from graphs.minimum_spanning_tree_kruskal import kruskal
def  snake_case						()	->	Dict:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   9
						_snake_case						:					Union[str, Any]    =   [
						    [0, 1, 4],
						    [0, 7, 8],
						    [1, 2, 8],
						    [7, 8, 7],
						    [7, 6, 1],
						    [2, 8, 2],
						    [8, 6, 6],
						    [2, 3, 7],
						    [2, 5, 4],
						    [6, 5, 2],
						    [3, 5, 14],
						    [3, 4, 9],
						    [5, 4, 10],
						    [1, 7, 11],
						]

						_snake_case						:					Dict    =   kruskal(__lowercase ,      __lowercase						)

						_snake_case						:					List[Any]    =   [
						    [7, 6, 1],
						    [2, 8, 2],
						    [6, 5, 2],
						    [0, 1, 4],
						    [2, 5, 4],
						    [2, 3, 7],
						    [0, 7, 8],
						    [3, 4, 9],
						]

						assert sorted(__lowercase						) == sorted(__lowercase						)
# 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


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    '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

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)