






"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase :    Optional[Any]      =			logging.get_logger(__name__)

_UpperCamelCase :    List[Any]      =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}






class __a ( snake_case_					):
  __lowercase     :     List[str]       						=       'rembert'



  def __init__(self	:		List[Any]					,      lowercase_	:		List[Any]=250_300					,      lowercase_	:		int=1_152					,      lowercase_	:		Union[str, Any]=32					,      lowercase_	:		int=18					,      lowercase_	:		List[str]=256					,      lowercase_	:		List[Any]=1_664					,      lowercase_	:		str=4_608					,      lowercase_	:		str="gelu"					,      lowercase_	:		Optional[int]=0.0					,      lowercase_	:		Tuple=0.0					,      lowercase_	:		List[Any]=0.1					,      lowercase_	:		Optional[int]=512					,      lowercase_	:		List[str]=2					,      lowercase_	:		str=0.02					,      lowercase_	:		Tuple=1E-12					,      lowercase_	:		List[str]=True					,      lowercase_	:		Optional[Any]=0					,      lowercase_	:		List[str]=312					,      lowercase_	:		Optional[int]=313					,      **lowercase_	:		List[Any]					,      )		->				Tuple:
   super().__init__(pad_token_id=lowercase_					,      bos_token_id=lowercase_					,      eos_token_id=lowercase_					,      **lowercase_ )

   __A						:							int      =						vocab_size
   __A						:							List[Any]      =						input_embedding_size
   __A						:							Any      =						output_embedding_size
   __A						:							Optional[int]      =						max_position_embeddings
   __A						:							Union[str, Any]      =						hidden_size
   __A						:							Optional[int]      =						num_hidden_layers
   __A						:							int      =						num_attention_heads
   __A						:							Union[str, Any]      =						intermediate_size
   __A						:							int      =						hidden_act
   __A						:							Tuple      =						hidden_dropout_prob
   __A						:							Optional[int]      =						attention_probs_dropout_prob
   __A						:							Union[str, Any]      =						classifier_dropout_prob
   __A						:							List[Any]      =						initializer_range
   __A						:							Optional[int]      =						type_vocab_size
   __A						:							List[Any]      =						layer_norm_eps
   __A						:							Any      =						use_cache
   __A						:							Any      =						False






class __a ( snake_case_					):



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



  @property
  def     A_					(self	:		Tuple )		->				float:
   return 1E-4













"""simple docstring"""


import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_UpperCamelCase :    List[str]      =			logging.getLogger(__name__)




def 					a			(  )	->		Union[str, Any]:
 __A						:							int      =						argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'		)
 parser.add_argument(
     '--dataset_name'  ,						type=UpperCamelCase_  ,						default='wikitext'  ,						help='Name of the training. Explore datasets at: hf.co/datasets.'  ,						)
 parser.add_argument(
     '--dataset_config'  ,						type=UpperCamelCase_  ,						default='wikitext-103-raw-v1'  ,						help='Configuration name of the dataset.'		)
 parser.add_argument(
     '--batch_size'  ,						type=UpperCamelCase_  ,						default=1_0_0_0  ,						help='Batch size during training.'  ,						)
 parser.add_argument(
     '--vocab_size'  ,						type=UpperCamelCase_  ,						default=1_0_0_4_8  ,						help='Size of the desired vocabulary.'  ,						)
 parser.add_argument(
     '--limit'  ,						default=UpperCamelCase_  ,						type=UpperCamelCase_  ,						help='Limit the number of shards (used for debugging).'  ,						)
 parser.add_argument(
     '--export_to_hub'  ,						action='store_true'  ,						)

 __A						:							List[Any]      =						parser.parse_args()
 return args




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		Optional[Any]:
 __A						:							Union[str, Any]      =						datasets.load_dataset(args.dataset_name  ,						args.dataset_config  ,						split='train'		)

 if args.limit is not None:
  __A						:							Tuple      =						min(len(UpperCamelCase_		)  ,						args.limit		)
  __A						:							Optional[int]      =						dataset.select(range(UpperCamelCase_		)		)
  logger.info(F"Limiting the dataset to {args.limit} entries."		)

 def batch_iterator():
  for i in range(0  ,						len(UpperCamelCase_		)  ,						args.batch_size		):
   yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
 __A						:							Any      =						Tokenizer(Unigram()		)
 __A						:							List[Any]      =						normalizers.Sequence([normalizers.Replace('``'  ,						'"'		), normalizers.Replace('\'\''  ,						'"'		)]		)
 __A						:							Optional[int]      =						pre_tokenizers.Metaspace()

 # Prepare the trainer.
 __A						:							int      =						UnigramTrainer(
     unk_token='<unk>'  ,						special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']  ,						vocab_size=args.vocab_size  ,						)

 logger.info('Training the tokenizer.'		)
 tokenizer.train_from_iterator(batch_iterator()  ,						trainer=UpperCamelCase_		)
 logger.info('Tokenizer training complete!'		)

 __A						:							Union[str, Any]      =						tokenizer.token_to_id('[CLS]'		)
 __A						:							str      =						tokenizer.token_to_id('[SEP]'		)
 __A						:							Tuple      =						processors.TemplateProcessing(
     single='[CLS]:0 $A:0 [SEP]:0'  ,						pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'  ,						special_tokens=[
         ('[CLS]', cls_token_id),
         ('[SEP]', sep_token_id),
     ]  ,						)
 __A						:							List[str]      =						decoders.Metaspace()

 if args.export_to_hub:
  logger.info('Exporting the trained tokenzier to Hub.'		)
  __A						:							Union[str, Any]      =						AlbertTokenizerFast(tokenizer_object=UpperCamelCase_		)
  new_tokenizer.push_to_hub('unigram-tokenizer-dataset'		)


if __name__ == "__main__":
   _UpperCamelCase :    Optional[Any]      =			parse_args()
   main(args)





