



'''simple docstring'''

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     						=1.0_54_57_18_17e-34  # unit of ℏ : J * s

_A				:    str     						=3e8  # unit of c : m * s^-1







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			dict[str, float]:

	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					:       Dict       =		(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
		    240 * (distance) ** 4
		)
		return {"force": force}
	elif area == 0:
		_lowercase					:       Optional[Any]       =		(240 * force * (distance) ** 4) / (
		    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
		)
		return {"area": area}
	elif distance == 0:
		_lowercase					:       int       =		(
		    (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()





'''simple docstring'''

from typing import TYPE_CHECKING

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


_A				:    Union[str, Any]     						={'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Dict     						=['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    Union[str, Any]     						=['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A				:    str     						=[
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		_A				:    Union[str, Any]     						=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
