

def 					_lowercase     (				a_				:					float					,a_				:					float					,a_				:					float					,a_				:					float					,a_				:					float					,)				->	float:




					'''simple docstring'''







					__magic_name__							=    [redshift, radiation_density, matter_density, dark_energy]
					if any(p < 0 for p in parameters				):
										raise ValueError('All input parameters must be positive'				)

					if any(p > 1 for p in parameters[1:4]				):
										raise ValueError('Relative densities cannot be greater than one'				)
					else:
										__magic_name__							=    1 - (matter_density + radiation_density + dark_energy)

										__magic_name__							=    (
										    radiation_density * (redshift + 1) ** 4
										    + matter_density * (redshift + 1) ** 3
										    + curvature * (redshift + 1) ** 2
										    + dark_energy
										)

										__magic_name__							=    hubble_constant * e_a ** (1 / 2)
										return hubble


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo LCDM approximation
						A__						=							0.3

						print(
						    hubble_parameter(
						        hubble_constant=68.3,
						        radiation_density=1e-4,
						        matter_density=matter_density,
						        dark_energy=1 - matter_density,
						        redshift=0,
						    )
						)





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__						=							{"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "VanForImageClassification",
						    "VanModel",
						    "VanPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						A__						=							_LazyModule(__name__, globals()["__file__"], _import_structure)




