





from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       float:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE		=						sorted(numsa + numsa     )
						SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						divmod(len(_SCREAMING_SNAKE_CASE     )			,						2     )
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						SCREAMING_SNAKE_CASE_						       =	[float(x) for x in input("""Enter the elements of first array: """).split()]
						SCREAMING_SNAKE_CASE_						       =	[float(x) for x in input("""Enter the elements of second array: """).split()]
						print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

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


