




'''simple docstring'''







from typing import TYPE_CHECKING

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


A     ={
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =['LongformerTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'LongformerForMaskedLM',
       'LongformerForMultipleChoice',
       'LongformerForQuestionAnswering',
       'LongformerForSequenceClassification',
       'LongformerForTokenClassification',
       'LongformerModel',
       'LongformerPreTrainedModel',
       'LongformerSelfAttention',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFLongformerForMaskedLM',
       'TFLongformerForMultipleChoice',
       'TFLongformerForQuestionAnswering',
       'TFLongformerForSequenceClassification',
       'TFLongformerForTokenClassification',
       'TFLongformerModel',
       'TFLongformerPreTrainedModel',
       'TFLongformerSelfAttention',
   ]


if TYPE_CHECKING:
   from .configuration_longformer import (
       LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       LongformerConfig,
       LongformerOnnxConfig,
   )
   from .tokenization_longformer import LongformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_longformer_fast import LongformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longformer import (
          LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongformerForMaskedLM,
          LongformerForMultipleChoice,
          LongformerForQuestionAnswering,
          LongformerForSequenceClassification,
          LongformerForTokenClassification,
          LongformerModel,
          LongformerPreTrainedModel,
          LongformerSelfAttention,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_longformer import (
          TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLongformerForMaskedLM,
          TFLongformerForMultipleChoice,
          TFLongformerForQuestionAnswering,
          TFLongformerForSequenceClassification,
          TFLongformerForTokenClassification,
          TFLongformerModel,
          TFLongformerPreTrainedModel,
          TFLongformerSelfAttention,
      )

else:
   import sys

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








'''simple docstring'''

class   __lowerCAmelCase						:  # Public class to implement a graph





      '''simple docstring'''






      def __init__(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= row
             lowercase__      			= col
             lowercase__      			= graph



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowercase__      			= [-1, 0, 1, -1, 1, -1, 0, 1]
             lowercase__      			= True  # Make those cells visited
             for k in range(8 ):
                    if self.is_safe(i + row_nbr[k]   ,						j + col_nbr[k]   ,						UpperCamelCase ):
                           self.diffs(i + row_nbr[k]   ,						j + col_nbr[k]   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Dict ):  # And finally, count all islands.

             '''simple docstring'''







             lowercase__      			= [[False for j in range(self.COL )] for i in range(self.ROW )]
             lowercase__      			= 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(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                                  count += 1
             return count




