



from typing import TYPE_CHECKING

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


__snake_case		:List[str]   	=  {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:Optional[Any]   	=  ['''XLMRobertaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:str   	=  ['''XLMRobertaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:List[str]   	=  [
	    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XLMRobertaForCausalLM''',
	    '''XLMRobertaForMaskedLM''',
	    '''XLMRobertaForMultipleChoice''',
	    '''XLMRobertaForQuestionAnswering''',
	    '''XLMRobertaForSequenceClassification''',
	    '''XLMRobertaForTokenClassification''',
	    '''XLMRobertaModel''',
	    '''XLMRobertaPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:Any   	=  [
	    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXLMRobertaForCausalLM''',
	    '''TFXLMRobertaForMaskedLM''',
	    '''TFXLMRobertaForMultipleChoice''',
	    '''TFXLMRobertaForQuestionAnswering''',
	    '''TFXLMRobertaForSequenceClassification''',
	    '''TFXLMRobertaForTokenClassification''',
	    '''TFXLMRobertaModel''',
	    '''TFXLMRobertaPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case		:List[Any]   	=  [
	    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FlaxXLMRobertaForMaskedLM''',
	    '''FlaxXLMRobertaForCausalLM''',
	    '''FlaxXLMRobertaForMultipleChoice''',
	    '''FlaxXLMRobertaForQuestionAnswering''',
	    '''FlaxXLMRobertaForSequenceClassification''',
	    '''FlaxXLMRobertaForTokenClassification''',
	    '''FlaxXLMRobertaModel''',
	    '''FlaxXLMRobertaPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_xlm_roberta import (
	    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XLMRobertaConfig,
	    XLMRobertaOnnxConfig,
	)

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta import XLMRobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMRobertaForCausalLM,
		    XLMRobertaForMaskedLM,
		    XLMRobertaForMultipleChoice,
		    XLMRobertaForQuestionAnswering,
		    XLMRobertaForSequenceClassification,
		    XLMRobertaForTokenClassification,
		    XLMRobertaModel,
		    XLMRobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm_roberta import (
		    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMRobertaForCausalLM,
		    TFXLMRobertaForMaskedLM,
		    TFXLMRobertaForMultipleChoice,
		    TFXLMRobertaForQuestionAnswering,
		    TFXLMRobertaForSequenceClassification,
		    TFXLMRobertaForTokenClassification,
		    TFXLMRobertaModel,
		    TFXLMRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xlm_roberta import (
		    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxXLMRobertaForCausalLM,
		    FlaxXLMRobertaForMaskedLM,
		    FlaxXLMRobertaForMultipleChoice,
		    FlaxXLMRobertaForQuestionAnswering,
		    FlaxXLMRobertaForSequenceClassification,
		    FlaxXLMRobertaForTokenClassification,
		    FlaxXLMRobertaModel,
		    FlaxXLMRobertaPreTrainedModel,
		)

else:
	import sys

	__snake_case		:Any   	=  _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))





