



"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase:				Optional[int]    							=  TypeVar('T')



class   a__		(						Generic[T]     ):


   def __init__(							self      :							Any,     lowerCAmelCase      :							T			)    ->       Optional[Any]:
         lowercase  :      List[Any]					=   data  # Assign data
         lowercase  :      Node[T] | None					=   None  # Initialize next as null
         lowercase  :      Node[T] | None					=   None  # Initialize prev as null




class   a__		(						Generic[T]     ):


   def __init__(							self      :							List[Any]			)    ->       None:
         lowercase  :      Node[T] | None					=   None


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							T			)    ->       None:
         if self.head is None:
               lowercase  :      Optional[int]					=   Node(lowerCAmelCase			)
         else:
               lowercase  :      Any					=   Node(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   new_node
               lowercase  :      Any					=   self.head
               lowercase  :      Optional[Any]					=   None
               lowercase  :      List[str]					=   new_node


   def        lowercase				(							self      :							Optional[Any]			)    ->       T | None:
         if self.head is None:
               return None
         else:
               assert self.head is not None
               lowercase  :      Any					=   self.head.data
               lowercase  :      Any					=   self.head.next
               if self.head is not None:
                     lowercase  :      int					=   None
               return temp


   def        lowercase				(							self      :							Optional[int]			)    ->       T | None:
         return self.head.data if self.head is not None else None


   def __len__(							self      :							List[str]			)    ->       int:
         lowercase  :      Any					=   self.head
         lowercase  :      int					=   0
         while temp is not None:
               count += 1
               lowercase  :      Optional[int]					=   temp.next
         return count


   def        lowercase				(							self      :							List[Any]			)    ->       bool:
         return self.head is None






   def        lowercase				(							self      :							Any			)    ->       None:
         print('stack elements are:'			)
         lowercase  :      List[str]					=   self.head
         while temp is not None:
               print(temp.data,     end='->'			)
               lowercase  :      str					=   temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     _UpperCamelCase:				Stack[int]    							=  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"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
