



'''simple docstring'''




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_       :  Union[str, Any]					=  logging.get_logger(__name__)

UpperCamelCase_       :  Union[str, Any]					=  {"vocab_file": "spiece.model"}

UpperCamelCase_       :  List[Any]					=  {
    "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_       :  Any					=  {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}



class    lowerCamelCase__				(			a_						):






      """simple docstring"""
      UpperCamelCase__			 =     VOCAB_FILES_NAMES
      UpperCamelCase__			 =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__			 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__			 =     ["input_ids", "attention_mask"]
      UpperCamelCase__			 =     []




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

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

             a__								= {} if sp_model_kwargs is None else sp_model_kwargs

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

             a__								= vocab_file

             a__								= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
             self.sp_model.Load(a__   )




      @property
      def lowerCAmelCase_					(       self							:     str   ):
             return self.sp_model.get_piece_size()




      def lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= {self.convert_ids_to_tokens(a__   ): i for i in range(self.vocab_size   )}
             vocab.update(self.added_tokens_encoder   )
             return vocab




      def __getstate__(       self							:     List[str]   ):
             a__								= self.__dict__.copy()
             a__								= None
             return state




      def __setstate__(       self							:     int			,a__							:     int   ):
             a__								= d

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

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




      def lowerCAmelCase_					(       self							:     str			,a__							:     str   ):
             return self.sp_model.encode(a__			,out_type=a__   )




      def lowerCAmelCase_					(       self							:     str			,a__							:     Tuple   ):
             return self.sp_model.piece_to_id(a__   )




      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     int   ):
             a__								= self.sp_model.IdToPiece(a__   )
             return token




      def lowerCAmelCase_					(       self							:     str			,a__							:     Dict   ):
             a__								= []
             a__								= ""
             a__								= 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(a__   ) + token
                           a__								= True
                           a__								= []
                    else:
                           current_sub_tokens.append(a__   )
                           a__								= False
             out_string += self.sp_model.decode(a__   )
             return out_string.strip()




      def lowerCAmelCase_					(       self							:     Optional[int]			,a__							:     Dict			,a__							:     str = False			,a__							:     Dict = None			,a__							:     Tuple = True			,**a__							:     Optional[Any]			,):
             a__								= kwargs.pop("use_source_tokenizer"			,a__   )

             a__								= self.convert_ids_to_tokens(a__			,skip_special_tokens=a__   )

             # 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
             a__								= []
             a__								= []
             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(a__   )   )
                                  a__								= []
                           sub_texts.append(a__   )
                    else:
                           current_sub_text.append(a__   )
             if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(a__   )   )

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

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




      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     Optional[int]			,a__							:     List[str] = None   ):







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

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

             return (out_vocab_file,)




      def lowerCAmelCase_					(       self							:     str			,a__							:     Dict			,a__							:     Dict = None   ):







             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             a__								= [self.cls_token_id]
             a__								= [self.sep_token_id]
             return cls + token_ids_a + sep + token_ids_a + sep




      def lowerCAmelCase_					(       self							:     int			,a__							:     int			,a__							:     List[Any] = None			,a__							:     str = False   ):







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

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




      def lowerCAmelCase_					(       self							:     Union[str, Any]			,a__							:     List[str]			,a__							:     Optional[int] = None   ):
             a__								= [self.sep_token_id]
             a__								= [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]




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase   :    Any     =		{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Union[str, Any]     =		[
		    "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoCBertForCausalLM",
		    "RoCBertForMaskedLM",
		    "RoCBertForMultipleChoice",
		    "RoCBertForPreTraining",
		    "RoCBertForQuestionAnswering",
		    "RoCBertForSequenceClassification",
		    "RoCBertForTokenClassification",
		    "RoCBertLayer",
		    "RoCBertModel",
		    "RoCBertPreTrainedModel",
		    "load_tf_weights_in_roc_bert",
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		UpperCAmelCase   :    Tuple     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

