



"""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
)
lowercase__       =	logging.getLogger(__name__)





def    __magic_name__     ( ):
							__a		:		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=snake_case__	,							default="""data/dump.txt"""	,							help="""The path to the data."""       )
							parser.add_argument("""--tokenizer_type"""	,							type=snake_case__	,							default="""bert"""	,							choices=["""bert""", """roberta""", """gpt2"""]       )
							parser.add_argument("""--tokenizer_name"""	,							type=snake_case__	,							default="""bert-base-uncased"""	,							help="""The tokenizer to use."""       )
							parser.add_argument("""--dump_file"""	,							type=snake_case__	,							default="""data/dump"""	,							help="""The dump file prefix."""       )
							__a		:		List[str]						=							parser.parse_args()

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

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

							__a		:		List[Any]						=							[]
							__a		:		List[Any]						=							0
							__a		:		Any						=							1_0_0_0_0
							__a		:		int						=							time.time()
							for text in data:
														__a		:		int						=							F'''{bos} {text.strip()} {sep}'''
														__a		:		Tuple						=							tokenizer.encode(snake_case__	,							add_special_tokens=snake_case__       )
														rslt.append(snake_case__       )

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

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


if __name__ == "__main__":
	main()









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate




@dataclass(frozen=lowercase_)
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			field(default="audio-classification"						,     metadata={"include_in_asdict_even_if_is_default": True})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"audio": Audio()})
       lowerCamelCase				:					ClassVar[Features]								=			Features({"labels": ClassLabel})
       lowerCamelCase				:					str								=			"audio"
       lowerCamelCase				:					str								=			"labels"



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if self.label_column not in features:
                 raise ValueError(F'''Column {self.label_column} is not present in features.'''     )
            if not isinstance(features[self.label_column]      ,    a__     ):
                 raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''     )
            __snake_case      :Any 							=    copy.deepcopy(self     )
            __snake_case      :List[Any] 							=    self.label_schema.copy()
            __snake_case      :List[Any] 							=    features[self.label_column]
            __snake_case      :Optional[Any] 							=    label_schema
            return task_template



       @property
       def        __lowercase    (				self     )			->   Dict[str, str]:






            '''simple docstring'''





            return {
                self.audio_column: "audio",
                self.label_column: "labels",
            }


