



'''simple docstring'''





import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowercase_		       =  logging.getLogger()

lowercase_		       =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class __A			(  A						):



 '''simple docstring'''







 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      os.makedirs(A ,   exist_ok=A       )
      _a      =  {'''source''': '''What is love ?''', '''target''': '''life'''}
      _a      =  {'''train''': 12, '''val''': 2, '''test''': 2}
      for split in ["train", "test", "val"]:
           for field in ["source", "target"]:
                _a      =  '''\n'''.join([contents[field]] * n_lines[split]       )
                with open(os.path.join(A ,   f'''{split}.{field}'''       ) ,   '''w'''       ) as f:
                     f.write(A       )



 def 					a__  (self ,   A ,   A = "pytorch"       )    ->     int:


      """simple docstring"""



      _a      =  self.get_auto_remove_tmp_dir()
      _a      =  os.path.join(A ,   '''output'''       )
      _a      =  os.path.join(A ,   '''data'''       )
      self._create_dummy_data(data_dir=A       )

      _a      =  f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

      if gpus > 0:
           testargs.append(f'''--gpus={gpus}'''       )
           if is_apex_available():
                testargs.append('''--fp16'''       )
      else:
           testargs.append('''--gpus=0'''       )
           testargs.append('''--distributed_backend=ddp_cpu'''       )
           testargs.append('''--num_processes=2'''       )

      _a      =  [sys.executable, str(Path(finetune_rag.__file__       ).resolve()       )] + testargs
      execute_subprocess_async(A ,   env=self.get_env()       )

      _a      =  os.path.join(A ,   '''metrics.json'''       )
      with open(A       ) as f:
           _a      =  json.load(A       )
      return result



 @require_torch_gpu
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  self._run_finetune(gpus=1       )
      self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,   0.2       )



 @require_torch_multi_gpu
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  self._run_finetune(gpus=2       )
      self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,   0.2       )



 @require_torch_gpu
 @require_ray
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  self._run_finetune(gpus=1 ,   distributed_retriever='''ray'''       )
      self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,   0.2       )



 @require_torch_multi_gpu
 @require_ray
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  self._run_finetune(gpus=1 ,   distributed_retriever='''ray'''       )
      self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] ,   0.2       )








'''simple docstring'''





import math






class __A			:



 '''simple docstring'''







 def __init__(self ,   A=0       )    ->     Dict:  # a graph with Node 0,1,...,N-1


      """simple docstring"""



      _a      =  n
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # adjacency matrix for weight
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # dp[i][j] stores minimum distance from i to j



 def 					a__  (self ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  w



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      for k in range(0 ,   self.n       ):
           for i in range(0 ,   self.n       ):
                for j in range(0 ,   self.n       ):
                     _a      =  min(self.dp[i][j] ,   self.dp[i][k] + self.dp[k][j]       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      return self.dp[u][v]


if __name__ == "__main__":
  lowercase_		       =  Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)



