

def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	)							->     float:



		"""simple docstring"""






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

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

				_snake_case						=							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,
)


__A      		=     {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FalconForCausalLM''',
     '''FalconModel''',
     '''FalconPreTrainedModel''',
     '''FalconForSequenceClassification''',
     '''FalconForTokenClassification''',
     '''FalconForQuestionAnswering''',
 ]


if TYPE_CHECKING:
 from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_falcon import (
      FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
      FalconPreTrainedModel,
  )


else:
 import sys

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

