__magic_name__    		=[
    [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 			__UpperCamelCase     (    A			,				A			,				A			,				A				):
     # Return True if there is node that has not iterated.
     UpperCamelCase__					    =   [False] * len(lowercase_				)
     UpperCamelCase__					    =   [s]
     UpperCamelCase__					    =   True

     while queue:
          UpperCamelCase__					    =   queue.pop(0				)
          for ind in range(len(graph[u]				)				):
               if visited[ind] is False and graph[u][ind] > 0:
                    queue.append(lowercase_				)
                    UpperCamelCase__					    =   True
                    UpperCamelCase__					    =   u

     return visited[t]







def 			__UpperCamelCase     (    A			,				A			,				A				):
     UpperCamelCase__					    =   [-1] * (len(lowercase_				))
     UpperCamelCase__					    =   0
     UpperCamelCase__					    =   []
     UpperCamelCase__					    =   [i[:] for i in graph]  # Record original cut, copy.
     while bfs(lowercase_			,				lowercase_			,				lowercase_			,				lowercase_				):
          UpperCamelCase__					    =   float('''Inf'''				)
          UpperCamelCase__					    =   sink

          while s != source:
               # Find the minimum value in select path
               UpperCamelCase__					    =   min(lowercase_			,				graph[parent[s]][s]				)
               UpperCamelCase__					    =   parent[s]

          max_flow += path_flow
          UpperCamelCase__					    =   sink

          while v != source:
               UpperCamelCase__					    =   parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               UpperCamelCase__					    =   parent[v]

     for i in range(len(lowercase_				)				):
          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))






from typing import TYPE_CHECKING

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


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	A_			:					Tuple    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



