


from ...configuration_utils import PretrainedConfig


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

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

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

             # Aggregation hyperparameters
             a_ :		str							=  aggregation_labels
             a_ :		Dict							=  no_aggregation_label_index

             if isinstance(self.aggregation_labels					,							SCREAMING_SNAKE_CASE__					):
                    a_ :		List[Any]							=  {int(SCREAMING_SNAKE_CASE__					): v for k, v in aggregation_labels.items()}






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


