






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

lowerCamelCase_							      = 8.988E9  # units = N * m^s * C^-2

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					str     	=   abs(chargea * chargea		)

			if (force, chargea, chargea, distance).count(0		) != 1:
						raise ValueError("One and only one argument must be 0"		)
			if distance < 0:
						raise ValueError("Distance cannot be negative"		)
			if force == 0:
						UpperCAmelCase_	:					Any     	=   COULOMBS_CONSTANT * charge_product / (distance**2)
						return {"force": force}
			elif chargea == 0:
						UpperCAmelCase_	:					Optional[Any]     	=   abs(A__		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
						return {"charge1": chargea}
			elif chargea == 0:
						UpperCAmelCase_	:					Any     	=   abs(A__		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
						return {"charge2": chargea}
			elif distance == 0:
						UpperCAmelCase_	:					Tuple     	=   (COULOMBS_CONSTANT * charge_product / abs(A__		)) ** 0.5
						return {"distance": distance}
			raise ValueError("Exactly one argument must be 0"		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()










'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

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



