

"""simple docstring"""





from ...configuration_utils import PretrainedConfig


__UpperCamelCase  :      Optional[int]							      =		{
    """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					(		UpperCAmelCase__       ):






					"""simple docstring"""



					lowercase__				 =       "tapas"







					def __init__(       self			:				List[str]   ,lowercase_			:				Tuple=3_0_5_2_2   ,lowercase_			:				str=7_6_8   ,lowercase_			:				List[str]=1_2   ,lowercase_			:				str=1_2   ,lowercase_			:				str=3_0_7_2   ,lowercase_			:				str="gelu"   ,lowercase_			:				Optional[int]=0.1   ,lowercase_			:				Union[str, Any]=0.1   ,lowercase_			:				Tuple=1_0_2_4   ,lowercase_			:				Union[str, Any]=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0]   ,lowercase_			:				Union[str, Any]=0.02   ,lowercase_			:				List[Any]=1E-12   ,lowercase_			:				Dict=0   ,lowercase_			:				Tuple=10.0   ,lowercase_			:				Optional[int]=0   ,lowercase_			:				Optional[int]=1.0   ,lowercase_			:				List[str]=None   ,lowercase_			:				Optional[Any]=1.0   ,lowercase_			:				int=False   ,lowercase_			:				Any=None   ,lowercase_			:				List[Any]=1.0   ,lowercase_			:				List[Any]=1.0   ,lowercase_			:				Any=False   ,lowercase_			:				Optional[int]=False   ,lowercase_			:				Dict="ratio"   ,lowercase_			:				Tuple=None   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				List[str]=6_4   ,lowercase_			:				Tuple=3_2   ,lowercase_			:				Optional[int]=False   ,lowercase_			:				int=True   ,lowercase_			:				Any=False   ,lowercase_			:				Optional[int]=False   ,lowercase_			:				str=True   ,lowercase_			:				Optional[Any]=False   ,lowercase_			:				str=None   ,lowercase_			:				str=None   ,**lowercase_			:				List[str]   ,):
						super().__init__(pad_token_id=lowercase_   ,**lowercase_		)

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

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

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

						if isinstance(self.aggregation_labels   ,lowercase_		):
							lowerCAmelCase__	:						Dict  				=	{int(lowercase_		): v for k, v in aggregation_labels.items()}












"""simple docstring"""






import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_   : Dict   			= logging.getLogger(__name__)

if __name__ == "__main__":
  UpperCAmelCase_   : List[str]   			= argparse.ArgumentParser(
      description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
  )
  parser.add_argument(
      """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
  )
  parser.add_argument(
      """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
  )
  parser.add_argument("""--vocab_size""", default=30522, type=int)
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()

  logger.info(f'''Loading data from {args.data_file}''')
  with open(args.data_file, """rb""") as fp:
    UpperCAmelCase_   : Union[str, Any]   			= pickle.load(fp)

  logger.info("""Counting occurrences for MLM.""")
  UpperCAmelCase_   : Any   			= Counter()
  for tk_ids in data:
    counter.update(tk_ids)
  UpperCAmelCase_   : List[Any]   			= [0] * args.vocab_size
  for k, v in counter.items():
    UpperCAmelCase_   : Dict   			= v

  logger.info(f'''Dump to {args.token_counts_dump}''')
  with open(args.token_counts_dump, """wb""") as handle:
    pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


