




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


lowerCamelCase				:		Any     =   get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 lowerCamelCase				:		Any     =   json.load(f)







@require_torch
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def 				a__ (				self					:  Tuple						,							A_					:  int  )   ->	str:

       """simple docstring"""






       return FSMTTokenizer.from_pretrained(A_  )

 def 				a__ (				self					:  Dict						,							A_					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    FSMTForConditionalGeneration.from_pretrained(A_  ).to(A_  )
       if torch_device == "cuda":
             model.half()
       return model





 @parameterized.expand(
     [
         ['en-ru', 26.0],
         ['ru-en', 22.0],
         ['en-de', 22.0],
         ['de-en', 29.0],
     ]  )
 @slow
 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, Any]						,							A_					:  int  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    f"""facebook/wmt19-{pair}"""
       lowerCamelCase_		     =    self.get_tokenizer(A_  )
       lowerCamelCase_		     =    self.get_model(A_  )

       lowerCamelCase_		     =    bleu_data[pair]['src']
       lowerCamelCase_		     =    bleu_data[pair]['tgt']

       lowerCamelCase_		     =    tokenizer(A_						,							return_tensors='pt'						,							truncation=A_						,							padding='longest'  ).to(A_  )
       lowerCamelCase_		     =    model.generate(
           input_ids=batch.input_ids						,							num_beams=8						,							)
       lowerCamelCase_		     =    tokenizer.batch_decode(
           A_						,							skip_special_tokens=A_						,							clean_up_tokenization_spaces=A_  )
       lowerCamelCase_		     =    calculate_bleu(A_						,							A_  )
       print(A_  )
       self.assertGreaterEqual(scores['bleu']						,							A_  )









lowerCamelCase				:		Tuple     =   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       int	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [False] * len(lowercase     )
      lowerCamelCase_		     =    [s]
      lowerCamelCase_		     =    True

      while queue:
            lowerCamelCase_		     =    queue.pop(0     )
            for ind in range(len(graph[u]     )     ):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(lowercase     )
                        lowerCamelCase_		     =    True
                        lowerCamelCase_		     =    u

      return visited[t]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       Tuple	,							lowercase	:       Tuple     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [-1] * (len(lowercase     ))
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    [i[:] for i in graph]  # Record original cut, copy.
      while bfs(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    float('Inf'     )
            lowerCamelCase_		     =    sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase_		     =    min(lowercase	,							graph[parent[s]][s]     )
                  lowerCamelCase_		     =    parent[s]

            max_flow += path_flow
            lowerCamelCase_		     =    sink

            while v != source:
                  lowerCamelCase_		     =    parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase_		     =    parent[v]

      for i in range(len(lowercase     )     ):
            for j in range(len(graph[0]     )     ):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)     )

      return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))



