

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
)
lowerCAmelCase_       	=	logging.getLogger(__name__)


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

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

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

						snake_case_   =	[]
						snake_case_   =	0
						snake_case_   =	10000
						snake_case_   =	time.time()
						for text in data:
												snake_case_   =	F'''{bos} {text.strip()} {sep}'''
												snake_case_   =	tokenizer.encode(SCREAMING_SNAKE_CASE__  ,		add_special_tokens=SCREAMING_SNAKE_CASE__       )
												rslt.append(SCREAMING_SNAKE_CASE__       )

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

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


if __name__ == "__main__":
					main()


from typing import TYPE_CHECKING

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


lowerCAmelCase_       	=	{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''XGLMTokenizer''']

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''XGLMForCausalLM''',
					    '''XGLMModel''',
					    '''XGLMPreTrainedModel''',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxXGLMForCausalLM''',
					    '''FlaxXGLMModel''',
					    '''FlaxXGLMPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFXGLMForCausalLM''',
					    '''TFXGLMModel''',
					    '''TFXGLMPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm import XGLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xglm_fast import XGLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xglm import (
										    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXGLMForCausalLM,
										    TFXGLMModel,
										    TFXGLMPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)