




from typing import TYPE_CHECKING

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


UpperCamelCase__		=						{'configuration_vilt': ['VILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViltConfig']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__		=						['ViltFeatureExtractor']
							UpperCamelCase__		=						['ViltImageProcessor']
							UpperCamelCase__		=						['ViltProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__		=						[
							    'VILT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ViltForImageAndTextRetrieval',
							    'ViltForImagesAndTextClassification',
							    'ViltForTokenClassification',
							    'ViltForMaskedLM',
							    'ViltForQuestionAnswering',
							    'ViltLayer',
							    'ViltModel',
							    'ViltPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vilt import ViltFeatureExtractor
														from .image_processing_vilt import ViltImageProcessor
														from .processing_vilt import ViltProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vilt import (
														    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViltForImageAndTextRetrieval,
														    ViltForImagesAndTextClassification,
														    ViltForMaskedLM,
														    ViltForQuestionAnswering,
														    ViltForTokenClassification,
														    ViltLayer,
														    ViltModel,
														    ViltPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase__		=						_LazyModule(__name__, globals()['__file__'], _import_structure)






UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_009_842_073,
    "short-ton": 0.0_011_023_122,
    "pound": 2.2_046_244_202,
    "stone": 0.1_574_731_728,
    "ounce": 35.273_990_723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652e26,
}

UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_016.04_608,
    "short-ton": 907.184,
    "pound": 0.453_592,
    "stone": 6.35_029,
    "ounce": 0.0_283_495,
    "carrat": 0.0_002,
    "atomic-mass-unit": 1.660540199e-27,
}






def  snake_case (							A						,       A						,       A      )							->							float:




	'''simple docstring'''


	if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
		lowerCamelCase_							:		Union[str, Any]							       =							(
		    f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
		    f"Supported values are: {', '.join(A      )}"
		)
		raise ValueError(A      )
	return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
