


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__					(  ):
 __lowerCamelCase		:							Optional[Any]		 =						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.'						)
 __lowerCamelCase		:							Dict		 =						parser.parse_args()

 logger.info(f'Loading Tokenizer ({args.tokenizer_name})'						)
 if args.tokenizer_type == "bert":
  __lowerCamelCase		:							str		 =						BertTokenizer.from_pretrained(args.tokenizer_name						)
  __lowerCamelCase		:							Optional[Any]		 =						tokenizer.special_tokens_map['cls_token']  # `[CLS]`
  __lowerCamelCase		:							Union[str, Any]		 =						tokenizer.special_tokens_map['sep_token']  # `[SEP]`
 elif args.tokenizer_type == "roberta":
  __lowerCamelCase		:							int		 =						RobertaTokenizer.from_pretrained(args.tokenizer_name						)
  __lowerCamelCase		:							Tuple		 =						tokenizer.special_tokens_map['cls_token']  # `<s>`
  __lowerCamelCase		:							List[Any]		 =						tokenizer.special_tokens_map['sep_token']  # `</s>`
 elif args.tokenizer_type == "gpt2":
  __lowerCamelCase		:							Optional[int]		 =						GPTaTokenizer.from_pretrained(args.tokenizer_name						)
  __lowerCamelCase		:							List[str]		 =						tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
  __lowerCamelCase		:							Optional[Any]		 =						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:
  __lowerCamelCase		:							Optional[int]		 =						fp.readlines()

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

 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							int		 =						0
 __lowerCamelCase		:							int		 =						10_000
 __lowerCamelCase		:							Union[str, Any]		 =						time.time()
 for text in data:
  __lowerCamelCase		:							Union[str, Any]		 =						f'{bos} {text.strip()} {sep}'
  __lowerCamelCase		:							Optional[Any]		 =						tokenizer.encode(SCREAMING_SNAKE_CASE__      ,	add_special_tokens=SCREAMING_SNAKE_CASE__						)
  rslt.append(SCREAMING_SNAKE_CASE__						)

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

 __lowerCamelCase		:							int		 =						f'{args.dump_file}.{args.tokenizer_name}.pickle'
 __lowerCamelCase		:							List[str]		 =						tokenizer.vocab_size
 if vocab_size < (1 << 16):
  __lowerCamelCase		:							Dict		 =						[np.uintaa(SCREAMING_SNAKE_CASE__						) for d in rslt]
 else:
  __lowerCamelCase		:							Union[str, Any]		 =						[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,
)


lowercase_	   =     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

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

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MBartForCausalLM',
           'MBartForConditionalGeneration',
           'MBartForQuestionAnswering',
           'MBartForSequenceClassification',
           'MBartModel',
           'MBartPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFMBartForConditionalGeneration',
           'TFMBartModel',
           'TFMBartPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'FlaxMBartForConditionalGeneration',
           'FlaxMBartForQuestionAnswering',
           'FlaxMBartForSequenceClassification',
           'FlaxMBartModel',
           'FlaxMBartPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

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

