




from typing import TYPE_CHECKING

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


lowerCAmelCase_						=			{
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			['PerceiverFeatureExtractor']
 lowerCAmelCase_						=			['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_						=			[
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





class 						_A :  # Public class to implement a graph






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   None:






       """simple docstring"""







       lowercase :       Tuple       =			row
       lowercase :       Union[str, Any]       =			col
       lowercase :       int       =			graph






  def  __a      (  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   bool:






       """simple docstring"""







       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )






  def  __a      (  self		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     list[list[bool]]				)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       lowercase :       Dict       =			[-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.






       """simple docstring"""







       lowercase :       List[str]       =			[[False for j in range(self.COL				)] for i in range(self.ROW				)]
       lowercase :       Optional[Any]       =			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