


"""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


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       '''▁'''

__snake_case		=       {'''vocab_file''': '''sentencepiece.bpe.model'''}

__snake_case		=       {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__snake_case		=       {
    '''facebook/xglm-564M''': 2048,
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[str]    =		VOCAB_FILES_NAMES
     A_       :	Tuple    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	List[Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_       :	int    =		['input_ids', 'attention_mask']


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase="<s>"    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          _a    	=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          _a    	=				7
          _a    	=				[F'<madeupword{i}>' for i in range(self.num_madeup_words							)]

          _a    	=				kwargs.get('''additional_special_tokens'''    ,				[]							)
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(str(__UpperCAmelCase							)							)
          _a    	=				vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          _a    	=				1

          # Mimic fairseq token-to-id alignment for the first 4 token
          _a    	=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          _a    	=				len(self.sp_model							)
          _a    	=				{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
          self.fairseq_tokens_to_ids.update(__UpperCAmelCase							)

          _a    	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}


     def __getstate__(      self							)       ->							Union[str, Any]:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          _a    	=				self.sp_model.serialized_model_proto()
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Tuple:
          _a    	=				d

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

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:

          if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
          _a    	=				[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

          if token_ids_a is None:
               return [1] + ([0] * len(__UpperCAmelCase							))
          return [1] + ([0] * len(__UpperCAmelCase							)) + [1, 1] + ([0] * len(__UpperCAmelCase							))


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]

          if token_ids_a is None:
               return len(sep + token_ids_a							) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]


     @property
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				{self.convert_ids_to_tokens(__UpperCAmelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							int:
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          _a    	=				self.sp_model.PieceToId(__UpperCAmelCase							)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Dict:
          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							str:
          _a    	=				''''''.join(__UpperCAmelCase							).replace(__UpperCAmelCase    ,				''' '''							).strip()
          return out_string





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				os.path.join(
              __UpperCAmelCase    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

          return (out_vocab_file,)



"""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()
__snake_case		=       logging.get_logger(__name__)
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					Optional[int]=False,     _lowerCAmelCase     :					Any=False       ):






     """simple docstring"""

     _a    	=				'''backbone.''' if is_semantic else ''''''

     _a    	=				[]
     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       A_   (   _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					List[Any]=False       ):






     """simple docstring"""

     for i in range(config.num_hidden_layers       ):
          _a    	=				'''backbone.''' if is_semantic else ''''''
          # queries, keys and values
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'       )
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'       )
          _a    	=				state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'       )

          _a    	=				in_proj_weight[
              : config.hidden_size, :
          ]
          _a    	=				q_bias
          _a    	=				in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _a    	=				in_proj_weight[
              -config.hidden_size :, :
          ]
          _a    	=				v_bias

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

          _a    	=				gamma_a
          _a    	=				gamma_a
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				dct.pop(_lowerCAmelCase       )
     _a    	=				val
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]=False       ):






     """simple docstring"""

     _a    	=				False if '''rvlcdip''' in checkpoint_url else True
     _a    	=				BeitConfig(use_absolute_position_embeddings=_lowerCAmelCase,     use_mask_token=_lowerCAmelCase       )

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          _a    	=				10_24
          _a    	=				40_96
          _a    	=				24
          _a    	=				16

     # labels
     if "rvlcdip" in checkpoint_url:
          _a    	=				16
          _a    	=				'''huggingface/label-files'''
          _a    	=				'''rvlcdip-id2label.json'''
          _a    	=				json.load(open(hf_hub_download(_lowerCAmelCase,     _lowerCAmelCase,     repo_type='''dataset'''       ),     '''r'''       )       )
          _a    	=				{int(_lowerCAmelCase       ): v for k, v in idalabel.items()}
          _a    	=				idalabel
          _a    	=				{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     _a    	=				torch.hub.load_state_dict_from_url(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']

     _a    	=				create_rename_keys(_lowerCAmelCase,     has_lm_head=_lowerCAmelCase       )
     for src, dest in rename_keys:
          rename_key(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )
     read_in_q_k_v(_lowerCAmelCase,     _lowerCAmelCase,     has_lm_head=_lowerCAmelCase       )

     # load HuggingFace model
     _a    	=				BeitForMaskedImageModeling(_lowerCAmelCase       ) if has_lm_head else BeitForImageClassification(_lowerCAmelCase       )
     model.eval()
     model.load_state_dict(_lowerCAmelCase       )

     # Check outputs on an image
     _a    	=				BeitImageProcessor(
         size=config.image_size,     resample=PILImageResampling.BILINEAR,     do_center_crop=_lowerCAmelCase       )
     _a    	=				prepare_img()

     _a    	=				image_processor(images=_lowerCAmelCase,     return_tensors='''pt'''       )
     _a    	=				encoding['''pixel_values''']

     _a    	=				model(_lowerCAmelCase       )
     _a    	=				outputs.logits

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

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

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


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