


"""simple docstring"""


import numpy as np


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			int(np.ceil((x_end - xa) / h      )      )
			__lowerCAmelCase				    =			np.zeros((n + 1,)      )
			__lowerCAmelCase				    =			ya
			__lowerCAmelCase				    =			xa

			for k in range(_UpperCamelCase      ):
						__lowerCAmelCase				    =			f(_UpperCamelCase    , y[k]      )
						__lowerCAmelCase				    =			f(x + 0.5 * h    , y[k] + 0.5 * h * ka      )
						__lowerCAmelCase				    =			f(x + 0.5 * h    , y[k] + 0.5 * h * ka      )
						__lowerCAmelCase				    =			f(x + h    , y[k] + h * ka      )
						__lowerCAmelCase				    =			y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
						x += h

			return y


if __name__ == "__main__":
	import doctest

	doctest.testmod()











"""simple docstring"""





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

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

@dataclass(frozen=_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      field(default='language-modeling'     ,  metadata={'include_in_asdict_even_if_is_default': True}					)

      _SCREAMING_SNAKE_CASE					=      Features({'text': Value('string'					)}					)
      _SCREAMING_SNAKE_CASE					=      Features({}					)
      _SCREAMING_SNAKE_CASE					=      "text"





      @property
      def     _snake_case			(       self )		->     Dict[str, str]:
            return {self.text_column: "text"}






