






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Dict  )    ->List[str]:




       """simple docstring"""


       a		  =      {}







      def       __lowerCAmelCase					(      self      :						str  )    ->None:




       """simple docstring"""


       print(self.vertex  )
       for i in self.vertex:
        print(__UpperCAmelCase ,					''' -> ''' ,					''' -> '''.join([str(__UpperCAmelCase  ) for j in self.vertex[i]]  )  )







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						int  )    ->None:




       """simple docstring"""


       if from_vertex in self.vertex:
        self.vertex[from_vertex].append(__UpperCAmelCase  )
       else:
        # else make a new vertex
        a		  =      [to_vertex]







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->None:




       """simple docstring"""


       a		  =      [False] * len(self.vertex  )

       # call the recursive helper function
       for i in range(len(self.vertex  )  ):
        if not visited[i]:
         self.dfs_recursive(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						int ,					__UpperCAmelCase      :						list  )    ->None:




       """simple docstring"""


       a		  =      True

       print(__UpperCAmelCase ,					end=''' '''  )

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
        if not visited[i]:
         self.dfs_recursive(__UpperCAmelCase ,					__UpperCAmelCase  )


if __name__ == "__main__":
     UpperCAmelCase__				   =		Graph()
     g.add_edge(0, 1)
     g.add_edge(0, 2)
     g.add_edge(1, 2)
     g.add_edge(2, 0)
     g.add_edge(2, 3)
     g.add_edge(3, 3)

     g.print_graph()
     print("DFS:")
     g.dfs()

     # OUTPUT:
     # 0  ->  1 -> 2
     # 1  ->  2
     # 2  ->  0 -> 3
     # 3  ->  3
     # DFS:
     #  0 1 2 3






"""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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     int   						=		[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
