



"""simple docstring"""







UpperCAmelCase:  str			     =				[0.33, 0.44, 0.55, 0.44, 0.33]
UpperCAmelCase:  Optional[Any]			     =				3
UpperCAmelCase:  List[str]			     =				5

# Buffer Space (with Parameters Space)
UpperCAmelCase:  list[float]			     =				[]
UpperCAmelCase:  list[float]			     =				[]

# Machine Time
UpperCAmelCase:  Tuple			     =				0


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		global buffer_space, params_space, machine_time, K, m, t

		# Choosing Dynamical Systems (All)
		for key, value in enumerate(__UpperCAmelCase      ):
				# Evolution Parameter
				_lowercase				: List[Any]     =   float(seed / value      )

				# Control Theory: Orbit Change
				_lowercase				: List[str]     =   (buffer_space[(key + 1) % m] + e) % 1

				# Control Theory: Trajectory Change
				_lowercase				: int     =   (params_space[key] + e) % 1 + 3

				# Modification (Transition Function) - Jumps
				_lowercase				: Union[str, Any]     =   round(float(r * value * (1 - value)      )   ,				10      )
				_lowercase				: List[Any]     =   r  # Saving to Parameters Space

		# Logistic Map
		assert max(__UpperCAmelCase      ) < 1
		assert max(__UpperCAmelCase      ) < 4

		# Machine Time
		machine_time += 1


def  __SCREAMING_SNAKE_CASE (  ):
		global buffer_space, params_space, machine_time, K, m, t

		# PRNG (Xorshift by George Marsaglia)
		def xorshift(__UpperCAmelCase   ,				__UpperCAmelCase      ):
				x ^= y >> 13
				y ^= x << 17
				x ^= y >> 5
				return x

		# Choosing Dynamical Systems (Increment)
		_lowercase				: Optional[Any]     =   machine_time % m

		# Evolution (Time Length)
		for _ in range(0   ,				__UpperCAmelCase      ):
				# Variables (Position + Parameters)
				_lowercase				: str     =   params_space[key]
				_lowercase				: Optional[int]     =   buffer_space[key]

				# Modification (Transition Function) - Flow
				_lowercase				: Optional[Any]     =   round(float(r * value * (1 - value)      )   ,				10      )
				_lowercase				: Optional[Any]     =   (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

		# Choosing Chaotic Data
		_lowercase				: List[Any]     =   int(buffer_space[(key + 2) % m] * (10**10)      )
		_lowercase				: List[Any]     =   int(buffer_space[(key - 2) % m] * (10**10)      )

		# Machine Time
		machine_time += 1

		return xorshift(__UpperCAmelCase   ,				__UpperCAmelCase      ) % 0xff_fff_fff


def  __SCREAMING_SNAKE_CASE (  ):
		global buffer_space, params_space, machine_time, K, m, t

		_lowercase				: int     =   K
		_lowercase				: Optional[Any]     =   [0] * m
		_lowercase				: List[str]     =   0


if __name__ == "__main__":
		# Initialization
		reset()

		# Pushing Data (Input)
		import random

		UpperCAmelCase:  Any			     =				random.sample(range(0xff_ff_ff_ff), 100)
		for chunk in message:
				push(chunk)

		# for controlling
		UpperCAmelCase:  Any			     =				""""""

		# Pulling Data (Output)
		while inp in ("e", "E"):
				print(F'{format(pull(), "#04x")}')
				print(buffer_space)
				print(params_space)
				UpperCAmelCase:  List[Any]			     =				input("""(e)exit? """).strip()




'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



