

"""simple docstring"""




from ...configuration_utils import PretrainedConfig


__UpperCAmelCase			=					{
    '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   __lowercase      (     lowercase_       ):
  snake_case_												=							"tapas"

  def __init__(	self		:      str       ,A		:      Union[str, Any]=30_522       ,A		:      Dict=768       ,A		:      Union[str, Any]=12       ,A		:      int=12       ,A		:      Tuple=3_072       ,A		:      str="gelu"       ,A		:      Optional[int]=0.1       ,A		:      int=0.1       ,A		:      Optional[int]=1_024       ,A		:      Optional[Any]=[3, 256, 256, 2, 256, 256, 10]       ,A		:      Dict=0.0_2       ,A		:      Any=1e-12       ,A		:      List[Any]=0       ,A		:      Union[str, Any]=1_0.0       ,A		:      Dict=0       ,A		:      List[str]=1.0       ,A		:      Any=None       ,A		:      int=1.0       ,A		:      Any=False       ,A		:      Optional[Any]=None       ,A		:      List[Any]=1.0       ,A		:      List[str]=1.0       ,A		:      Optional[Any]=False       ,A		:      Optional[int]=False       ,A		:      Optional[int]="ratio"       ,A		:      Optional[Any]=None       ,A		:      Any=None       ,A		:      Union[str, Any]=64       ,A		:      int=32       ,A		:      Tuple=False       ,A		:      Optional[int]=True       ,A		:      Dict=False       ,A		:      Union[str, Any]=False       ,A		:      str=True       ,A		:      str=False       ,A		:      Union[str, Any]=None       ,A		:      int=None       ,**A		:      Union[str, Any]       ,):






   '''simple docstring'''
   super().__init__(pad_token_id=a__       ,**a__     )

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

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

   # Aggregation hyperparameters
   UpperCAmelCase__       :	List[str]        =       aggregation_labels
   UpperCAmelCase__       :	int        =       no_aggregation_label_index

   if isinstance(self.aggregation_labels       ,a__     ):
    UpperCAmelCase__       :	Any        =       {int(a__     ): v for k, v in aggregation_labels.items()}













import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






     __snake_case      :str 							=    {"""num examples""": SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __snake_case      :Dict 							=    datasets.Features({"""text""": datasets.Value("""string"""	), """numbers""": datasets.Value("""float32"""	)}	)
          __snake_case      :Optional[int] 							=    generate_example_dataset(
              os.path.join(snake_case__						,"""dataset.arrow"""	)						,snake_case__						,num_examples=snake_case__	)

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

     with open(snake_case__						,"""wb"""	) as f:
          f.write(json.dumps(snake_case__	).encode("""utf-8"""	)	)


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()


