






snake_case_    						=				float('''inf''')


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a):
         lowercase__					:     List[Any]   = [0] * n
         lowercase__					:     Any   = [0] * n
         lowercase__					:     Tuple   = [0] * n
         lowercase__					:     int   = [[] for _ in range(__a)]





    def    snake_case_       (   self		,     a		,     a		,     a		,     a=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		,     a		,     a		,     a):





         if vertex == sink or not flow:
              return flow

         for i in range(self.ptr[vertex]		,     len(self.adj[vertex])):
              lowercase__					:     List[Any]   = self.adj[vertex][i]
              if self.lvl[e[0]] == self.lvl[vertex] + 1:
                   lowercase__					:     Optional[int]   = 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
              lowercase__					:     int   = self.ptr[vertex] + 1
         return 0





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

                   lowercase__					:     Tuple   = self.depth_first_search(__a		,     __a		,     __a)
                   while p:
                        flow += p
                        lowercase__					:     Optional[Any]   = self.depth_first_search(__a		,     __a		,     __a)

                   if not self.lvl[sink]:
                        break

         return flow



snake_case_    						=				Dinic(10)
snake_case_    						=				0
snake_case_    						=				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))




from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
         return True





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
         lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
         lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
         lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

         lowercase__					:     Any   = tokenizer.tokenize(a)
         self.assertListEqual(a		,     a)

         lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]

         lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)

         lowercase__					:     Optional[Any]   = tokenizer.decode(a)
         self.assertEqual(a		,     a)

