
'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


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

A__ :		Any					={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A__ :		int					={
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

A__ :		Union[str, Any]					={
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=VOCAB_FILES_NAMES
 lowerCamelCase												=PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase												=['''input_ids''', '''attention_mask''']
 lowerCamelCase												=LongformerTokenizer
 def __init__( self      :     int					, lowerCamelCase      :     Union[str, Any]=None					, lowerCamelCase      :     List[Any]=None					, lowerCamelCase      :     Optional[int]=None					, lowerCamelCase      :     int="replace"					, lowerCamelCase      :     List[Any]="<s>"					, lowerCamelCase      :     Dict="</s>"					, lowerCamelCase      :     Union[str, Any]="</s>"					, lowerCamelCase      :     Tuple="<s>"					, lowerCamelCase      :     Tuple="<unk>"					, lowerCamelCase      :     Dict="<pad>"					, lowerCamelCase      :     List[Any]="<mask>"					, lowerCamelCase      :     Union[str, Any]=False					, lowerCamelCase      :     str=True					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





   super().__init__(
       lowerCamelCase					, lowerCamelCase					, tokenizer_file=lowerCamelCase					, errors=lowerCamelCase					, bos_token=lowerCamelCase					, eos_token=lowerCamelCase					, sep_token=lowerCamelCase					, cls_token=lowerCamelCase					, unk_token=lowerCamelCase					, pad_token=lowerCamelCase					, mask_token=lowerCamelCase					, add_prefix_space=lowerCamelCase					, trim_offsets=lowerCamelCase					, **lowerCamelCase					, )

   __A		:  Optional[int]       				=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
   if pre_tok_state.get("""add_prefix_space"""					, lowerCamelCase							) != add_prefix_space:
     __A		:  Union[str, Any]       				=		getattr(lowerCamelCase					, pre_tok_state.pop("""type"""							)							)
     __A		:  List[str]       				=		add_prefix_space
     __A		:  List[Any]       				=		pre_tok_class(**lowerCamelCase							)

   __A		:  Union[str, Any]       				=		add_prefix_space

   __A		:  str       				=		"""post_processor"""
   __A		:  Tuple       				=		getattr(self.backend_tokenizer					, lowerCamelCase					, lowerCamelCase							)
   if tokenizer_component_instance:
     __A		:  Tuple       				=		json.loads(tokenizer_component_instance.__getstate__()							)

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
       __A		:  str       				=		tuple(state["""sep"""]							)
     if "cls" in state:
       __A		:  List[str]       				=		tuple(state["""cls"""]							)

     __A		:  List[Any]       				=		False

     if state.get("""add_prefix_space"""					, lowerCamelCase							) != add_prefix_space:
       __A		:  Any       				=		add_prefix_space
       __A		:  Dict       				=		True

     if state.get("""trim_offsets"""					, lowerCamelCase							) != trim_offsets:
       __A		:  List[str]       				=		trim_offsets
       __A		:  Any       				=		True

     if changes_to_apply:
       __A		:  Union[str, Any]       				=		getattr(lowerCamelCase					, state.pop("""type"""							)							)
       __A		:  List[Any]       				=		component_class(**lowerCamelCase							)
       setattr(self.backend_tokenizer					, lowerCamelCase					, lowerCamelCase							)
 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   if self._mask_token is None:
     if self.verbose:
       logger.error("""Using mask_token, but it is not set yet."""							)
     return None
   return str(self._mask_token							)
 @mask_token.setter
 def 		lowercase_( self      :     List[Any]					, lowerCamelCase      :     List[str]							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		AddedToken(lowerCamelCase					, lstrip=lowerCamelCase					, rstrip=lowerCamelCase							) if isinstance(lowerCamelCase					, lowerCamelCase							) else value
   __A		:  Optional[Any]       				=		value
 def 		lowercase_( self      :     int					, *lowerCamelCase      :     Dict					, **lowerCamelCase      :     Tuple							):



   """simple docstring"""





   __A		:  List[str]       				=		kwargs.get("""is_split_into_words"""					, lowerCamelCase							)
   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Tuple					, *lowerCamelCase      :     str					, **lowerCamelCase      :     Tuple							):



   """simple docstring"""





   __A		:  Tuple       				=		kwargs.get("""is_split_into_words"""					, lowerCamelCase							)

   assert self.add_prefix_space or not is_split_into_words, (
       f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     str					, lowerCamelCase      :     Optional[str] = None							):



   """simple docstring"""





   __A		:  int       				=		self._tokenizer.model.save(lowerCamelCase					, name=lowerCamelCase							)
   return tuple(lowerCamelCase							)
 def 		lowercase_( self      :     Optional[int]					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     List[Any]=None							):



   """simple docstring"""





   __A		:  str       				=		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     List[int]					, lowerCamelCase      :     Optional[List[int]] = None							):



   """simple docstring"""





   __A		:  int       				=		[self.sep_token_id]
   __A		:  str       				=		[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 + sep + token_ids_a + sep							) * [0]





'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__ :		str					={'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[int]					=['T5Tokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'T5EncoderModel',
         'T5ForConditionalGeneration',
         'T5Model',
         'T5PreTrainedModel',
         'load_tf_weights_in_t5',
         'T5ForQuestionAnswering',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         'FlaxT5EncoderModel',
         'FlaxT5ForConditionalGeneration',
         'FlaxT5Model',
         'FlaxT5PreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta import TaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta_fast import TaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ta import (
              T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TaEncoderModel,
              TaForConditionalGeneration,
              TaForQuestionAnswering,
              TaModel,
              TaPreTrainedModel,
              load_tf_weights_in_ta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_ta import (
              TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTaEncoderModel,
              TFTaForConditionalGeneration,
              TFTaModel,
              TFTaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_ta import (
              FlaxTaEncoderModel,
              FlaxTaForConditionalGeneration,
              FlaxTaModel,
              FlaxTaPreTrainedModel,
          )


else:
     import sys

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



