





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
)
snake_case_				=			logging.getLogger(__name__)






def lowerCamelCase__    (							)      ->				List[str]:
   __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							       =   1_0000
   __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)