from math import pow, sqrt


def    a__       (						*__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase ) > 0 and all(value > 0.0 for value in values )
      return result


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return (
          round(sqrt(molar_mass_a / molar_mass_a )   ,					6 )
          if validate(__UpperCamelCase   ,					__UpperCamelCase )
          else ValueError("Input Error: Molar mass values must greater than 0." )
      )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )   ,					6 )
          if validate(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
          else ValueError(
              "Input Error: Molar mass and effusion rate values must greater than 0." )
      )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )   ,					6 )
          if validate(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
          else ValueError(
              "Input Error: Molar mass and effusion rate values must greater than 0." )
      )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      return (
          round(molar_mass / pow(effusion_rate_a / effusion_rate_a   ,					2 )   ,					6 )
          if validate(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
          else ValueError(
              "Input Error: Molar mass and effusion rate values must greater than 0." )
      )





def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      return (
          round(pow(effusion_rate_a / effusion_rate_a   ,					2 ) / molar_mass   ,					6 )
          if validate(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
          else ValueError(
              "Input Error: Molar mass and effusion rate values must greater than 0." )
      )







"""simple docstring"""
from typing import TYPE_CHECKING

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


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
