





'''simple docstring'''






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_     =	logging.get_logger(__name__)


class   _lowercase						(							SCREAMING_SNAKE_CASE__				):
   _UpperCamelCase       		=   ["""input_ids""", """attention_mask"""]


   def __init__( self    ,   _UpperCAmelCase="</s>"    ,   _UpperCAmelCase="<unk>"    ,   _UpperCAmelCase="<pad>"    ,   _UpperCAmelCase=125    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase    ,   ):

       if extra_ids > 0 and additional_special_tokens is None:
           A       :       Dict							=						[f'''<extra_id_{i}>''' for i in range(_lowercase					)]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           A       :       Optional[Any]							=						len(set(filter(lambda _UpperCAmelCase					: bool('''extra_id''' in str(_lowercase					)					)    ,   _lowercase					)					)					)
           if extra_tokens != extra_ids:
               raise ValueError(
                   f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                   ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                   ''' extra_ids tokens'''					)

       A       :       List[Any]							=						AddedToken(_lowercase    ,   lstrip=_lowercase    ,   rstrip=_lowercase					) if isinstance(_lowercase    ,   _lowercase					) else pad_token
       A       :       Dict							=						AddedToken(_lowercase    ,   lstrip=_lowercase    ,   rstrip=_lowercase					) if isinstance(_lowercase    ,   _lowercase					) else eos_token
       A       :       str							=						AddedToken(_lowercase    ,   lstrip=_lowercase    ,   rstrip=_lowercase					) if isinstance(_lowercase    ,   _lowercase					) else unk_token

       super().__init__(
           eos_token=_lowercase    ,   unk_token=_lowercase    ,   pad_token=_lowercase    ,   extra_ids=_lowercase    ,   additional_special_tokens=_lowercase    ,   **_lowercase    ,   )

       A       :       List[str]							=						extra_ids

       A       :       Tuple							=						2**8  # utf is 8 bits

       # define special tokens dict
       A       :       Dict[int, str]							=						{
           self.pad_token: 0,
           self.eos_token: 1,
           self.unk_token: 2,
       }
       A       :       int							=						len(self.special_tokens_encoder					)
       A       :       Tuple							=						len(_lowercase					)
       for i, token in enumerate(_lowercase					):
           A       :       List[str]							=						self.vocab_size + i - n
       A       :       Dict[str, int]							=						{v: k for k, v in self.special_tokens_encoder.items()}


   @property
   def 					snake_case				( self					):
       return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = False					):

       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_lowercase    ,   token_ids_a=_lowercase    ,   already_has_special_tokens=_lowercase					)

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(_lowercase					)) + [1]
       return ([0] * len(_lowercase					)) + [1] + ([0] * len(_lowercase					)) + [1]


   def 					snake_case				( self    ,   _UpperCAmelCase					):

       if len(_lowercase					) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
               ''' eos tokens being added.'''					)
           return token_ids
       else:
           return token_ids + [self.eos_token_id]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Optional[int]							=						[self.eos_token_id]

       if token_ids_a is None:
           return len(token_ids_a + eos					) * [0]
       return len(token_ids_a + eos + token_ids_a + eos					) * [0]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       List[str]							=						self._add_eos_if_not_present(_lowercase					)
       if token_ids_a is None:
           return token_ids_a
       else:
           A       :       List[Any]							=						self._add_eos_if_not_present(_lowercase					)
           return token_ids_a + token_ids_a


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						[chr(_lowercase					) for i in text.encode('''utf-8'''					)]
       return tokens


   def 					snake_case				( self    ,   _UpperCAmelCase					):

       if token in self.special_tokens_encoder:
           A       :       Tuple							=						self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
           A       :       List[Any]							=						self.added_tokens_encoder[token]
       elif len(_lowercase					) != 1:
           A       :       List[Any]							=						self.unk_token_id
       else:
           A       :       List[Any]							=						ord(_lowercase					) + self._num_special_tokens
       return token_id


   def 					snake_case				( self    ,   _UpperCAmelCase					):

       if index in self.special_tokens_decoder:
           A       :       Dict							=						self.special_tokens_decoder[index]
       else:
           A       :       Any							=						chr(index - self._num_special_tokens					)
       return token


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       List[str]							=						b""""""
       for token in tokens:
           if token in self.special_tokens_decoder:
               A       :       List[Any]							=						self.special_tokens_decoder[token].encode('''utf-8'''					)
           elif token in self.added_tokens_decoder:
               A       :       Optional[Any]							=						self.special_tokens_decoder[token].encode('''utf-8'''					)
           elif token in self.special_tokens_encoder:
               A       :       Optional[Any]							=						token.encode('''utf-8'''					)
           elif token in self.added_tokens_encoder:
               A       :       Optional[int]							=						token.encode('''utf-8'''					)
           else:
               A       :       Optional[Any]							=						bytes([ord(_lowercase					)]					)
           bstring += tok_string
       A       :       List[Any]							=						bstring.decode('''utf-8'''    ,   errors='''ignore'''					)
       return string


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       return ()







'''simple docstring'''






from typing import TYPE_CHECKING

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


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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