





"""simple docstring"""



from typing import TYPE_CHECKING

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


a_					=     {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_					=     [
           """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SwinForImageClassification""",
           """SwinForMaskedImageModeling""",
           """SwinModel""",
           """SwinPreTrainedModel""",
           """SwinBackbone""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_					=     [
           """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFSwinForImageClassification""",
           """TFSwinForMaskedImageModeling""",
           """TFSwinModel""",
           """TFSwinPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_swin import (
                  SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SwinBackbone,
                  SwinForImageClassification,
                  SwinForMaskedImageModeling,
                  SwinModel,
                  SwinPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_swin import (
                  TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFSwinForImageClassification,
                  TFSwinForMaskedImageModeling,
                  TFSwinModel,
                  TFSwinPreTrainedModel,
              )

else:
       import sys

       a_					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











"""simple docstring"""



from collections import namedtuple

a_					=     namedtuple("""from_to""", """from_ to""")

a_					=     {
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1000),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00454, 264.172),
    """cubicyard""": from_to(0.76455, 1.30795),
    """cubicfoot""": from_to(0.028, 35.3147),
    """cup""": from_to(0.000236588, 4226.75),
}







def 						__lowercase	(  snake_case_    :    float							,snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    if from_type not in METRIC_CONVERSION:
        raise ValueError(
            F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
            + ''', '''.join(snake_case_		)		)
    if to_type not in METRIC_CONVERSION:
        raise ValueError(
            F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
            + ''', '''.join(snake_case_		)		)
    return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
       import doctest

       doctest.testmod()




