

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_             =     {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ["""RobertaTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """RobertaForCausalLM""",
      """RobertaForMaskedLM""",
      """RobertaForMultipleChoice""",
      """RobertaForQuestionAnswering""",
      """RobertaForSequenceClassification""",
      """RobertaForTokenClassification""",
      """RobertaModel""",
      """RobertaPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFRobertaForCausalLM""",
      """TFRobertaForMaskedLM""",
      """TFRobertaForMultipleChoice""",
      """TFRobertaForQuestionAnswering""",
      """TFRobertaForSequenceClassification""",
      """TFRobertaForTokenClassification""",
      """TFRobertaMainLayer""",
      """TFRobertaModel""",
      """TFRobertaPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      """FlaxRobertaForCausalLM""",
      """FlaxRobertaForMaskedLM""",
      """FlaxRobertaForMultipleChoice""",
      """FlaxRobertaForQuestionAnswering""",
      """FlaxRobertaForSequenceClassification""",
      """FlaxRobertaForTokenClassification""",
      """FlaxRobertaModel""",
      """FlaxRobertaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
  from .tokenization_roberta import RobertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roberta_fast import RobertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta import (
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaForCausalLM,
        RobertaForMaskedLM,
        RobertaForMultipleChoice,
        RobertaForQuestionAnswering,
        RobertaForSequenceClassification,
        RobertaForTokenClassification,
        RobertaModel,
        RobertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta import (
        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        TFRobertaForMultipleChoice,
        TFRobertaForQuestionAnswering,
        TFRobertaForSequenceClassification,
        TFRobertaForTokenClassification,
        TFRobertaMainLayer,
        TFRobertaModel,
        TFRobertaPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta import (
        FlaxRobertaForCausalLM,
        FlaxRobertaForMaskedLM,
        FlaxRobertaForMultipleChoice,
        FlaxRobertaForQuestionAnswering,
        FlaxRobertaForSequenceClassification,
        FlaxRobertaForTokenClassification,
        FlaxRobertaModel,
        FlaxRobertaPreTrainedModel,
    )

else:
  import sys

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





from math import pow, sqrt
def   a   (    *A__			:     float  )					->   bool:





      """simple docstring"""



      _lowercase			 =len(A__  ) > 0 and all(value > 0.0 for value in values  )
      return result
def   a   (    A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__  )
          else ValueError('Input Error: Molar mass values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate * sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



      return (
          round(effusion_rate / sqrt(molar_mass_a / molar_mass_a  )   ,     6  )
          if validate(A__   ,     A__   ,     A__  )
          else ValueError(
              'Input Error: Molar mass and effusion rate values must greater than 0.'  )
      )
def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



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







def   a   (    A__			:     float   ,     A__			:     float   ,     A__			:     float  )					->   float | ValueError:





      """simple docstring"""



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


