from typing import TYPE_CHECKING

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


A__           =							{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""MaskFormerFeatureExtractor"""]
			A__           =							["""MaskFormerImageProcessor"""]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """MaskFormerForInstanceSegmentation""",
			    """MaskFormerModel""",
			    """MaskFormerPreTrainedModel""",
			]
			A__           =							[
			    """MaskFormerSwinBackbone""",
			    """MaskFormerSwinModel""",
			    """MaskFormerSwinPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
			from .configuration_maskformer_swin import MaskFormerSwinConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_maskformer import MaskFormerFeatureExtractor
						from .image_processing_maskformer import MaskFormerImageProcessor
			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_maskformer import (
						    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaskFormerForInstanceSegmentation,
						    MaskFormerModel,
						    MaskFormerPreTrainedModel,
						)
						from .modeling_maskformer_swin import (
						    MaskFormerSwinBackbone,
						    MaskFormerSwinModel,
						    MaskFormerSwinPreTrainedModel,
						)


else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure)




import os
from math import logaa





def  _lowerCamelCase					(   a_				: str = "base_exp.txt"):
			lowerCamelCase						:float						  =     0
			lowerCamelCase						:Optional[int]						  =     0
			for i, line in enumerate(open(os.path.join(os.path.dirname(a_)						,			a_))):
						lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     list(map(a_						,			line.split(''',''')))
						if x * logaa(a_) > largest:
									lowerCamelCase						:List[Any]						  =     x * logaa(a_)
									lowerCamelCase						:Any						  =     i + 1
			return result


if __name__ == "__main__":
			print(solution())



