




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a													=			{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a													=			["FNetTokenizer"]

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a													=			[
				    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FNetForMaskedLM",
				    "FNetForMultipleChoice",
				    "FNetForNextSentencePrediction",
				    "FNetForPreTraining",
				    "FNetForQuestionAnswering",
				    "FNetForSequenceClassification",
				    "FNetForTokenClassification",
				    "FNetLayer",
				    "FNetModel",
				    "FNetPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet import FNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet_fast import FNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fnet import (
								    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FNetForMaskedLM,
								    FNetForMultipleChoice,
								    FNetForNextSentencePrediction,
								    FNetForPreTraining,
								    FNetForQuestionAnswering,
								    FNetForSequenceClassification,
								    FNetForTokenClassification,
								    FNetLayer,
								    FNetModel,
								    FNetPreTrainedModel,
								)


else:
				import sys

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





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						_UpperCAmelCase      =			sorted(numsa + numsa       )
						_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(len(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()
				a													=			[float(x) for x in input("Enter the elements of first array: ").split()]
				a													=			[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)}')