






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
A__	:			Tuple			   = 1.054_571_817e-34  # unit of ℏ : J * s

A__	:			int			   = 3e8  # unit of c : m * s^-1






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''

				if (force, area, distance).count(0				) != 1:
								raise ValueError('''One and only one argument must be 0'''				)
				if force < 0:
								raise ValueError('''Magnitude of force can not be negative'''				)
				if distance < 0:
								raise ValueError('''Distance can not be negative'''				)
				if area < 0:
								raise ValueError('''Area can not be negative'''				)
				if force == 0:
								lowercase__    =			(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
								    240 * (distance) ** 4
								)
								return {"force": force}
				elif area == 0:
								lowercase__    =			(240 * force * (distance) ** 4) / (
								    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
								)
								return {"area": area}
				elif distance == 0:
								lowercase__    =			(
								    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
								) ** (1 / 4)
								return {"distance": distance}
				raise ValueError('''One and only one argument must be 0'''				)


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()










from typing import TYPE_CHECKING

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


UpperCamelCase__    			=			{
    """configuration_audio_spectrogram_transformer""": [
        """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ASTConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__    			=			[
					    """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ASTForAudioClassification""",
					    """ASTModel""",
					    """ASTPreTrainedModel""",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__    			=			["""ASTFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

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




