
from itertools import count
from math import asin, pi, sqrt

def       UpperCAmelCase__							(    __magic_name__  :       float					):


					'''simple docstring'''

					return (
					    (1 - 2 * point) * sqrt(point - point**2					) + 2 * point + asin(sqrt(1 - point					)					)
					) / 4

def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[Any]							=       (circles_number + 1 - sqrt(2 * circles_number					)) / (
					    2 * (circles_number**2 + 1)
					)
					lowerCAmelCase						:   Any							=       circles_number * intersection_y

					lowerCAmelCase						:   Union[str, Any]							=       intersection_x * intersection_y / 2
					lowerCAmelCase						:   Tuple							=       circle_bottom_arc_integral(
					    1 / 2					) - circle_bottom_arc_integral(__magic_name__					)

					return triangle_area + concave_region_area

def       UpperCAmelCase__							(    __magic_name__  :       float = 1 / 10_00					):


					'''simple docstring'''

					lowerCAmelCase						:   Optional[Any]							=       (1 - pi / 4) / 4

					for n in count(1					):
										if concave_triangle_area(__magic_name__					) / l_section_area < fraction:
															return n

					return -1


if __name__ == "__main__":
		print(f"""{solution() = }""")


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)
