
lowerCamelCase_						: List[Any]         =  [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Dict:
				# Return True if there is node that has not iterated.
				UpperCamelCase_:							List[Any]	=						[False] * len(lowerCamelCase						)
				UpperCamelCase_:							int	=						[s]
				UpperCamelCase_:							Tuple	=						True

				while queue:
								UpperCamelCase_:							str	=						queue.pop(0						)
								for ind in range(len(graph[u]						)						):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(lowerCamelCase						)
																UpperCamelCase_:							str	=						True
																UpperCamelCase_:							Union[str, Any]	=						u

				return visited[t]







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							Any	=						[-1] * (len(lowerCamelCase						))
				UpperCamelCase_:							Tuple	=						0
				UpperCamelCase_:							Union[str, Any]	=						[]
				UpperCamelCase_:							Union[str, Any]	=						[i[:] for i in graph]  # Record original cut, copy.
				while bfs(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							List[str]	=						float("""Inf"""						)
								UpperCamelCase_:							Any	=						sink

								while s != source:
												# Find the minimum value in select path
												UpperCamelCase_:							List[str]	=						min(lowerCamelCase     ,    graph[parent[s]][s]						)
												UpperCamelCase_:							List[str]	=						parent[s]

								max_flow += path_flow
								UpperCamelCase_:							int	=						sink

								while v != source:
												UpperCamelCase_:							List[str]	=						parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												UpperCamelCase_:							str	=						parent[v]

				for i in range(len(lowerCamelCase						)						):
								for j in range(len(graph[0]						)						):
												if graph[i][j] == 0 and temp[i][j] > 0:
																res.append((i, j)						)

				return res


if __name__ == "__main__":
							print(mincut(test_graph, source=0, sink=5))








# 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


lowerCamelCase_						: List[str]         =  {
    """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:
							lowerCamelCase_						: Optional[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

							lowerCamelCase_						: int         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






