






"""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_   : List[str]   			= logging.getLogger(__name__)



def 					_A			() ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		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.'''							)
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		parser.parse_args()

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

    logger.info('''Start encoding'''							)
    logger.info(f'{len(__a							)} examples to process.'							)

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

        iter += 1
        if iter % interval == 0:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		time.time()
            logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		time.time()
    logger.info('''Finished binarization'''							)
    logger.info(f'{len(__a							)} examples processed.'							)

    SCREAMING_SNAKE_CASE_      :  Any    						=		f'{args.dump_file}.{args.tokenizer_name}.pickle'
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tokenizer.vocab_size
    if vocab_size < (1 << 16):
        SCREAMING_SNAKE_CASE_      :  Any    						=		[np.uintaa(__a							) for d in rslt]
    else:
        SCREAMING_SNAKE_CASE_      :  List[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()






'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:   Union[str, Any]							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




