





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_big_bird import BigBirdTokenizer
else:
      _UpperCAmelCase             =							None

_UpperCAmelCase             =							logging.get_logger(__name__)
_UpperCAmelCase             =							{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCAmelCase             =							{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCAmelCase             =							{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}


_UpperCAmelCase             =							'▁'

class        snake_case_    (		lowerCamelCase__						):
  A_      							=   VOCAB_FILES_NAMES
  A_      							=   PRETRAINED_VOCAB_FILES_MAP
  A_      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_      							=   BigBirdTokenizer
  A_      							=   ["""input_ids""", """attention_mask"""]
  A_      							=   []






  def __init__(							self			:					int       ,  _snake_case			:					int=None       ,  _snake_case			:					Union[str, Any]=None       ,  _snake_case			:					Tuple="<unk>"       ,  _snake_case			:					Any="<s>"       ,  _snake_case			:					Union[str, Any]="</s>"       ,  _snake_case			:					Any="<pad>"       ,  _snake_case			:					Optional[Any]="[SEP]"       ,  _snake_case			:					Tuple="[MASK]"       ,  _snake_case			:					Any="[CLS]"       ,  **_snake_case			:					Optional[int]       ,  )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else bos_token
   __lowerCAmelCase					:	Optional[Any]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else eos_token
   __lowerCAmelCase					:	List[str]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else unk_token
   __lowerCAmelCase					:	Optional[int]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else pad_token
   __lowerCAmelCase					:	List[Any]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else cls_token
   __lowerCAmelCase					:	Optional[Any]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else sep_token

   # Mask token behave like a normal word, i.e. include the space before it
   __lowerCAmelCase					:	List[Any]      =       AddedToken(snake_case__       ,  lstrip=snake_case__       ,  rstrip=snake_case__      ) if isinstance(snake_case__       ,  snake_case__      ) else mask_token

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

   __lowerCAmelCase					:	Optional[Any]      =       vocab_file
   __lowerCAmelCase					:	List[str]      =       False if not self.vocab_file else True






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Any       ,  _snake_case			:					Union[str, Any] = None      )->List[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       [self.sep_token_id]
   __lowerCAmelCase					:	Optional[int]      =       [self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Optional[Any] = None       ,  _snake_case			:					Dict = False      )->List[int]:






   '''simple docstring'''

   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         """You should not supply a second sequence if the provided sequence of """
         """ids is already formatted with special tokens for the model."""      )
    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

   if token_ids_a is None:
    return [1] + ([0] * len(snake_case__      )) + [1]
   return [1] + ([0] * len(snake_case__      )) + [1] + ([0] * len(snake_case__      )) + [1]






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					Tuple       ,  _snake_case			:					Any = None      )->List[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       [self.sep_token_id]
   __lowerCAmelCase					:	Optional[int]      =       [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      ) * [0] + len(token_ids_a + sep      ) * [1]






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					List[str]       ,  _snake_case			:					Tuple = None      )->Tuple[str]:






   '''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(snake_case__      ):
    logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
    return
   __lowerCAmelCase					:	int      =       os.path.join(
       snake_case__       ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

   return (out_vocab_file,)






from typing import TYPE_CHECKING

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


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

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