


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


def 		lowercase_	(  ):


			'''simple docstring'''







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

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

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

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

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

			__lowercase			       = F'{args.dump_file}.{args.tokenizer_name}.pickle'
			__lowercase			       = tokenizer.vocab_size
			if vocab_size < (1 << 16):
						__lowercase			       = [np.uintaa(_UpperCamelCase	) for d in rslt]
			else:
						__lowercase			       = [np.intaa(_UpperCamelCase	) for d in rslt]
			random.shuffle(rslt_	)
			logger.info(F'Dump to {dp_file}'	)
			with open(_UpperCamelCase							,      '''wb'''	) as handle:
						pickle.dump(rslt_							,      _UpperCamelCase							,      protocol=pickle.HIGHEST_PROTOCOL	)


if __name__ == "__main__":
	main()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

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


