'''simple docstring'''






class lowercase_       :  # Public class to implement a graph







   """simple docstring"""

   def __init__(      self      :			Any, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			list[list[bool]]     )  ->			None:
        _A    		=  row
        _A    		=  col
        _A    		=  graph
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			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 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			int, UpperCamelCase__      :			int, UpperCamelCase__      :			list[list[bool]]     )  ->			None:
        # Checking all 8 elements surrounding nth element
        _A    		=  [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
        _A    		=  [-1, 0, 1, -1, 1, -1, 0, 1]
        _A    		=  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      :			Tuple     )  ->			int:  # And finally, count all islands.
        _A    		=  [[False for j in range(self.COL     )] for i in range(self.ROW     )]
        _A    		=  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





"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A     :			Optional[int]  						=			{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			[
       """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MgpstrModel""",
       """MgpstrPreTrainedModel""",
       """MgpstrForSceneTextRecognition""",
   ]

if TYPE_CHECKING:
   from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
   from .processing_mgp_str import MgpstrProcessor
   from .tokenization_mgp_str import MgpstrTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mgp_str import (
          MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
          MgpstrForSceneTextRecognition,
          MgpstrModel,
          MgpstrPreTrainedModel,
      )
else:
   import sys

   _A     :			Tuple  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


