




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





def 							lowercase					()     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				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.""" )
				lowerCAmelCase  =				parser.parse_args()

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

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

				lowerCAmelCase  =				[]
				lowerCAmelCase  =				0
				lowerCAmelCase  =				10_000
				lowerCAmelCase  =				time.time()
				for text in data:
								lowerCAmelCase  =				f'''{bos} {text.strip()} {sep}'''
								lowerCAmelCase  =				tokenizer.encode(snake_case__      ,       add_special_tokens=snake_case__ )
								rslt.append(snake_case__ )

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

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





