



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

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


_UpperCAmelCase     =			{
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RoFormerForCausalLM""",
       """RoFormerForMaskedLM""",
       """RoFormerForMultipleChoice""",
       """RoFormerForQuestionAnswering""",
       """RoFormerForSequenceClassification""",
       """RoFormerForTokenClassification""",
       """RoFormerLayer""",
       """RoFormerModel""",
       """RoFormerPreTrainedModel""",
       """load_tf_weights_in_roformer""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRoFormerForCausalLM""",
       """TFRoFormerForMaskedLM""",
       """TFRoFormerForMultipleChoice""",
       """TFRoFormerForQuestionAnswering""",
       """TFRoFormerForSequenceClassification""",
       """TFRoFormerForTokenClassification""",
       """TFRoFormerLayer""",
       """TFRoFormerModel""",
       """TFRoFormerPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """FlaxRoFormerForMaskedLM""",
       """FlaxRoFormerForMultipleChoice""",
       """FlaxRoFormerForQuestionAnswering""",
       """FlaxRoFormerForSequenceClassification""",
       """FlaxRoFormerForTokenClassification""",
       """FlaxRoFormerModel""",
       """FlaxRoFormerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

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








"""simple docstring"""
from __future__ import annotations





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=sorted(numsa + numsa )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=divmod(len(lowercase )							,			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()
   _UpperCAmelCase     =			[float(x) for x in input("""Enter the elements of first array: """).split()]
   _UpperCAmelCase     =			[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)}""")



