





from __future__ import annotations

from typing import Generic, TypeVar

_UpperCAmelCase										=					TypeVar('T')



class 				_UpperCamelCase       (						Generic[T]  ):

       def __init__(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			T		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	data  # Assign data
              UpperCamelCase_ 							=	None  # Initialize next as null
              UpperCamelCase_ 							=	None  # Initialize prev as null



class 				_UpperCamelCase       (						Generic[T]  ):

       def __init__(		self:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	None

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			T		)							->			None:




              """simple docstring"""


              if self.head is None:
                     UpperCamelCase_ 							=	Node(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	Node(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	new_node
                     UpperCamelCase_ 							=	self.head
                     UpperCamelCase_ 							=	None
                     UpperCamelCase_ 							=	new_node

       def lowercase			(		self:			int		)							->			T | None:




              """simple docstring"""


              if self.head is None:
                     return None
              else:
                     assert self.head is not None
                     UpperCamelCase_ 							=	self.head.data
                     UpperCamelCase_ 							=	self.head.next
                     if self.head is not None:
                            UpperCamelCase_ 							=	None
                     return temp

       def lowercase			(		self:			Tuple		)							->			T | None:




              """simple docstring"""


              return self.head.data if self.head is not None else None

       def __len__(		self:			List[Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	self.head
              UpperCamelCase_ 							=	0
              while temp is not None:
                     count += 1
                     UpperCamelCase_ 							=	temp.next
              return count

       def lowercase			(		self:			Tuple		)							->			bool:




              """simple docstring"""


              return self.head is None



       def lowercase			(		self:			Optional[Any]		)							->			None:




              """simple docstring"""


              print("stack elements are:"		)
              UpperCamelCase_ 							=	self.head
              while temp is not None:
                     print(temp.data       , end="->"		)
                     UpperCamelCase_ 							=	temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     _UpperCAmelCase										=					Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())






"""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  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: List[Any]     =   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=1000   ,				help="""Batch size during training."""   ,				)
		parser.add_argument(
		    """--vocab_size"""   ,				type=__UpperCAmelCase   ,				default=10048   ,				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"""   ,				)

		_lowercase				: Any     =   parser.parse_args()
		return args


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: List[Any]     =   datasets.load_dataset(args.dataset_name   ,				args.dataset_config   ,				split="""train"""      )

		if args.limit is not None:
				_lowercase				: Tuple     =   min(len(__UpperCAmelCase      )   ,				args.limit      )
				_lowercase				: Optional[Any]     =   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.
		_lowercase				: Optional[int]     =   Tokenizer(Unigram()      )
		_lowercase				: List[str]     =   normalizers.Sequence([normalizers.Replace("""``"""   ,				"""\""""      ), normalizers.Replace("""''"""   ,				"""\""""      )]      )
		_lowercase				: str     =   pre_tokenizers.Metaspace()

		# Prepare the trainer.
		_lowercase				: 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!"""      )

		_lowercase				: int     =   tokenizer.token_to_id("""[CLS]"""      )
		_lowercase				: Optional[Any]     =   tokenizer.token_to_id("""[SEP]"""      )
		_lowercase				: Optional[int]     =   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),
		    ]   ,				)
		_lowercase				: Union[str, Any]     =   decoders.Metaspace()

		if args.export_to_hub:
				logger.info("""Exporting the trained tokenzier to Hub."""      )
				_lowercase				: List[Any]     =   AlbertTokenizerFast(tokenizer_object=__UpperCAmelCase      )
				new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""      )


if __name__ == "__main__":
		UpperCAmelCase:  List[Any]			     =				parse_args()
		main(args)



