

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
)
SCREAMING_SNAKE_CASE_          =   logging.getLogger(__name__)

def 				__SCREAMING_SNAKE_CASE				(					) ->							Dict:
				_UpperCAmelCase							:		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							:		Union[str, Any]					=     parser.parse_args()

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

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

				_UpperCAmelCase							:		Union[str, Any]					=     []
				_UpperCAmelCase							:		str					=     0
				_UpperCAmelCase							:		str					=     1_0000
				_UpperCAmelCase							:		Dict					=     time.time()
				for text in data:
								_UpperCAmelCase							:		int					=     F'{bos} {text.strip()} {sep}'
								_UpperCAmelCase							:		Optional[Any]					=     tokenizer.encode(__a	,   add_special_tokens=__a			)
								rslt.append(__a			)

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

				_UpperCAmelCase							:		Any					=     F'{args.dump_file}.{args.tokenizer_name}.pickle'
				_UpperCAmelCase							:		List[str]					=     tokenizer.vocab_size
				if vocab_size < (1 << 16):
								_UpperCAmelCase							:		List[str]					=     [np.uintaa(__a			) for d in rslt]
				else:
								_UpperCAmelCase							:		List[Any]					=     [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 ..utils import DummyObject, requires_backends





class 			snake_case  (				metaclass=__lowercase ):
							UpperCAmelCase__      =   ['''note_seq''']

							def __init__(self							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(self							,							['''note_seq''']     )

							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(cls							,							['''note_seq''']     )







							@classmethod
							def _lowercase       (cls							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									requires_backends(cls							,							['''note_seq''']     )