

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__)

