






def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,						lowercase__						:       Optional[int]     ,						lowercase__						:       Optional[Any]       )		->    float:


				'''simple docstring'''




				return round(float(moles / volume       ) * nfactor       )





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]     ,						lowercase__						:       Optional[int]     ,						lowercase__						:       Tuple       )		->    float:


				'''simple docstring'''




				return round(float((moles * 0.0821 * temperature) / (volume)       )       )





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,						lowercase__						:       Optional[int]     ,						lowercase__						:       int       )		->    float:


				'''simple docstring'''




				return round(float((moles * 0.0821 * temperature) / (pressure)       )       )





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]     ,						lowercase__						:       Tuple     ,						lowercase__						:       Optional[int]       )		->    float:


				'''simple docstring'''




				return round(float((pressure * volume) / (0.0821 * moles)       )       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    [
			    "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MCTCTForCTC",
			    "MCTCTModel",
			    "MCTCTPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
			from .feature_extraction_mctct import MCTCTFeatureExtractor
			from .processing_mctct import MCTCTProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
			import sys

			_UpperCAmelCase			:		List[str]   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





