





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						()       ->     str:
  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=_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.'	)
  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(_SCREAMING_SNAKE_CASE	)} examples to process."""	)

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

    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(_SCREAMING_SNAKE_CASE	)} examples processed."""	)

  lowercase__			    =						F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
  lowercase__			    =						tokenizer.vocab_size
  if vocab_size < (1 << 16):
    lowercase__			    =						[np.uintaa(_SCREAMING_SNAKE_CASE	) for d in rslt]
  else:
    lowercase__			    =						[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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Any				      = 'deit'


   def __init__(					self    :       Any    ,  a    :       Union[str, Any]=768    ,  a    :       Optional[Any]=12    ,  a    :       Union[str, Any]=12    ,  a    :       Optional[int]=3_072    ,  a    :       Optional[int]="gelu"    ,  a    :       Optional[Any]=0.0    ,  a    :       List[Any]=0.0    ,  a    :       int=0.02    ,  a    :       List[str]=1E-1_2    ,  a    :       Optional[int]=224    ,  a    :       Tuple=16    ,  a    :       List[Any]=3    ,  a    :       List[str]=True    ,  a    :       Any=16    ,  **a    :       Union[str, Any]    ,  )->     int:





     """simple docstring"""







     super().__init__(**a )

     lowercase__			    =						hidden_size
     lowercase__			    =						num_hidden_layers
     lowercase__			    =						num_attention_heads
     lowercase__			    =						intermediate_size
     lowercase__			    =						hidden_act
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						attention_probs_dropout_prob
     lowercase__			    =						initializer_range
     lowercase__			    =						layer_norm_eps
     lowercase__			    =						image_size
     lowercase__			    =						patch_size
     lowercase__			    =						num_channels
     lowercase__			    =						qkv_bias
     lowercase__			    =						encoder_stride






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	List[Any]				      = version.parse('1.11'   )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Mapping[str, Mapping[int, str]]:





     """simple docstring"""







     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ] )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     float:





     """simple docstring"""







     return 1E-4


