



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case		:str   	=  {
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:Any   	=  [
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:List[str]   	=  [
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:List[Any]   	=  [
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__snake_case		:str   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










__snake_case		:str   	=  [
    [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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    # Return True if there is node that has not iterated.
    __a		 =       [False] * len(_UpperCAmelCase       )
    __a		 =       [s]
    __a		 =       True

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

    return visited[t]





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

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

        max_flow += path_flow
        __a		 =       sink

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

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





