





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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_						       =	{
    """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"""
        ),
    }
}

SCREAMING_SNAKE_CASE_						       =	{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      VOCAB_FILES_NAMES
     __snake_case   :     Optional[Any]		     =      PRETRAINED_VOCAB_FILES_MAP
     __snake_case   :     str		     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case   :     str		     =      ["input_ids", "attention_mask"]
     __snake_case   :     List[int]		     =      []






     def __init__(							self						:   Union[str, Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   str="<unk>"						,lowerCamelCase__						:   List[Any]="<s>"						,lowerCamelCase__						:   Dict="</s>"						,lowerCamelCase__						:   List[str]="<pad>"						,lowerCamelCase__						:   Tuple="[SEP]"						,lowerCamelCase__						:   Dict="[MASK]"						,lowerCamelCase__						:   List[Any]="[CLS]"						,lowerCamelCase__						:   Optional[Dict[str, Any]] = None						,**lowerCamelCase__						:   Union[str, Any]						,)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else bos_token
           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else eos_token
           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else unk_token
           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else pad_token
           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else cls_token
           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else sep_token

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

           SCREAMING_SNAKE_CASE		=						{} if sp_model_kwargs is None else sp_model_kwargs

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

           SCREAMING_SNAKE_CASE		=						vocab_file

           SCREAMING_SNAKE_CASE		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(lowerCamelCase__	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		str:




           '''simple docstring'''


           return self.sp_model.get_piece_size()






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{self.convert_ids_to_tokens(lowerCamelCase__	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab






     def __getstate__(							self						:   str	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.__dict__.copy()
           SCREAMING_SNAKE_CASE		=						None
           return state






     def __setstate__(							self						:   Any						,lowerCamelCase__						:   Optional[int]	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						d

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

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






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




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Optional[Any]	)					->		Dict:




           '''simple docstring'''


           return self.sp_model.piece_to_id(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   int	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.sp_model.IdToPiece(lowerCamelCase__	)
           return token






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						""""""
           SCREAMING_SNAKE_CASE		=						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(lowerCamelCase__	) + token
                       SCREAMING_SNAKE_CASE		=						True
                       SCREAMING_SNAKE_CASE		=						[]
                 else:
                       current_sub_tokens.append(lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						False
           out_string += self.sp_model.decode(lowerCamelCase__	)
           return out_string.strip()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   bool = True						,**lowerCamelCase__						:   Any						,)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""use_source_tokenizer"""						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.convert_ids_to_tokens(lowerCamelCase__						,skip_special_tokens=lowerCamelCase__	)

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

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

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Optional[str] = None	)					->		Tuple[str]:




           '''simple docstring'''


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

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

           return (out_vocab_file,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None						,lowerCamelCase__						:   bool = False	)					->		List[int]:




           '''simple docstring'''


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

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[self.sep_token_id]
           SCREAMING_SNAKE_CASE		=						[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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


