





'''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


lowercase__						:      Tuple	=    logging.get_logger(__name__)

lowercase__						:      Union[str, Any]	=    {'vocab_file': 'spiece.model'}

lowercase__						:      Optional[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'
        ),
    }
}

lowercase__						:      Tuple	=    {
    'google/bigbird-roberta-base': 40_96,
    'google/bigbird-roberta-large': 40_96,
    'google/bigbird-base-trivia-itc': 40_96,
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Optional[Any]						    =   VOCAB_FILES_NAMES
       _snake_case     :					Any						    =   PRETRAINED_VOCAB_FILES_MAP
       _snake_case     :					int						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case     :					Tuple						    =   ['input_ids', 'attention_mask']
       _snake_case     :					List[int]						    =   []





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

            '''simple docstring'''




            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else bos_token
            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else eos_token
            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else unk_token
            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else pad_token
            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else cls_token
            _UpperCamelCase       =	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
            _UpperCamelCase       =	AddedToken(lowerCAmelCase__	,			lstrip=lowerCAmelCase__	,			rstrip=lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else mask_token

            _UpperCamelCase       =	{} 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__	,			)

            _UpperCamelCase       =	vocab_file

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





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

            '''simple docstring'''




            return self.sp_model.get_piece_size()





       def snake_case__						(							self       :		List[Any]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	{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       :		Tuple					)   ->					Tuple:

            '''simple docstring'''




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





       def __setstate__(							self       :		int	,			lowerCAmelCase__       :		List[str]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	d

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

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





       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		str					)   ->					List[str]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		Tuple					)   ->					Dict:

            '''simple docstring'''




            return self.sp_model.piece_to_id(lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		Optional[int]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.sp_model.IdToPiece(lowerCAmelCase__					)
            return token





       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		List[Any]					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	[]
            _UpperCamelCase       =	''''''
            _UpperCamelCase       =	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
                      _UpperCamelCase       =	True
                      _UpperCamelCase       =	[]
                 else:
                      current_sub_tokens.append(lowerCAmelCase__					)
                      _UpperCamelCase       =	False
            out_string += self.sp_model.decode(lowerCAmelCase__					)
            return out_string.strip()





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

            '''simple docstring'''




            _UpperCamelCase       =	kwargs.pop('''use_source_tokenizer'''	,			lowerCAmelCase__					)

            _UpperCamelCase       =	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
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            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__					)					)
                           _UpperCamelCase       =	[]
                      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:
                 _UpperCamelCase       =	re.sub(r''' (\[(MASK|SEP)\])'''	,			r'''\1'''	,			''' '''.join(lowerCAmelCase__					)					)
            else:
                 _UpperCamelCase       =	''''''.join(lowerCAmelCase__					)

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





       def 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
            _UpperCamelCase       =	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:
                      _UpperCamelCase       =	self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__					)

            return (out_vocab_file,)





       def snake_case__						(							self       :		str	,			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]
            _UpperCamelCase       =	[self.cls_token_id]
            _UpperCamelCase       =	[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep





       def snake_case__						(							self       :		Any	,			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 snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		List[int]	,			lowerCAmelCase__       :		Optional[List[int]] = None					)   ->					List[int]:

            '''simple docstring'''




            _UpperCamelCase       =	[self.sep_token_id]
            _UpperCamelCase       =	[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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__						:      Optional[Any]	=    {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      List[Any]	=    [
       'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'AutoformerForPrediction',
       'AutoformerModel',
       'AutoformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_autoformer import (
       AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AutoformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_autoformer import (
          AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          AutoformerForPrediction,
          AutoformerModel,
          AutoformerPreTrainedModel,
      )

else:
   import sys

   lowercase__						:      Tuple	=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



