





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


A__							:  str    =logging.get_logger(__name__)

A__							:  Union[str, Any]    ={
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[Any]			=  '''roformer'''




      def __init__(     self     :					List[str]     ,     __snake_case     :					Optional[int]=5_00_00     ,     __snake_case     :					Optional[int]=None     ,     __snake_case     :					Optional[int]=7_68     ,     __snake_case     :					List[Any]=12     ,     __snake_case     :					Optional[Any]=12     ,     __snake_case     :					Tuple=30_72     ,     __snake_case     :					Dict="gelu"     ,     __snake_case     :					int=0.1     ,     __snake_case     :					Optional[Any]=0.1     ,     __snake_case     :					List[Any]=15_36     ,     __snake_case     :					List[Any]=2     ,     __snake_case     :					Dict=0.02     ,     __snake_case     :					Optional[int]=1E-1_2     ,     __snake_case     :					Optional[int]=0     ,     __snake_case     :					Any=False     ,     __snake_case     :					Optional[Any]=True     ,     **__snake_case     :					Union[str, Any]     ,     )							-> Dict:
            super().__init__(pad_token_id=__snake_case     ,     **__snake_case    )

            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				hidden_size if embedding_size is None else embedding_size
            _lowerCAmelCase					=				hidden_size
            _lowerCAmelCase					=				num_hidden_layers
            _lowerCAmelCase					=				num_attention_heads
            _lowerCAmelCase					=				hidden_act
            _lowerCAmelCase					=				intermediate_size
            _lowerCAmelCase					=				hidden_dropout_prob
            _lowerCAmelCase					=				attention_probs_dropout_prob
            _lowerCAmelCase					=				max_position_embeddings
            _lowerCAmelCase					=				type_vocab_size
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				layer_norm_eps
            _lowerCAmelCase					=				rotary_value
            _lowerCAmelCase					=				use_cache




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                  _lowerCAmelCase					=				{0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  _lowerCAmelCase					=				{0: """batch""", 1: """sequence"""}
            _lowerCAmelCase					=				{0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""input_ids""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                    ("""token_type_ids""", dynamic_axis),
                ]    )













'''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
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


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

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

      logger.info("""Start encoding"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples to process."    )

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

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

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


if __name__ == "__main__":
      main()






