




SCREAMING_SNAKE_CASE_						:					str			   =       float('''inf''')







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       Tuple						,				A :       Any    )			->			int:






         """simple docstring"""
         a__      =       [0] * n
         a__      =       [0] * n
         a__      =       [0] * n
         a__      =       [[] for _ in range(A    )]

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       int						,				A :       Tuple						,				A :       Dict						,				A :       Tuple=0    )			->			Dict:






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






         """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]						,				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
             a__      =       self.ptr[vertex] + 1
         return 0






     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       Dict						,				A :       Union[str, Any]    )			->			Dict:






         """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(A						,				A						,				A    )
                 while p:
                     flow += p
                     a__      =       self.depth_first_search(A						,				A						,				A    )

                 if not self.lvl[sink]:
                     break

         return flow
SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       Dinic(10)
SCREAMING_SNAKE_CASE_						:					List[Any]			   =       0
SCREAMING_SNAKE_CASE_						:					Union[str, 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))












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import CamembertModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				dtype=torch.float						,				)
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(torch.allclose(output[:, :3, :3]						,				A						,				atol=1E-4    )    )






