

from collections.abc import Callable

import numpy as np

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							int(np.ceil((x_end - xa) / step_size))
	SCREAMING_SNAKE_CASE :   Tuple					  =							np.zeros((n + 1,))
	SCREAMING_SNAKE_CASE :   List[Any]					  =							ya
	SCREAMING_SNAKE_CASE :   str					  =							xa

	for k in range(_a):
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							y[k] + step_size * ode_func(_a				,      y[k])
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							y[k] + (
		    (step_size / 2) * (ode_func(_a				,      y[k]) + ode_func(x + step_size				,      _a))
		)
		x += step_size

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)