






from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     bool:
     if len(__lowerCAmelCase      ) == 0:
          return False
     UpperCamelCase__      :       Any	    =  len(__lowerCAmelCase      ) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint] ,   __lowerCAmelCase      )
     else:
          return binary_search(a_list[midpoint + 1 :] ,   __lowerCAmelCase      )


if __name__ == "__main__":
 lowerCamelCase     :							Any    =input('''Enter numbers separated by comma:\n''').strip()
 lowerCamelCase     :							Dict    =[int(item.strip()) for item in user_input.split(''',''')]
 lowerCamelCase     :							List[str]    =int(input('''Enter the number to be found in the list:\n''').strip())
 lowerCamelCase     :							Union[str, Any]    ='''''' if binary_search(sequence, target) else '''not '''
 print(F"""{target} was {not_str}found in {sequence}""")







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=A__	)
class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			field(default='''language-modeling'''	,       metadata={'''include_in_asdict_even_if_is_default''': True}	)

 _lowerCAmelCase	:	ClassVar[Features]				=			Features({'''text''': Value('''string'''	)}	)
 _lowerCAmelCase	:	ClassVar[Features]				=			Features({}	)
 _lowerCAmelCase	:	str				=			"text"







 @property
 def  __lowercase (		self :       str						):


      '''simple docstring'''






      return {self.text_column: "text"}