



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_bart import BartTokenizer


a_   :List[Any]        =      logging.get_logger(__name__)


a_   :Optional[int]        =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
a_   :List[str]        =      {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

a_   :Tuple        =      {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						=	["""input_ids""", """attention_mask"""]
     _SCREAMING_SNAKE_CASE						=	BartTokenizer






     def __init__(       self  :  Optional[int], _snake_case  :  Optional[Any]=None, _snake_case  :  Dict=None, _snake_case  :  Dict=None, _snake_case  :  List[str]="replace", _snake_case  :  int="<s>", _snake_case  :  Optional[Any]="</s>", _snake_case  :  List[Any]="</s>", _snake_case  :  int="<s>", _snake_case  :  Optional[Any]="<unk>", _snake_case  :  str="<pad>", _snake_case  :  Union[str, Any]="<mask>", _snake_case  :  int=False, _snake_case  :  Optional[int]=True, **_snake_case  :  Any, )   ->int:
            super().__init__(
                _snake_case, _snake_case, tokenizer_file=_snake_case, errors=_snake_case, bos_token=_snake_case, eos_token=_snake_case, sep_token=_snake_case, cls_token=_snake_case, unk_token=_snake_case, pad_token=_snake_case, mask_token=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case, **_snake_case, )

            snake_case__							:	str   	=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
            if pre_tok_state.get('add_prefix_space', _snake_case					) != add_prefix_space:
                   snake_case__							:	Tuple   	=	getattr(_snake_case, pre_tok_state.pop('type'					)					)
                   snake_case__							:	List[Any]   	=	add_prefix_space
                   snake_case__							:	str   	=	pre_tok_class(**_snake_case					)

            snake_case__							:	int   	=	add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            snake_case__							:	Any   	=	'post_processor'
            snake_case__							:	List[Any]   	=	getattr(self.backend_tokenizer, _snake_case, _snake_case					)
            if tokenizer_component_instance:
                   snake_case__							:	Union[str, Any]   	=	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:
                          snake_case__							:	Tuple   	=	tuple(state['sep']					)
                   if "cls" in state:
                          snake_case__							:	Optional[Any]   	=	tuple(state['cls']					)

                   snake_case__							:	int   	=	False

                   if state.get('add_prefix_space', _snake_case					) != add_prefix_space:
                          snake_case__							:	int   	=	add_prefix_space
                          snake_case__							:	Tuple   	=	True

                   if state.get('trim_offsets', _snake_case					) != trim_offsets:
                          snake_case__							:	Optional[Any]   	=	trim_offsets
                          snake_case__							:	Tuple   	=	True

                   if changes_to_apply:
                          snake_case__							:	Union[str, Any]   	=	getattr(_snake_case, state.pop('type'					)					)
                          snake_case__							:	List[str]   	=	component_class(**_snake_case					)
                          setattr(self.backend_tokenizer, _snake_case, _snake_case					)






     @property
     def    lowercase_   (       self  :  List[str]					)   ->str:
            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  :  Union[str, Any], _snake_case  :  Optional[int]					)   ->Dict:
            snake_case__							:	List[str]   	=	AddedToken(_snake_case, lstrip=_snake_case, rstrip=_snake_case					) if isinstance(_snake_case, _snake_case					) else value
            snake_case__							:	Union[str, Any]   	=	value






     def    lowercase_   (       self  :  str, *_snake_case  :  str, **_snake_case  :  Optional[Any]					)   ->BatchEncoding:
            snake_case__							:	str   	=	kwargs.get('is_split_into_words', _snake_case					)

            if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                       'to use it with pretokenized inputs.'					)

            return super()._batch_encode_plus(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, *_snake_case  :  List[str], **_snake_case  :  int					)   ->BatchEncoding:
            snake_case__							:	List[str]   	=	kwargs.get('is_split_into_words', _snake_case					)

            if is_split_into_words and not self.add_prefix_space:
                   raise ValueError(
                       F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                       'to use it with pretokenized inputs.'					)

            return super()._encode_plus(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  str, _snake_case  :  Optional[str] = None					)   ->Tuple[str]:
            snake_case__							:	Dict   	=	self._tokenizer.model.save(_snake_case, name=_snake_case					)
            return tuple(_snake_case					)






     def    lowercase_   (       self  :  str, _snake_case  :  int, _snake_case  :  str=None					)   ->Dict:
            snake_case__							:	Tuple   	=	[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[Any], _snake_case  :  List[int], _snake_case  :  Optional[List[int]] = None					)   ->List[int]:
            snake_case__							:	Any   	=	[self.sep_token_id]
            snake_case__							:	Optional[Any]   	=	[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]











from typing import TYPE_CHECKING

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


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






