



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 List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

          self.register_buffer("watermark_image"				,torch.zeros((62, 62, 4)		)		)
          _UpperCAmelCase 						=       None







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       images[0].height
          _UpperCAmelCase 						=       images[0].width

          _UpperCAmelCase 						=       sample_size or h

          _UpperCAmelCase 						=       min(h / sample_size				,w / sample_size		)
          _UpperCAmelCase						,				_UpperCAmelCase 						=       (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       1024**2, img_w * img_h
          _UpperCAmelCase 						=       (Sa / Sa) ** 0.5
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

          if self.watermark_image_as_pil is None:
             _UpperCAmelCase 						=       self.watermark_image.to(torch.uinta		).cpu().numpy()
             _UpperCAmelCase 						=       Image.fromarray(lowerCamelCase_				,mode="RGBA"		)
             _UpperCAmelCase 						=       watermark_image

          _UpperCAmelCase 						=       self.watermark_image_as_pil.resize(
              (wm_size, wm_size)				,PIL_INTERPOLATION["bicubic"]				,reducing_gap=lowerCamelCase_		)

          for pil_img in images:
             pil_img.paste(lowerCamelCase_				,box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,mask=wm_img.split()[-1]		)

          return images

