




from ...configuration_utils import PretrainedConfig


__lowerCAmelCase	:       Dict     =	{
    '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__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_											=      """tapas"""





				def __init__(   self       :  int      ,   __lowerCamelCase       :  Optional[Any]=3_05_22      ,   __lowerCamelCase       :  Optional[Any]=7_68      ,   __lowerCamelCase       :  Union[str, Any]=12      ,   __lowerCamelCase       :  Tuple=12      ,   __lowerCamelCase       :  List[str]=30_72      ,   __lowerCamelCase       :  Any="gelu"      ,   __lowerCamelCase       :  str=0.1      ,   __lowerCamelCase       :  Union[str, Any]=0.1      ,   __lowerCamelCase       :  Optional[Any]=10_24      ,   __lowerCamelCase       :  Any=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]      ,   __lowerCamelCase       :  List[Any]=0.02      ,   __lowerCamelCase       :  Dict=1e-12      ,   __lowerCamelCase       :  List[str]=0      ,   __lowerCamelCase       :  Dict=10.0      ,   __lowerCamelCase       :  List[str]=0      ,   __lowerCamelCase       :  int=1.0      ,   __lowerCamelCase       :  List[Any]=None      ,   __lowerCamelCase       :  Optional[Any]=1.0      ,   __lowerCamelCase       :  Optional[Any]=False      ,   __lowerCamelCase       :  List[Any]=None      ,   __lowerCamelCase       :  List[str]=1.0      ,   __lowerCamelCase       :  List[str]=1.0      ,   __lowerCamelCase       :  List[Any]=False      ,   __lowerCamelCase       :  List[Any]=False      ,   __lowerCamelCase       :  Union[str, Any]="ratio"      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  List[str]=None      ,   __lowerCamelCase       :  Dict=64      ,   __lowerCamelCase       :  Any=32      ,   __lowerCamelCase       :  List[Any]=False      ,   __lowerCamelCase       :  str=True      ,   __lowerCamelCase       :  Dict=False      ,   __lowerCamelCase       :  Optional[int]=False      ,   __lowerCamelCase       :  int=True      ,   __lowerCamelCase       :  List[str]=False      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Any=None      ,   **__lowerCamelCase       :  Optional[Any]      ,   )      ->  Any:
							super().__init__(pad_token_id=__lowerCamelCase      ,   **__lowerCamelCase							)

							# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
							a					=    vocab_size
							a					=    hidden_size
							a					=    num_hidden_layers
							a					=    num_attention_heads
							a					=    hidden_act
							a					=    intermediate_size
							a					=    hidden_dropout_prob
							a					=    attention_probs_dropout_prob
							a					=    max_position_embeddings
							a					=    type_vocab_sizes
							a					=    initializer_range
							a					=    layer_norm_eps

							# Fine-tuning task hyperparameters
							a					=    positive_label_weight
							a					=    num_aggregation_labels
							a					=    aggregation_loss_weight
							a					=    use_answer_as_supervision
							a					=    answer_loss_importance
							a					=    use_normalized_answer_loss
							a					=    huber_loss_delta
							a					=    temperature
							a					=    aggregation_temperature
							a					=    use_gumbel_for_cells
							a					=    use_gumbel_for_aggregation
							a					=    average_approximation_function
							a					=    cell_selection_preference
							a					=    answer_loss_cutoff
							a					=    max_num_rows
							a					=    max_num_columns
							a					=    average_logits_per_cell
							a					=    select_one_column
							a					=    allow_empty_column_selection
							a					=    init_cell_selection_weights_to_zero
							a					=    reset_position_index_per_cell
							a					=    disable_per_token_loss

							# Aggregation hyperparameters
							a					=    aggregation_labels
							a					=    no_aggregation_label_index

							if isinstance(self.aggregation_labels      ,   __lowerCamelCase							):
										a					=    {int(__lowerCamelCase							): v for k, v in aggregation_labels.items()}












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






