




'''simple docstring'''





from math import pow, sqrt




def     __magic_name__       (				*A	:      float     ):






			'''simple docstring'''







			a					=    len(UpperCamelCase__     ) > 0 and all(value > 0.0 for value in values     )
			return result




def     __magic_name__       (				A	:      float,						A	:      float     ):






			'''simple docstring'''







			return (
			    round(sqrt(molar_mass_a / molar_mass_a     ),						6     )
			    if validate(UpperCamelCase__,						UpperCamelCase__     )
			    else ValueError("Input Error: Molar mass values must greater than 0."     )
			)




def     __magic_name__       (				A	:      float,						A	:      float,						A	:      float     ):






			'''simple docstring'''







			return (
			    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a     ),						6     )
			    if validate(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__     )
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."     )
			)




def     __magic_name__       (				A	:      float,						A	:      float,						A	:      float     ):






			'''simple docstring'''







			return (
			    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a     ),						6     )
			    if validate(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__     )
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."     )
			)




def     __magic_name__       (				A	:      float,						A	:      float,						A	:      float     ):






			'''simple docstring'''







			return (
			    round(molar_mass / pow(effusion_rate_a / effusion_rate_a,						2     ),						6     )
			    if validate(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__     )
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."     )
			)






def     __magic_name__       (				A	:      float,						A	:      float,						A	:      float     ):






			'''simple docstring'''







			return (
			    round(pow(effusion_rate_a / effusion_rate_a,						2     ) / molar_mass,						6     )
			    if validate(UpperCamelCase__,						UpperCamelCase__,						UpperCamelCase__     )
			    else ValueError(
			        "Input Error: Molar mass and effusion rate values must greater than 0."     )
			)












from typing import TYPE_CHECKING

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


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

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






