"""simple docstring"""


from __future__ import annotations

# Define the Gravitational Constant G and the function
a							=6.6_7_4_3E-1_1  # unit of G : m^3 * kg^-1 * s^-2





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> dict[str, float]:




						'''simple docstring'''



						lowerCamelCase__ =mass_a * mass_a

						if (force, mass_a, mass_a, distance).count(0    ) != 1:
												raise ValueError("One and only one argument must be 0"    )
						if force < 0:
												raise ValueError("Gravitational force can not be negative"    )
						if distance < 0:
												raise ValueError("Distance can not be negative"    )
						if mass_a < 0 or mass_a < 0:
												raise ValueError("Mass can not be negative"    )
						if force == 0:
												lowerCamelCase__ =GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
												return {"force": force}
						elif mass_a == 0:
												lowerCamelCase__ =(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
												return {"mass_1": mass_a}
						elif mass_a == 0:
												lowerCamelCase__ =(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
												return {"mass_2": mass_a}
						elif distance == 0:
												lowerCamelCase__ =(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
												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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OpenAIGPTDoubleHeadsModel',
	    'OpenAIGPTForSequenceClassification',
	    'OpenAIGPTLMHeadModel',
	    'OpenAIGPTModel',
	    'OpenAIGPTPreTrainedModel',
	    'load_tf_weights_in_openai_gpt',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFOpenAIGPTDoubleHeadsModel',
	    'TFOpenAIGPTForSequenceClassification',
	    'TFOpenAIGPTLMHeadModel',
	    'TFOpenAIGPTMainLayer',
	    'TFOpenAIGPTModel',
	    'TFOpenAIGPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

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


