


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


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

a_ :Optional[Any]  							=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_ :Tuple  							=			{
    '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'
        )
    },
}

a_ :Tuple  							=			{
    'facebook/blenderbot_small-90M': 5_12,
}

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			str      = VOCAB_FILES_NAMES
     lowerCamelCase			:			int      = PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase			:			int      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase			:			int      = BlenderbotSmallTokenizer







     def __init__(						self :						List[Any]   ,   _lowercase :						Optional[int]=None   ,   _lowercase :						Union[str, Any]=None   ,   _lowercase :						List[Any]="<|endoftext|>"   ,   _lowercase :						List[Any]="<|endoftext|>"   ,   _lowercase :						Any="<|endoftext|>"   ,   _lowercase :						int=False   ,   _lowercase :						Optional[int]=True   ,   **_lowercase :						List[Any]   ,   ):
           super().__init__(
               ByteLevelBPETokenizer(
                   vocab=_lowercase   ,   merges=_lowercase   ,   add_prefix_space=_lowercase   ,   trim_offsets=_lowercase   ,   )   ,   bos_token=_lowercase   ,   eos_token=_lowercase   ,   unk_token=_lowercase   ,   **_lowercase   ,   )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		add_prefix_space







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						Optional[Any]   ,   _lowercase :						Optional[int]=None      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[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]   ,   _lowercase :						List[int]   ,   _lowercase :						Optional[List[int]] = None      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		[self.sep_token_id]
           SCREAMING_SNAKE_CASE__       :  Dict						     =		[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]








"""simple docstring"""






from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MCTCTForCTC',
     'MCTCTModel',
     'MCTCTPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

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