






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:
      SCREAMING_SNAKE_CASE__		:							List[Any] 				=				None

SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__		:							str 				=				{
    """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"""
        ),
    },
}

SCREAMING_SNAKE_CASE__		:							List[Any] 				=				{
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}


SCREAMING_SNAKE_CASE__		:							str 				=				"""▁"""







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  VOCAB_FILES_NAMES
     __lowerCamelCase      				=  PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase      				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase      				=  BigBirdTokenizer
     __lowerCamelCase      				=  ['input_ids', 'attention_mask']
     __lowerCamelCase      				=  []




     def __init__(				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None	,						_lowerCAmelCase="<unk>"	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="<pad>"	,						_lowerCAmelCase="[SEP]"	,						_lowerCAmelCase="[MASK]"	,						_lowerCAmelCase="[CLS]"	,						**_lowerCAmelCase	,						):
          UpperCAmelCase__      :  int				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else bos_token
          UpperCAmelCase__      :  Any				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else eos_token
          UpperCAmelCase__      :  str				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else unk_token
          UpperCAmelCase__      :  List[Any]				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else pad_token
          UpperCAmelCase__      :  Dict				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else cls_token
          UpperCAmelCase__      :  Union[str, Any]				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else sep_token

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

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

          UpperCAmelCase__      :  List[str]				      =			vocab_file
          UpperCAmelCase__      :  Union[str, Any]				      =			False if not self.vocab_file else True




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  List[str]				      =			[self.sep_token_id]
          UpperCAmelCase__      :  str				      =			[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	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = False		):

          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(_lowerCAmelCase		)) + [1]
          return [1] + ([0] * len(_lowerCAmelCase		)) + [1] + ([0] * len(_lowerCAmelCase		)) + [1]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  Any				      =			[self.sep_token_id]
          UpperCAmelCase__      :  Union[str, 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		) * [0] + len(token_ids_a + sep		) * [1]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          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(_lowerCAmelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          UpperCAmelCase__      :  Optional[int]				      =			os.path.join(
              _lowerCAmelCase	,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

          return (out_vocab_file,)













'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}