
'''simple docstring'''



from ...configuration_utils import PretrainedConfig


_lowercase          =						{
    '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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: str										=  '''tapas'''




						def __init__(   self       ,  _lowercase=30_522       ,  _lowercase=768       ,  _lowercase=12       ,  _lowercase=12       ,  _lowercase=3_072       ,  _lowercase="gelu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=1_024       ,  _lowercase=[3, 256, 256, 2, 256, 256, 10]       ,  _lowercase=0.02       ,  _lowercase=1e-12       ,  _lowercase=0       ,  _lowercase=10.0       ,  _lowercase=0       ,  _lowercase=1.0       ,  _lowercase=None       ,  _lowercase=1.0       ,  _lowercase=False       ,  _lowercase=None       ,  _lowercase=1.0       ,  _lowercase=1.0       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase="ratio"       ,  _lowercase=None       ,  _lowercase=None       ,  _lowercase=64       ,  _lowercase=32       ,  _lowercase=False       ,  _lowercase=True       ,  _lowercase=False       ,  _lowercase=False       ,  _lowercase=True       ,  _lowercase=False       ,  _lowercase=None       ,  _lowercase=None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(pad_token_id=lowerCamelCase_       ,  **lowerCamelCase_ )

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

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

												# Aggregation hyperparameters
												_lowerCAmelCase    		=					aggregation_labels
												_lowerCAmelCase    		=					no_aggregation_label_index

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

'''simple docstring'''

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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__)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[Any] 					=	argparse.ArgumentParser(
       description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''     )
   parser.add_argument('''--file_path'''	,      type=_A	,      default='''data/dump.txt'''	,      help='''The path to the data.'''     )
   parser.add_argument('''--tokenizer_type'''	,      type=_A	,      default='''bert'''	,      choices=['''bert''', '''roberta''', '''gpt2''']     )
   parser.add_argument('''--tokenizer_name'''	,      type=_A	,      default='''bert-base-uncased'''	,      help='''The tokenizer to use.'''     )
   parser.add_argument('''--dump_file'''	,      type=_A	,      default='''data/dump'''	,      help='''The dump file prefix.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	parser.parse_args()

   logger.info(F'''Loading Tokenizer ({args.tokenizer_name})'''     )
   if args.tokenizer_type == "bert":
      UpperCAmelCase__				:					List[str] 					=	BertTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
      UpperCAmelCase__				:					Union[str, Any] 					=	tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
   elif args.tokenizer_type == "roberta":
      UpperCAmelCase__				:					Union[str, Any] 					=	RobertaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''cls_token''']  # `<s>`
      UpperCAmelCase__				:					Dict 					=	tokenizer.special_tokens_map['''sep_token''']  # `</s>`
   elif args.tokenizer_type == "gpt2":
      UpperCAmelCase__				:					List[str] 					=	GPTaTokenizer.from_pretrained(args.tokenizer_name     )
      UpperCAmelCase__				:					List[Any] 					=	tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
      UpperCAmelCase__				:					Optional[Any] 					=	tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

   logger.info(F'''Loading text from {args.file_path}'''     )
   with open(args.file_path	,      '''r'''	,      encoding='''utf8'''     ) as fp:
      UpperCAmelCase__				:					List[Any] 					=	fp.readlines()

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	time.time()
         logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''     )
         UpperCAmelCase__				:					Optional[Any] 					=	time.time()
   logger.info('''Finished binarization'''     )
   logger.info(F'''{len(_A     )} examples processed.'''     )

   UpperCAmelCase__				:					Dict 					=	F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
   UpperCAmelCase__				:					Dict 					=	tokenizer.vocab_size
   if vocab_size < (1 << 16):
      UpperCAmelCase__				:					Any 					=	[np.uintaa(_A     ) for d in rslt]
   else:
      UpperCAmelCase__				:					str 					=	[np.intaa(_A     ) for d in rslt]
   random.shuffle(rslt_     )
   logger.info(F'''Dump to {dp_file}'''     )
   with open(_A	,      '''wb'''     ) as handle:
      pickle.dump(rslt_	,      _A	,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
  main()




