





from typing import TYPE_CHECKING

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


snake_case 				=       {
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPTNeoForCausalLM""",
          """GPTNeoForQuestionAnswering""",
          """GPTNeoForSequenceClassification""",
          """GPTNeoForTokenClassification""",
          """GPTNeoModel""",
          """GPTNeoPreTrainedModel""",
          """load_tf_weights_in_gpt_neo""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxGPTNeoForCausalLM""",
          """FlaxGPTNeoModel""",
          """FlaxGPTNeoPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_neo import (
                GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTNeoForCausalLM,
                GPTNeoForQuestionAnswering,
                GPTNeoForSequenceClassification,
                GPTNeoForTokenClassification,
                GPTNeoModel,
                GPTNeoPreTrainedModel,
                load_tf_weights_in_gpt_neo,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






snake_case 				=       8.3_144_598

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float			,							snake_case__  :float     )      ->						float:
  if temperature < 0:
    raise Exception('Temperature cannot be less than 0 K'     )
  if molar_mass <= 0:
    raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'     )
  else:
    return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # example
      snake_case 				=       3_0_0
      snake_case 				=       2_8
      snake_case 				=       rms_speed_of_molecule(temperature, molar_mass)
      print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")