






"""simple docstring"""



class 					_UpperCAmelCase       :  # Public class to implement a graph




					def __init__(				self     :						Optional[int]    ,						A     :						int    ,						A     :						int    ,						A     :						list[list[bool]]   )  ->  None:
							lowercase_ : Union[str, Any]							       =   row
							lowercase_ : str							       =   col
							lowercase_ : Union[str, Any]							       =   graph




					def   A  (				self     :						Optional[int]    ,						A     :						int    ,						A     :						int    ,						A     :						list[list[bool]]   )  ->  bool:
							return (
							    0 <= i < self.ROW
							    and 0 <= j < self.COL
							    and not visited[i][j]
							    and self.graph[i][j]
							)




					def   A  (				self     :						List[str]    ,						A     :						int    ,						A     :						int    ,						A     :						list[list[bool]]   )  ->  None:
							# Checking all 8 elements surrounding nth element
							lowercase_ : Union[str, Any]							       =   [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
							lowercase_ : str							       =   [-1, 0, 1, -1, 1, -1, 0, 1]
							lowercase_ : Dict							       =   True  # Make those cells visited
							for k in range(8   ):
									if self.is_safe(i + row_nbr[k]    ,						j + col_nbr[k]    ,						A   ):
											self.diffs(i + row_nbr[k]    ,						j + col_nbr[k]    ,						A   )






					def   A  (				self     :						List[str]   )  ->  int:  # And finally, count all islands.
							lowercase_ : Union[str, Any]							       =   [[False for j in range(self.COL   )] for i in range(self.ROW   )]
							lowercase_ : int							       =   0
							for i in range(self.ROW   ):
									for j in range(self.COL   ):
											if visited[i][j] is False and self.graph[i][j] == 1:
													self.diffs(A    ,						A    ,						A   )
													count += 1
							return count











"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       List[str]  				=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''MobileNetV2FeatureExtractor''']
						__A		:       Optional[int]  				=		['''MobileNetV2ImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[Any]  				=		[
						    '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileNetV2ForImageClassification''',
						    '''MobileNetV2ForSemanticSegmentation''',
						    '''MobileNetV2Model''',
						    '''MobileNetV2PreTrainedModel''',
						    '''load_tf_weights_in_mobilenet_v2''',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

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



