





from typing import TYPE_CHECKING

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


lowercase_					     =	{
    """configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
    """tokenization_roc_bert""": ["""RoCBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      pass

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RoCBertForCausalLM""",
          """RoCBertForMaskedLM""",
          """RoCBertForMultipleChoice""",
          """RoCBertForPreTraining""",
          """RoCBertForQuestionAnswering""",
          """RoCBertForSequenceClassification""",
          """RoCBertForTokenClassification""",
          """RoCBertLayer""",
          """RoCBertModel""",
          """RoCBertPreTrainedModel""",
          """load_tf_weights_in_roc_bert""",
      ]

if TYPE_CHECKING:
      from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
      from .tokenization_roc_bert import RoCBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            raise OptionalDependencyNotAvailable()

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roc_bert import (
                ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoCBertForCausalLM,
                RoCBertForMaskedLM,
                RoCBertForMultipleChoice,
                RoCBertForPreTraining,
                RoCBertForQuestionAnswering,
                RoCBertForSequenceClassification,
                RoCBertForTokenClassification,
                RoCBertLayer,
                RoCBertModel,
                RoCBertPreTrainedModel,
                load_tf_weights_in_roc_bert,
            )


else:
      import sys

      lowercase_					     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








class        SCREAMING_SNAKE_CASE     :  # Public class to implement a graph


   def __init__(					self    :       int    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						row
     lowercase__			    =						col
     lowercase__			    =						graph


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  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  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """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]    ,  a ):
         self.diffs(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     int:  # 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(a    ,  a    ,  a )
           count += 1
     return count


