep_token=SCREAMING_SNAKE_CASE__		,sp_model_kwargs=self.sp_model_kwargs		,**SCREAMING_SNAKE_CASE__		,)

             __SCREAMING_SNAKE_CASE       :Dict		    =  vocab_file

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(SCREAMING_SNAKE_CASE__							)




       @property
       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             return self.sp_model.get_piece_size()




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__							): i for i in range(self.vocab_size							)}
             vocab.update(self.added_tokens_encoder							)
             return vocab




       def __getstate__(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.__dict__.copy()
             __SCREAMING_SNAKE_CASE       :Dict		    =  None
             return state




       def __setstate__(						self		,SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  d

             # for backward compatibility
             if not hasattr(self		,'''sp_model_kwargs'''							):
                   __SCREAMING_SNAKE_CASE       :str		    =  {}

             __SCREAMING_SNAKE_CASE       :str		    =  spm.SentencePieceProcessor(**self.sp_model_kwargs							)
             self.sp_model.Load(self.vocab_file							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             return self.sp_model.encode(SCREAMING_SNAKE_CASE__		,out_type=SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__							)
             return token




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  []
             __SCREAMING_SNAKE_CASE       :List[str]		    =  ''''''
             for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__							) + token
                         __SCREAMING_SNAKE_CASE       :Dict		    =  []
                   else:
                         current_sub_tokens.append(SCREAMING_SNAKE_CASE__							)
             out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__							)
             return out_string.strip()




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)      ->				Tuple[str]:





             """simple docstring"""






             if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  os.path.join(
                 SCREAMING_SNAKE_CASE__		,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

             if os.path.abspath(self.vocab_file							) != os.path.abspath(SCREAMING_SNAKE_CASE__							) and os.path.isfile(self.vocab_file							):
                   copyfile(self.vocab_file		,SCREAMING_SNAKE_CASE__							)
             elif not os.path.isfile(self.vocab_file							):
                   with open(SCREAMING_SNAKE_CASE__		,'''wb'''							) as fi:
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  self.sp_model.serialized_model_proto()
                         fi.write(SCREAMING_SNAKE_CASE__							)

             return (out_vocab_file,)