

"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      __UpperCAmelCase			=					None


__UpperCAmelCase			=					logging.get_logger(__name__)


__UpperCAmelCase			=					'▁'

__UpperCAmelCase			=					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase			=					{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__UpperCAmelCase			=					{
    'google/pegasus-xsum': 512,
}







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							VOCAB_FILES_NAMES
  snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
  snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_												=							PegasusTokenizer
  snake_case_												=							["""input_ids""", """attention_mask"""]

  def __init__(	self		:      Any       ,A		:      Any=None       ,A		:      Union[str, Any]=None       ,A		:      Optional[int]="<pad>"       ,A		:      Dict="</s>"       ,A		:      Optional[int]="<unk>"       ,A		:      Optional[Any]="<mask_2>"       ,A		:      int="<mask_1>"       ,A		:      Any=None       ,A		:      Union[str, Any]=103       ,**A		:      Tuple       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       offset

   if additional_special_tokens is not None:
    if not isinstance(lowerCamelCase_       ,lowerCamelCase_     ):
     raise TypeError(
         f"additional_special_tokens should be of type {type(lowerCamelCase_     )}, but is"
         f" {type(lowerCamelCase_     )}"     )

    UpperCAmelCase__       :	Union[str, Any]        =       (
        ([mask_token_sent] + additional_special_tokens)
        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
        else additional_special_tokens
    )
    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
    additional_special_tokens_extended += [
        f"<unk_{i}>" for i in range(len(lowerCamelCase_     )       ,self.offset - 1     )
    ]

    if len(set(lowerCamelCase_     )     ) != len(lowerCamelCase_     ):
     raise ValueError(
         """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
         f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."     )
    UpperCAmelCase__       :	Tuple        =       additional_special_tokens_extended
   else:
    UpperCAmelCase__       :	Dict        =       [mask_token_sent] if mask_token_sent is not None else []
    additional_special_tokens += [f"<unk_{i}>" for i in range(2       ,self.offset     )]

   super().__init__(
       lowerCamelCase_       ,tokenizer_file=lowerCamelCase_       ,pad_token=lowerCamelCase_       ,eos_token=lowerCamelCase_       ,unk_token=lowerCamelCase_       ,mask_token=lowerCamelCase_       ,mask_token_sent=lowerCamelCase_       ,offset=lowerCamelCase_       ,additional_special_tokens=lowerCamelCase_       ,**lowerCamelCase_       ,)
   UpperCAmelCase__       :	Optional[Any]        =       vocab_file
   UpperCAmelCase__       :	str        =       False if not self.vocab_file else True

  def  __lowercase							(	self		:      Any       ,A		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       set(self.all_special_ids     )  # call it once instead of inside list comp
   all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

   if all_special_ids != set(range(len(self.additional_special_tokens     ) + 3     )     ):
    raise ValueError(
        """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
        f" {len(self.additional_special_tokens     )} additional_special_tokens, but got {all_special_ids}"     )

   return [1 if x in all_special_ids else 0 for x in seq]

  def  __lowercase							(	self		:      Optional[Any]       ,A		:      List[Any]       ,A		:      Dict = None       ,A		:      Optional[int] = False     ):






   '''simple docstring'''
   if already_has_special_tokens:
    return self._special_token_mask(lowerCamelCase_     )
   elif token_ids_a is None:
    return self._special_token_mask(lowerCamelCase_     ) + [1]
   else:
    return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]

  def  __lowercase							(	self		:      Tuple       ,A		:      str       ,A		:      List[Any]=None     ):






   '''simple docstring'''
   if token_ids_a is None:
    return token_ids_a + [self.eos_token_id]
   # We don't expect to process pairs, but leave the pair logic for API consistency
   return token_ids_a + token_ids_a + [self.eos_token_id]




  def  __lowercase							(	self		:      Dict       ,A		:      Any       ,A		:      Dict = None     ):






   '''simple docstring'''
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
        """tokenizer."""     )

   if not os.path.isdir(lowerCamelCase_     ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
    return
   UpperCAmelCase__       :	List[str]        =       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_     ):
    copyfile(self.vocab_file       ,lowerCamelCase_     )

   return (out_vocab_file,)








"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase													={
    "configuration_informer": [
        "INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase													=[
      "INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "InformerForPrediction",
      "InformerModel",
      "InformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

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




