
"""simple docstring"""





_a		       =							[
    [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  ):
   UpperCAmelCase_					:					List[str]			     =    [False] * len(__lowerCAmelCase  )
   UpperCAmelCase_					:					List[Any]			     =    [s]
   UpperCAmelCase_					:					Optional[Any]			     =    True

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

   return visited[t]







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Tuple			     =    [-1] * (len(__lowerCAmelCase  ))
   UpperCAmelCase_					:					Optional[Any]			     =    0
   UpperCAmelCase_					:					Dict			     =    []
   UpperCAmelCase_					:					List[Any]			     =    [i[:] for i in graph]  # Record original cut, copy.
   while bfs(__lowerCAmelCase,		__lowerCAmelCase,		__lowerCAmelCase,		__lowerCAmelCase  ):
      UpperCAmelCase_					:					Union[str, Any]			     =    float("Inf"  )
      UpperCAmelCase_					:					int			     =    sink

      while s != source:
         # Find the minimum value in select path
         UpperCAmelCase_					:					Dict			     =    min(__lowerCAmelCase,		graph[parent[s]][s]  )
         UpperCAmelCase_					:					Optional[int]			     =    parent[s]

      max_flow += path_flow
      UpperCAmelCase_					:					Optional[Any]			     =    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))




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,
)


_A   =     {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["XGLMTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["XGLMTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XGLMForCausalLM",
			    "XGLMModel",
			    "XGLMPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxXGLMForCausalLM",
			    "FlaxXGLMModel",
			    "FlaxXGLMPreTrainedModel",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXGLMForCausalLM",
			    "TFXGLMModel",
			    "TFXGLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure)



