_lowercase:	dict[str, float]       			=		{
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.60_93_44,
    "knot": 1.8_52,
}

_lowercase:	dict[str, float]       			=		{
    "km/h": 1.0,
    "m/s": 0.2_77_77_77_78,
    "mph": 0.6_21_37_11_92,
    "knot": 0.5_39_95_68_03,
}






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case  ):
							if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
														_lowerCAmelCase							=							(
														    F'Incorrect \'from_type\' or \'to_type\' value: {unit_from!r}, {unit_to!r}\n'
														    F'Valid values are: {", ".join(snake_case  )}'
														)
														raise ValueError(snake_case  )
							return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]     , 3  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()







from typing import TYPE_CHECKING

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


_lowercase:	Any       			=		{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


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

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''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

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






