

'''simple docstring'''






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


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''sail/poolformer_s12''': '''https://huggingface.co/sail/poolformer_s12/resolve/main/config.json''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			'poolformer'






  def __init__(      self   :     Optional[Any]					,lowercase__   :     int=3					,lowercase__   :     int=1_6					,lowercase__   :     int=1_6					,lowercase__   :     str=3					,lowercase__   :     Dict=4.0					,lowercase__   :     str=[2, 2, 6, 2]					,lowercase__   :     List[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]					,lowercase__   :     Union[str, Any]=[7, 3, 3, 3]					,lowercase__   :     Tuple=[4, 2, 2, 2]					,lowercase__   :     Any=[2, 1, 1, 1]					,lowercase__   :     Dict=4					,lowercase__   :     int=0.0					,lowercase__   :     str="gelu"					,lowercase__   :     int=True					,lowercase__   :     Dict=1e-5					,lowercase__   :     Dict=0.0_2					,**lowercase__   :     List[str]					,):
   __lowercase							  = num_channels
   __lowercase							  = patch_size
   __lowercase							  = stride
   __lowercase							  = padding
   __lowercase							  = pool_size
   __lowercase							  = hidden_sizes
   __lowercase							  = mlp_ratio
   __lowercase							  = depths
   __lowercase							  = patch_sizes
   __lowercase							  = strides
   __lowercase							  = num_encoder_blocks
   __lowercase							  = drop_path_rate
   __lowercase							  = hidden_act
   __lowercase							  = use_layer_scale
   __lowercase							  = layer_scale_init_value
   __lowercase							  = initializer_range
   super().__init__(**lowercase__			)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			version.parse('1.11'      )






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return OrderedDict(
       [
           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
       ]			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return 2e-3








"""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
)
lowerCamelCase_								=		logging.getLogger(__name__)



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, 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=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.'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()

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

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

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[str]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1_00_00
      __SCREAMING_SNAKE_CASE   :List[Any]						=  time.time()
      for text in data:
            __SCREAMING_SNAKE_CASE   :Any						=  f'''{bos} {text.strip()} {sep}'''
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer.encode(a_ ,   add_special_tokens=a_							)
            rslt.append(a_							)

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

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