
"""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       lowerCamelCase__		(			)							->		Any:


					"""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=__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.'''	)
					_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(__snake_case	)} examples to process.'''	)

					_UpperCamelCase							 =  []
					_UpperCamelCase							 =  0
					_UpperCamelCase							 =  1_00_00
					_UpperCamelCase							 =  time.time()
					for text in data:
										_UpperCamelCase							 =  F'''{bos} {text.strip()} {sep}'''
										_UpperCamelCase							 =  tokenizer.encode(__snake_case,     add_special_tokens=__snake_case	)
										rslt.append(__snake_case	)

										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(__snake_case	)} examples processed.'''	)

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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a														=		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """AutoformerForPrediction""",
				    """AutoformerModel""",
				    """AutoformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






