
from __future__ import annotations

from typing import Generic, TypeVar

__SCREAMING_SNAKE_CASE     :				int             =       TypeVar('T')


class 						__magic_name__				(  snake_case      ):
		pass



class 						__magic_name__				(  snake_case      ):
		pass



class 						__magic_name__				(  Generic[T]      ):





		def __init__(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		int = 1_0			):
							lowerCAmelCase						:   list[T]							=       []
							lowerCAmelCase						:   List[str]							=       limit





		def __bool__(							self      :		Optional[Any]			):
							return bool(self.stack			)





		def __str__(							self      :		Optional[int]			):
							return str(self.stack			)





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		T			):
							if len(self.stack			) >= self.limit:
												raise StackOverflowError
							self.stack.append(lowerCamelCase__			)





		def  _A							(							self      :		Tuple			):
							if not self.stack:
												raise StackUnderflowError
							return self.stack.pop()





		def  _A							(							self      :		str			):
							if not self.stack:
												raise StackUnderflowError
							return self.stack[-1]





		def  _A							(							self      :		Optional[Any]			):
							return not bool(self.stack			)





		def  _A							(							self      :		str			):
							return self.size() == self.limit





		def  _A							(							self      :		int			):
							return len(self.stack			)





		def __contains__(							self      :		str  ,					lowerCamelCase__      :		T			):
							return item in self.stack



def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   Stack[int]							=       Stack(10					)
					assert bool(__magic_name__					) is False
					assert stack.is_empty() is True
					assert stack.is_full() is False
					assert str(__magic_name__					) == "[]"

					try:
										lowerCAmelCase						:   Union[str, Any]							=       stack.pop()
										raise AssertionError  # This should not happen
					except StackUnderflowError:
										assert True  # This should happen

					try:
										lowerCAmelCase						:   Dict							=       stack.peek()
										raise AssertionError  # This should not happen
					except StackUnderflowError:
										assert True  # This should happen

					for i in range(10					):
										assert stack.size() == i
										stack.push(__magic_name__					)

					assert bool(__magic_name__					)
					assert not stack.is_empty()
					assert stack.is_full()
					assert str(__magic_name__					) == str(list(range(10					)					)					)
					assert stack.pop() == 9
					assert stack.peek() == 8

					stack.push(1_00					)
					assert str(__magic_name__					) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_00]					)

					try:
										stack.push(2_00					)
										raise AssertionError  # This should not happen
					except StackOverflowError:
										assert True  # This should happen

					assert not stack.is_empty()
					assert stack.size() == 10

					assert 5 in stack
					assert 55 not in stack


if __name__ == "__main__":
		test_stack()


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       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

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


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

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


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       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)
