






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					:		Any           = {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Any           = ['''GPT2TokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Union[str, Any]           = [
	    '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPT2DoubleHeadsModel''',
	    '''GPT2ForQuestionAnswering''',
	    '''GPT2ForSequenceClassification''',
	    '''GPT2ForTokenClassification''',
	    '''GPT2LMHeadModel''',
	    '''GPT2Model''',
	    '''GPT2PreTrainedModel''',
	    '''load_tf_weights_in_gpt2''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Optional[int]           = [
	    '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFGPT2DoubleHeadsModel''',
	    '''TFGPT2ForSequenceClassification''',
	    '''TFGPT2LMHeadModel''',
	    '''TFGPT2MainLayer''',
	    '''TFGPT2Model''',
	    '''TFGPT2PreTrainedModel''',
	]

try:
	if not is_keras_nlp_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Tuple           = ['''TFGPT2Tokenizer''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		List[str]           = ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
	from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
	from .tokenization_gpta import GPTaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_fast import GPTaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpta import (
		    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTaDoubleHeadsModel,
		    GPTaForQuestionAnswering,
		    GPTaForSequenceClassification,
		    GPTaForTokenClassification,
		    GPTaLMHeadModel,
		    GPTaModel,
		    GPTaPreTrainedModel,
		    load_tf_weights_in_gpta,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gpta import (
		    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGPTaDoubleHeadsModel,
		    TFGPTaForSequenceClassification,
		    TFGPTaLMHeadModel,
		    TFGPTaMainLayer,
		    TFGPTaModel,
		    TFGPTaPreTrainedModel,
		)

	try:
		if not is_keras_nlp_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpta_tf import TFGPTaTokenizer

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
	import sys

	UpperCAmelCase_					:		Tuple           = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from __future__ import annotations

# Define the Gravitational Constant G and the function
UpperCAmelCase_					:		Union[str, Any]           = 6.6_743E-11  # unit of G : m^3 * kg^-1 * s^-2

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float      ,     __magic_name__   :    float   )						->     dict[str, float]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      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:
		UpperCamelCase	:Optional[int]		=      GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
		return {"force": force}
	elif mass_a == 0:
		UpperCamelCase	:Any		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_1": mass_a}
	elif mass_a == 0:
		UpperCamelCase	:Optional[int]		=      (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
		return {"mass_2": mass_a}
	elif distance == 0:
		UpperCamelCase	:str		=      (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()
