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

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


if is_sentencepiece_available():
   from .tokenization_barthez import BarthezTokenizer
else:
   __lowerCamelCase    :				Optional[Any]							     =	None

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

__lowerCamelCase    :				Dict							     =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase    :				List[str]							     =	{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCamelCase    :				Optional[Any]							     =	{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__lowerCamelCase    :				int							     =	'''▁'''







class __snake_case       (		__snake_case	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							BarthezTokenizer




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       Tuple=None ,			_lowercase    :       List[Any]=None ,			_lowercase    :       Union[str, Any]="<s>" ,			_lowercase    :       Optional[int]="</s>" ,			_lowercase    :       Tuple="</s>" ,			_lowercase    :       Any="<s>" ,			_lowercase    :       str="<unk>" ,			_lowercase    :       List[str]="<pad>" ,			_lowercase    :       List[str]="<mask>" ,			**_lowercase    :       List[str] ,			):



         """simple docstring"""






         SCREAMING_SNAKE_CASE__    							=   AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase ,			__UpperCamelCase   ) else mask_token

         super().__init__(
             __UpperCamelCase ,			tokenizer_file=__UpperCamelCase ,			bos_token=__UpperCamelCase ,			eos_token=__UpperCamelCase ,			unk_token=__UpperCamelCase ,			sep_token=__UpperCamelCase ,			cls_token=__UpperCamelCase ,			pad_token=__UpperCamelCase ,			mask_token=__UpperCamelCase ,			**__UpperCamelCase ,			)

         SCREAMING_SNAKE_CASE__    							=   vocab_file
         SCREAMING_SNAKE_CASE__    							=   False if not self.vocab_file else True




   def 			__a		(					self    :       Dict ,			_lowercase    :       str ,			_lowercase    :       List[Any] = None   ):



         """simple docstring"""






         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         SCREAMING_SNAKE_CASE__    							=   [self.cls_token_id]
         SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       Any ,			_lowercase    :       str = None   ):



         """simple docstring"""






         SCREAMING_SNAKE_CASE__    							=   [self.sep_token_id]
         SCREAMING_SNAKE_CASE__    							=   [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]




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Tuple ,			_lowercase    :       int = 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(__UpperCamelCase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
         SCREAMING_SNAKE_CASE__    							=   os.path.join(
             __UpperCamelCase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(__UpperCamelCase   ):
               copyfile(self.vocab_file ,			__UpperCamelCase   )

         return (out_vocab_file,)

from typing import TYPE_CHECKING

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


__lowerCamelCase    :				List[Any]							     =	{
    '''configuration_informer''': [
        '''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase    :				Union[str, Any]							     =	[
       '''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

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