




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{'vocab_file': 'spiece.model'}

UpperCamelCase								=	{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

UpperCamelCase								=	{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = VOCAB_FILES_NAMES
    snake_case__   = PRETRAINED_VOCAB_FILES_MAP
    snake_case__   = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__   = ["input_ids", "attention_mask"]
    snake_case__   = []


    def __init__(			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[str]="<unk>"  ,      SCREAMING_SNAKE_CASE__	:	List[str]="<s>"  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]="</s>"  ,      SCREAMING_SNAKE_CASE__	:	Tuple="<pad>"  ,      SCREAMING_SNAKE_CASE__	:	Any="[SEP]"  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]="[MASK]"  ,      SCREAMING_SNAKE_CASE__	:	List[Any]="[CLS]"  ,      SCREAMING_SNAKE_CASE__	:	Optional[Dict[str, Any]] = None  ,      **SCREAMING_SNAKE_CASE__	:	List[Any]  ,      ) ->			None:
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else bos_token
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else eos_token
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else unk_token
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else pad_token
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else cls_token
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowerCAmelCase__            =					AddedToken(SCREAMING_SNAKE_CASE__  ,      lstrip=SCREAMING_SNAKE_CASE__  ,      rstrip=SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) else mask_token

         lowerCAmelCase__            =					{} if sp_model_kwargs is None else sp_model_kwargs

         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__  ,      mask_token=SCREAMING_SNAKE_CASE__  ,      cls_token=SCREAMING_SNAKE_CASE__  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **SCREAMING_SNAKE_CASE__  ,      )

         lowerCAmelCase__            =					vocab_file

         lowerCAmelCase__            =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(SCREAMING_SNAKE_CASE__  )


    @property
    def   a (			self	:	List[str]  ) ->			List[str]:
         return self.sp_model.get_piece_size()


    def   a (			self	:	List[str]  ) ->			Dict:
         lowerCAmelCase__            =					{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	:	Optional[int]  ) ->			Any:
         lowerCAmelCase__            =					self.__dict__.copy()
         lowerCAmelCase__            =					None
         return state


    def __setstate__(			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ) ->			Any:
         lowerCAmelCase__            =					d

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

         lowerCAmelCase__            =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(self.vocab_file  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE__  ,      out_type=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ) ->			Tuple:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]  ) ->			List[str]:
         lowerCAmelCase__            =					self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__  )
         return token


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ) ->			str:
         lowerCAmelCase__            =					[]
         lowerCAmelCase__            =					""
         lowerCAmelCase__            =					False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   if not prev_is_special:
                        out_string += " "
                   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__  ) + token
                   lowerCAmelCase__            =					True
                   lowerCAmelCase__            =					[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE__  )
                   lowerCAmelCase__            =					False
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__  )
         return out_string.strip()


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			str:
         lowerCAmelCase__            =					kwargs.pop("use_source_tokenizer"  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  ,      skip_special_tokens=SCREAMING_SNAKE_CASE__  )

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         lowerCAmelCase__            =					[]
         lowerCAmelCase__            =					[]
         for token in filtered_tokens:
              if skip_special_tokens and token in self.all_special_ids:
                   continue
              if token in self.added_tokens_encoder:
                   if current_sub_text:
                        sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE__  )  )
                        lowerCAmelCase__            =					[]
                   sub_texts.append(SCREAMING_SNAKE_CASE__  )
              else:
                   current_sub_text.append(SCREAMING_SNAKE_CASE__  )
         if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE__  )  )

         # Mimic the behavior of the Rust tokenizer:
         # No space before [MASK] and [SEP]
         if spaces_between_special_tokens:
              lowerCAmelCase__            =					re.sub(r" (\[(MASK|SEP)\])"  ,      r"\1"  ,      " ".join(SCREAMING_SNAKE_CASE__  )  )
         else:
              lowerCAmelCase__            =					"".join(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					(
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
              lowerCAmelCase__            =					self.clean_up_tokenization(SCREAMING_SNAKE_CASE__  )
              return clean_text
         else:
              return text


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	str  ,      SCREAMING_SNAKE_CASE__	:	Optional[str] = None  ) ->			Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE__  ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
              return
         lowerCAmelCase__            =					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:
                   lowerCAmelCase__            =					self.sp_model.serialized_model_proto()
                   fi.write(SCREAMING_SNAKE_CASE__  )

         return (out_vocab_file,)


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[int]] = None  ) ->			List[int]:
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowerCAmelCase__            =					[self.cls_token_id]
         lowerCAmelCase__            =					[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[int]] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ) ->			List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=SCREAMING_SNAKE_CASE__  ,      token_ids_a=SCREAMING_SNAKE_CASE__  ,      already_has_special_tokens=SCREAMING_SNAKE_CASE__  )

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


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	List[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[List[int]] = None  ) ->			List[int]:
         lowerCAmelCase__            =					[self.sep_token_id]
         lowerCAmelCase__            =					[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep  ) * [0]
         return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




"""simple docstring"""






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


UpperCamelCase__				:str			   =	logging.get_logger(__name__)

UpperCamelCase__				:Optional[int]			   =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"markuplm"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3_05_22					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=30_72					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=1E-12					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2_56					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=2_16					,				SCREAMING_SNAKE_CASE__=10_01					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=50					,				SCREAMING_SNAKE_CASE__="absolute"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__(
					    pad_token_id=SCREAMING_SNAKE_CASE__					,				bos_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:Any					     =	vocab_size
					_UpperCamelCase					:Union[str, Any]					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	num_hidden_layers
					_UpperCamelCase					:int					     =	num_attention_heads
					_UpperCamelCase					:Tuple					     =	hidden_act
					_UpperCamelCase					:str					     =	intermediate_size
					_UpperCamelCase					:Optional[int]					     =	hidden_dropout_prob
					_UpperCamelCase					:Any					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Union[str, Any]					     =	max_position_embeddings
					_UpperCamelCase					:Dict					     =	type_vocab_size
					_UpperCamelCase					:int					     =	initializer_range
					_UpperCamelCase					:Optional[int]					     =	layer_norm_eps
					_UpperCamelCase					:Any					     =	position_embedding_type
					_UpperCamelCase					:Any					     =	use_cache
					_UpperCamelCase					:List[str]					     =	classifier_dropout
					# additional properties
					_UpperCamelCase					:Union[str, Any]					     =	max_depth
					_UpperCamelCase					:Union[str, Any]					     =	max_xpath_tag_unit_embeddings
					_UpperCamelCase					:Optional[Any]					     =	max_xpath_subs_unit_embeddings
					_UpperCamelCase					:int					     =	tag_pad_id
					_UpperCamelCase					:str					     =	subs_pad_id
					_UpperCamelCase					:List[str]					     =	xpath_unit_hidden_size

