def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						)						->    float:



      '''simple docstring'''

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

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

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


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     snake_case__				= 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


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

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