




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


_snake_case            = logging.get_logger(__name__)


_snake_case            = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
_snake_case            = {
    "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",
    },
}

_snake_case            = {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}







class 		lowercase							(     UpperCamelCase__	):
     _a											=		VOCAB_FILES_NAMES
     _a											=		PRETRAINED_VOCAB_FILES_MAP
     _a											=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a											=		["input_ids", "attention_mask"]
     _a											=		BartTokenizer

     def __init__(						self	,  _a=None	,  _a=None	,  _a=None	,  _a="replace"	,  _a="<s>"	,  _a="</s>"	,  _a="</s>"	,  _a="<s>"	,  _a="<unk>"	,  _a="<pad>"	,  _a="<mask>"	,  _a=False	,  _a=True	,  **_a	,  )		->							Tuple:
           super().__init__(
               _a	,  _a	,  tokenizer_file=_a	,  errors=_a	,  bos_token=_a	,  eos_token=_a	,  sep_token=_a	,  cls_token=_a	,  unk_token=_a	,  pad_token=_a	,  mask_token=_a	,  add_prefix_space=_a	,  trim_offsets=_a	,  **_a	,  )

           _A							:  Dict    =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""	,  _a       ) != add_prefix_space:
                 _A							:  Tuple    =		getattr(_a	,  pre_tok_state.pop("""type"""       )       )
                 _A							:  Union[str, Any]    =		add_prefix_space
                 _A							:  List[str]    =		pre_tok_class(**_a       )

           _A							:  List[Any]    =		add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           _A							:  int    =		"""post_processor"""
           _A							:  Optional[Any]    =		getattr(self.backend_tokenizer	,  _a	,  _a       )
           if tokenizer_component_instance:
                 _A							:  Dict    =		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							:  str    =		False

                 if state.get("""add_prefix_space"""	,  _a       ) != add_prefix_space:
                       _A							:  Any    =		add_prefix_space
                       _A							:  Dict    =		True

                 if state.get("""trim_offsets"""	,  _a       ) != trim_offsets:
                       _A							:  List[Any]    =		trim_offsets
                       _A							:  Union[str, Any]    =		True

                 if changes_to_apply:
                       _A							:  List[str]    =		getattr(_a	,  state.pop("""type"""       )       )
                       _A							:  int    =		component_class(**_a       )
                       setattr(self.backend_tokenizer	,  _a	,  _a       )

     @property
     def       a__			(						self       )		->							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       a__			(						self	,  _a       )		->							Tuple:
           _A							:  Union[str, Any]    =		AddedToken(_a	,  lstrip=_a	,  rstrip=_a       ) if isinstance(_a	,  _a       ) else value
           _A							:  Any    =		value

     def       a__			(						self	,  *_a	,  **_a       )		->							BatchEncoding:
           _A							:  Optional[int]    =		kwargs.get("""is_split_into_words"""	,  _a       )

           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(*_a	,  **_a       )

     def       a__			(						self	,  *_a	,  **_a       )		->							BatchEncoding:
           _A							:  str    =		kwargs.get("""is_split_into_words"""	,  _a       )

           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(*_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a = None       )		->							Tuple[str]:
           _A							:  Union[str, Any]    =		self._tokenizer.model.save(_a	,  name=_a       )
           return tuple(_a       )

     def       a__			(						self	,  _a	,  _a=None       )		->							Tuple:
           _A							:  Any    =		[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       a__			(						self	,  _a	,  _a = None       )		->							List[int]:
           _A							:  List[Any]    =		[self.sep_token_id]
           _A							:  Tuple    =		[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_torch_available


_snake_case            = {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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

