



from typing import TYPE_CHECKING

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


__snake_case     :List[str]			       ={
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case     :str			       =['MobileViTFeatureExtractor']
				__snake_case     :List[Any]			       =['MobileViTImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case     :Optional[Any]			       =[
				    'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MobileViTForImageClassification',
				    'MobileViTForSemanticSegmentation',
				    'MobileViTModel',
				    'MobileViTPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__snake_case     :Optional[int]			       =[
				    'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFMobileViTForImageClassification',
				    'TFMobileViTForSemanticSegmentation',
				    'TFMobileViTModel',
				    'TFMobileViTPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_mobilevit import MobileViTFeatureExtractor
								from .image_processing_mobilevit import MobileViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevit import (
								    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTForImageClassification,
								    MobileViTForSemanticSegmentation,
								    MobileViTModel,
								    MobileViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_mobilevit import (
								    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFMobileViTForImageClassification,
								    TFMobileViTForSemanticSegmentation,
								    TFMobileViTModel,
								    TFMobileViTPreTrainedModel,
								)


else:
				import sys

				__snake_case     :List[Any]			       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




from bisect import bisect
from itertools import accumulate






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[Any]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    sorted(zip(lowerCAmelCase__			,	lowerCAmelCase__				)			,	key=lambda lowerCAmelCase__				: x[0] / x[1]			,	reverse=lowerCAmelCase__				)
							A						,   A      						=    [i[0] for i in r], [i[1] for i in r]
							A      						=    list(accumulate(lowerCAmelCase__				)				)
							A      						=    bisect(lowerCAmelCase__			,	lowerCAmelCase__				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()