





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase:	Optional[int] = {
    """configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	str = ["""ConvNextFeatureExtractor"""]
			__UpperCamelCase:	Tuple = ["""ConvNextImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Optional[int] = [
			    """CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """ConvNextForImageClassification""",
			    """ConvNextModel""",
			    """ConvNextPreTrainedModel""",
			    """ConvNextBackbone""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Optional[Any] = [
			    """TFConvNextForImageClassification""",
			    """TFConvNextModel""",
			    """TFConvNextPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_convnext import ConvNextFeatureExtractor
						from .image_processing_convnext import ConvNextImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnext import (
						    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextBackbone,
						    ConvNextForImageClassification,
						    ConvNextModel,
						    ConvNextPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
			import sys

			__UpperCamelCase:	Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)





def 						lowerCamelCase__      ( __A    :int	,__A    :float	,__A    :float		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((moles * 0.0_821 * temperature) / (volume)		)		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((moles * 0.0_821 * temperature) / (pressure)		)		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((pressure * volume) / (0.0_821 * moles)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

