

import os
import unicodedata
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 SPIECE_UNDERLINE, logging


__A			:					List[str]						=			logging.get_logger(__name__)

__A			:					List[Any]						=			{'vocab_file': 'spiece.model'}

__A			:					Union[str, Any]						=			{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

__A			:					Any						=			{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
__A			:					Tuple						=			0
__A			:					Tuple						=			1
__A			:					str						=			2
__A			:					Optional[Any]						=			3
__A			:					Union[str, Any]						=			4





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      "left"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Any=True     , __lowerCamelCase					:			Dict=False     , __lowerCamelCase					:			Optional[int]="<s>"     , __lowerCamelCase					:			int="</s>"     , __lowerCamelCase					:			Dict="<unk>"     , __lowerCamelCase					:			Optional[Any]="<sep>"     , __lowerCamelCase					:			Dict="<pad>"     , __lowerCamelCase					:			List[str]="<cls>"     , __lowerCamelCase					:			str="<mask>"     , __lowerCamelCase					:			int=["<eop>", "<eod>"]     , __lowerCamelCase					:			Optional[Dict[str, Any]] = None     , **__lowerCamelCase					:			Any     , ):
       # 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__(
           do_lower_case=__lowerCamelCase     , remove_space=__lowerCamelCase     , keep_accents=__lowerCamelCase     , bos_token=__lowerCamelCase     , eos_token=__lowerCamelCase     , unk_token=__lowerCamelCase     , sep_token=__lowerCamelCase     , pad_token=__lowerCamelCase     , cls_token=__lowerCamelCase     , mask_token=__lowerCamelCase     , additional_special_tokens=__lowerCamelCase     , sp_model_kwargs=self.sp_model_kwargs     , **__lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   3

       SCREAMING_SNAKE_CASE										=   do_lower_case
       SCREAMING_SNAKE_CASE										=   remove_space
       SCREAMING_SNAKE_CASE										=   keep_accents
       SCREAMING_SNAKE_CASE										=   vocab_file

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





   @property
   def      _snake_case		(       self					:			Dict			):
       return len(self.sp_model			)





   def      _snake_case		(       self					:			List[Any]			):
       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					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.__dict__.copy()
       SCREAMING_SNAKE_CASE										=   None
       return state





   def __setstate__(       self					:			Optional[Any]     , __lowerCamelCase					:			Tuple			):
       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      _snake_case		(       self					:			str     , __lowerCamelCase					:			Optional[Any]			):
       if self.remove_space:
           SCREAMING_SNAKE_CASE										=   " ".join(inputs.strip().split()			)
       else:
           SCREAMING_SNAKE_CASE										=   inputs
       SCREAMING_SNAKE_CASE										=   outputs.replace("``"     , "\""			).replace("''"     , "\""			)

       if not self.keep_accents:
           SCREAMING_SNAKE_CASE										=   unicodedata.normalize("NFKD"     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   "".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase			)]			)
       if self.do_lower_case:
           SCREAMING_SNAKE_CASE										=   outputs.lower()

       return outputs





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   self.preprocess_text(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.sp_model.encode(__lowerCamelCase     , out_type=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   []
       for piece in pieces:
           if len(__lowerCamelCase			) > 1 and piece[-1] == str(","			) and piece[-2].isdigit():
               SCREAMING_SNAKE_CASE										=   self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase     , ""			)			)
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                   if len(cur_pieces[0]			) == 1:
                       SCREAMING_SNAKE_CASE										=   cur_pieces[1:]
                   else:
                       SCREAMING_SNAKE_CASE										=   cur_pieces[0][1:]
               cur_pieces.append(piece[-1]			)
               new_pieces.extend(__lowerCamelCase			)
           else:
               new_pieces.append(__lowerCamelCase			)

       return new_pieces





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Union[str, Any]			):
       return self.sp_model.PieceToId(__lowerCamelCase			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Union[str, Any]			):
       return self.sp_model.IdToPiece(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			).replace(__lowerCamelCase     , " "			).strip()
       return out_string





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			bool = False     , __lowerCamelCase					:			bool = None     , __lowerCamelCase					:			bool = True     , **__lowerCamelCase					:			Dict     , ):
       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:
       # By default, there are no spaces between special tokens
       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      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [self.cls_token_id]
       if token_ids_a is None:
           return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):

       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 not None:
           return ([0] * len(__lowerCamelCase			)) + [1] + ([0] * len(__lowerCamelCase			)) + [1, 1]
       return ([0] * len(__lowerCamelCase			)) + [1, 1]





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [2]

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





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       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,)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)