



"""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 	UpperCAmelCase     (		):

		'''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=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							 =		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(A       )} examples to process.'       )

		_UpperCAmelCase							 =		[]
		_UpperCAmelCase							 =		0
		_UpperCAmelCase							 =		1_0000
		_UpperCAmelCase							 =		time.time()
		for text in data:
				_UpperCAmelCase							 =		f'{bos} {text.strip()} {sep}'
				_UpperCAmelCase							 =		tokenizer.encode(A     ,				add_special_tokens=A       )
				rslt.append(A       )

				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(A       )} examples processed.'       )

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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  							=					{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''RobertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''RobertaForCausalLM''',
					    '''RobertaForMaskedLM''',
					    '''RobertaForMultipleChoice''',
					    '''RobertaForQuestionAnswering''',
					    '''RobertaForSequenceClassification''',
					    '''RobertaForTokenClassification''',
					    '''RobertaModel''',
					    '''RobertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFRobertaForCausalLM''',
					    '''TFRobertaForMaskedLM''',
					    '''TFRobertaForMultipleChoice''',
					    '''TFRobertaForQuestionAnswering''',
					    '''TFRobertaForSequenceClassification''',
					    '''TFRobertaForTokenClassification''',
					    '''TFRobertaMainLayer''',
					    '''TFRobertaModel''',
					    '''TFRobertaPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''FlaxRobertaForCausalLM''',
					    '''FlaxRobertaForMaskedLM''',
					    '''FlaxRobertaForMultipleChoice''',
					    '''FlaxRobertaForQuestionAnswering''',
					    '''FlaxRobertaForSequenceClassification''',
					    '''FlaxRobertaForTokenClassification''',
					    '''FlaxRobertaModel''',
					    '''FlaxRobertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
					from .tokenization_roberta import RobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roberta_fast import RobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta import (
										    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaForCausalLM,
										    RobertaForMaskedLM,
										    RobertaForMultipleChoice,
										    RobertaForQuestionAnswering,
										    RobertaForSequenceClassification,
										    RobertaForTokenClassification,
										    RobertaModel,
										    RobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta import (
										    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaForCausalLM,
										    TFRobertaForMaskedLM,
										    TFRobertaForMultipleChoice,
										    TFRobertaForQuestionAnswering,
										    TFRobertaForSequenceClassification,
										    TFRobertaForTokenClassification,
										    TFRobertaMainLayer,
										    TFRobertaModel,
										    TFRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta import (
										    FlaxRobertaForCausalLM,
										    FlaxRobertaForMaskedLM,
										    FlaxRobertaForMultipleChoice,
										    FlaxRobertaForQuestionAnswering,
										    FlaxRobertaForSequenceClassification,
										    FlaxRobertaForTokenClassification,
										    FlaxRobertaModel,
										    FlaxRobertaPreTrainedModel,
										)

else:
					import sys

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


