


'''simple docstring'''
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     str								=      logging.get_logger(__name__)

UpperCAmelCase_ :     List[Any]								=      {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Any    							=						'''xlnet'''
 __lowercase				:		List[Any]    							=						['''mems''']
 __lowercase				:		Dict    							=						{
     '''n_token''': '''vocab_size''',  # Backward compatibility
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }


 def __init__(      self      ,      __lowercase=32_000      ,      __lowercase=1_024      ,      __lowercase=24      ,      __lowercase=16      ,      __lowercase=4_096      ,      __lowercase="gelu"      ,      __lowercase=True      ,      __lowercase="bi"      ,      __lowercase=0.0_2      ,      __lowercase=1E-12      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=None      ,      __lowercase=True      ,      __lowercase=False      ,      __lowercase=False      ,      __lowercase=-1      ,      __lowercase=False      ,      __lowercase="last"      ,      __lowercase=True      ,      __lowercase="tanh"      ,      __lowercase=0.1      ,      __lowercase=5      ,      __lowercase=5      ,      __lowercase=5      ,      __lowercase=1      ,      __lowercase=2      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Optional[int]       =			vocab_size
  __A  :		str       =			d_model
  __A  :		Any       =			n_layer
  __A  :		List[Any]       =			n_head
  if d_model % n_head != 0:
   raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""						)
  if "d_head" in kwargs:
   if kwargs["d_head"] != d_model // n_head:
    raise ValueError(
        F"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""						)
  __A  :		Tuple       =			d_model // n_head
  __A  :		Optional[int]       =			ff_activation
  __A  :		str       =			d_inner
  __A  :		str       =			untie_r
  __A  :		Optional[int]       =			attn_type

  __A  :		Any       =			initializer_range
  __A  :		Union[str, Any]       =			layer_norm_eps

  __A  :		Any       =			dropout
  __A  :		Optional[int]       =			mem_len
  __A  :		str       =			reuse_len
  __A  :		str       =			bi_data
  __A  :		Optional[Any]       =			clamp_len
  __A  :		List[Any]       =			same_length

  __A  :		Union[str, Any]       =			summary_type
  __A  :		List[Any]       =			summary_use_proj
  __A  :		List[str]       =			summary_activation
  __A  :		Union[str, Any]       =			summary_last_dropout
  __A  :		Optional[Any]       =			start_n_top
  __A  :		Any       =			end_n_top

  __A  :		List[Any]       =			bos_token_id
  __A  :		int       =			pad_token_id
  __A  :		str       =			eos_token_id

  if "use_cache" in kwargs:
   warnings.warn(
       'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
       ' instead.'      ,      __lowercase      ,      )
   __A  :		Any       =			kwargs['use_cache']

  __A  :		Dict       =			use_mems_eval
  __A  :		List[Any]       =			use_mems_train
  super().__init__(pad_token_id=__lowercase      ,      bos_token_id=__lowercase      ,      eos_token_id=__lowercase      ,      **__lowercase						)


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)
  return -1







 @max_position_embeddings.setter
 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  raise NotImplementedError(
      F"""The model {self.model_type} is one of the few models that has no sequence length limit."""						)










'''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
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


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

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

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

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

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

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


if __name__ == "__main__":
 main()






