



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



class 		_lowercase		:

   def __init__(   self      :							Any				, __lowerCAmelCase      :							str				)							->					Optional[Any]:


    """simple docstring"""






    a							=		[0] * n
    a							=		[0] * n
    a							=		[0] * n
    a							=		[[] for _ in range(__lowerCAmelCase				)]

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Tuple=0				)							->					str:


    """simple docstring"""






    self.adj[a].append([b, len(self.adj[b]				), c, 0]				)
    self.adj[b].append([a, len(self.adj[a]				) - 1, rcap, 0]				)

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[int]				)							->					Tuple:


    """simple docstring"""






    if vertex == sink or not flow:
     return flow

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




   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							List[str]				)							->					str:


    """simple docstring"""






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

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

      if not self.lvl[sink]:
       break

    return flow
A_		:  Tuple										=  Dinic(10)
A_		:  Dict										=  0
A_		:  Optional[Any]										=  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_torch_available, is_vision_available


__A					=			{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A					=			[
			    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DonutSwinModel',
			    'DonutSwinPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A					=			['DonutFeatureExtractor']
			__A					=			['DonutImageProcessor']


if TYPE_CHECKING:
			from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
			from .processing_donut import DonutProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_donut_swin import (
						    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DonutSwinModel,
						    DonutSwinPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_donut import DonutFeatureExtractor
						from .image_processing_donut import DonutImageProcessor

else:
			import sys

			__A					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



