'''simple docstring'''




_UpperCamelCase				:		Optional[Any]       			=		float('inf')

class   _lowercase:



   """simple docstring"""





   def __init__(							self:    Dict			,a:    int	):
    __UpperCAmelCase         =					[0] * n
    __UpperCAmelCase         =					[0] * n
    __UpperCAmelCase         =					[0] * n
    __UpperCAmelCase         =					[[] for _ in range(a	)]





   def 			snake_case	(							self:    int			,a:    Tuple			,a:    List[str]			,a:    int			,a:    Optional[Any]=0	):
    self.adj[a].append([b, len(self.adj[b]	), c, 0]	)
    self.adj[b].append([a, len(self.adj[a]	) - 1, rcap, 0]	)





   def 			snake_case	(							self:    Any			,a:    Optional[int]			,a:    List[str]			,a:    int	):
    if vertex == sink or not flow:
     return flow

    for i in range(self.ptr[vertex]			,len(self.adj[vertex]	)	):
     __UpperCAmelCase         =					self.adj[vertex][i]
     if self.lvl[e[0]] == self.lvl[vertex] + 1:
      __UpperCAmelCase         =					self.depth_first_search(e[0]			,a			,min(a			,e[2] - e[3]	)	)
      if p:
       self.adj[vertex][i][3] += p
       self.adj[e[0]][e[1]][3] -= p
       return p
     __UpperCAmelCase         =					self.ptr[vertex] + 1
    return 0





   def 			snake_case	(							self:    Optional[Any]			,a:    List[str]			,a:    str	):
    __UpperCAmelCase  ,			__UpperCAmelCase         =					0, source
    for l in range(31	):  # noqa: E741  l = 30 maybe faster for random data
     while True:
      __UpperCAmelCase  ,			__UpperCAmelCase         =					[0] * len(self.q	), [0] * len(self.q	)
      __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					0, 1, 1
      while qi < qe and not self.lvl[sink]:
       __UpperCAmelCase         =					self.q[qi]
       qi += 1
       for e in self.adj[v]:
        if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
         __UpperCAmelCase         =					e[0]
         qe += 1
         __UpperCAmelCase         =					self.lvl[v] + 1

      __UpperCAmelCase         =					self.depth_first_search(a			,a			,a	)
      while p:
       flow += p
       __UpperCAmelCase         =					self.depth_first_search(a			,a			,a	)

      if not self.lvl[sink]:
       break

    return flow
_UpperCamelCase				:		Tuple       			=		Dinic(10)
_UpperCamelCase				:		Union[str, Any]       			=		0
_UpperCamelCase				:		Optional[int]       			=		9
for vertex in range(1, 5):
     graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
     graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
     graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))




'''simple docstring'''




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,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



