


"""simple docstring"""




def 			UpperCamelCase (     __magic_name__					:							int   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Tuple   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							Optional[int]   ,   )       ->  float:
		"""simple docstring"""




		lowercase__					      =		[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:
				lowercase__					      =		1 - (matter_density + radiation_density + dark_energy)

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

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


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# demo LCDM approximation
	A	:		Dict    =       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	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

