



"""simple docstring"""






from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase__        =				logging.get_logger(__name__)

lowerCAmelCase__        =				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase__        =				{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase__        =				{
    '''facebook/blenderbot_small-90M''': 512,
}






class        __snake_case       (	_lowercase):
  snake_case__				:   List[str]  			=						VOCAB_FILES_NAMES
  snake_case__				:   int  			=						PRETRAINED_VOCAB_FILES_MAP
  snake_case__				:   List[Any]  			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case__				:   List[Any]  			=						BlenderbotSmallTokenizer




  def __init__( self     :						List[str]						,					__lowerCAmelCase     :						Optional[int]=None						,					__lowerCAmelCase     :						Any=None						,					__lowerCAmelCase     :						str="<|endoftext|>"						,					__lowerCAmelCase     :						List[Any]="<|endoftext|>"						,					__lowerCAmelCase     :						Optional[Any]="<|endoftext|>"						,					__lowerCAmelCase     :						Tuple=False						,					__lowerCAmelCase     :						str=True						,					**__lowerCAmelCase     :						List[str]						,					):






         """simple docstring"""






         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=__lowerCAmelCase						,					merges=__lowerCAmelCase						,					add_prefix_space=__lowerCAmelCase						,					trim_offsets=__lowerCAmelCase						,					)						,					bos_token=__lowerCAmelCase						,					eos_token=__lowerCAmelCase						,					unk_token=__lowerCAmelCase						,					**__lowerCAmelCase						,					)
         _lowerCamelCase						:				List[Any]    			= add_prefix_space




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					__lowerCAmelCase     :						Any						,					__lowerCAmelCase     :						List[str]=None		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, 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 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						List[int]						,					__lowerCAmelCase     :						Optional[List[int]] = None		):






         """simple docstring"""






         _lowerCamelCase						:				str    			= [self.sep_token_id]
         _lowerCamelCase						:				List[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_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





