


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
)
lowerCAmelCase__    :Optional[Any]       =				logging.getLogger(__name__)

def 			lowerCAmelCase__					(     )    ->						List[str]:


							'''simple docstring'''





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

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

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

							_UpperCAmelCase											=     []
							_UpperCAmelCase											=     0
							_UpperCAmelCase											=     1_0_0_0_0
							_UpperCAmelCase											=     time.time()
							for text in data:
														_UpperCAmelCase											=     F'''{bos} {text.strip()} {sep}'''
														_UpperCAmelCase											=     tokenizer.encode(lowercase__     ,    add_special_tokens=lowercase__			)
														rslt.append(lowercase__			)

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

							_UpperCAmelCase											=     F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
							_UpperCAmelCase											=     tokenizer.vocab_size
							if vocab_size < (1 << 1_6):
														_UpperCAmelCase											=     [np.uintaa(lowercase__			) for d in rslt]
							else:
														_UpperCAmelCase											=     [np.intaa(lowercase__			) for d in rslt]
							random.shuffle(rslt_			)
							logger.info(F'''Dump to {dp_file}'''			)
							with open(lowercase__     ,    'wb'			) as handle:
														pickle.dump(rslt_     ,    lowercase__     ,    protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
		main()



"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'dpr'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=2			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case = 0			,					**snake_case			,					):





        """simple docstring"""

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

        lowerCAmelCase__   :				Dict 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				num_hidden_layers
        lowerCAmelCase__   :				List[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Any 			=				hidden_act
        lowerCAmelCase__   :				Any 			=				intermediate_size
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[str] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				max_position_embeddings
        lowerCAmelCase__   :				Optional[Any] 			=				type_vocab_size
        lowerCAmelCase__   :				Tuple 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps
        lowerCAmelCase__   :				Dict 			=				projection_dim
        lowerCAmelCase__   :				Any 			=				position_embedding_type






