

'''simple docstring'''

from ...configuration_utils import PretrainedConfig


A_       						=							{
    '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 							_snake_case (       lowerCAmelCase__						):
			_A       :     Optional[int]       =					"tapas"
			def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Tuple=30_522     ,SCREAMING_SNAKE_CASE__			:      Dict=768     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=12     ,SCREAMING_SNAKE_CASE__			:      Tuple=12     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=3_072     ,SCREAMING_SNAKE_CASE__			:      Optional[int]="gelu"     ,SCREAMING_SNAKE_CASE__			:      List[Any]=0.1     ,SCREAMING_SNAKE_CASE__			:      Tuple=0.1     ,SCREAMING_SNAKE_CASE__			:      Tuple=1_024     ,SCREAMING_SNAKE_CASE__			:      List[Any]=[3, 256, 256, 2, 256, 256, 10]     ,SCREAMING_SNAKE_CASE__			:      List[str]=0.02     ,SCREAMING_SNAKE_CASE__			:      int=1e-12     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=0     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=10.0     ,SCREAMING_SNAKE_CASE__			:      List[str]=0     ,SCREAMING_SNAKE_CASE__			:      str=1.0     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      Dict=1.0     ,SCREAMING_SNAKE_CASE__			:      str=False     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,SCREAMING_SNAKE_CASE__			:      List[Any]=1.0     ,SCREAMING_SNAKE_CASE__			:      Any=1.0     ,SCREAMING_SNAKE_CASE__			:      str=False     ,SCREAMING_SNAKE_CASE__			:      Any=False     ,SCREAMING_SNAKE_CASE__			:      Tuple="ratio"     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None     ,SCREAMING_SNAKE_CASE__			:      str=64     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=32     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=False     ,SCREAMING_SNAKE_CASE__			:      Tuple=True     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=False     ,SCREAMING_SNAKE_CASE__			:      Tuple=False     ,SCREAMING_SNAKE_CASE__			:      Any=True     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=False     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
					super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,**_SCREAMING_SNAKE_CASE							)

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

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

					# Aggregation hyperparameters
					SCREAMING_SNAKE_CASE:Optional[int]						      =					aggregation_labels
					SCREAMING_SNAKE_CASE:Any						      =					no_aggregation_label_index

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









'''simple docstring'''

import random





def    A_  ( snake_case      ,       snake_case      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:dict						      =					{i: [] for i in range(snake_case )}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(snake_case )
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(snake_case ):
    for j in range(i + 1      ,       snake_case ):
      if random.random() < probability:
        graph[i].append(snake_case )
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(snake_case )
  return graph





def    A_  ( snake_case ):
  return {
      i: [j for j in range(snake_case ) if i != j] for i in range(snake_case )
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()






