




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







@require_torch
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def     a__			(    self :  Union[str, Any]						,							A_ :  Tuple  )       ->     Optional[int]:

       """simple docstring"""






       return FSMTTokenizer.from_pretrained(__lowerCamelCase  )

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

       """simple docstring"""






       lowerCamelCase_							=   FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase  ).to(__lowerCamelCase  )
       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 :  Dict						,							A_ :  Any						,							A_ :  Optional[int]  )       ->     Optional[Any]:

       """simple docstring"""






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

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

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









from __future__ import annotations

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	dict     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_					,    lowerCamelCase_							=   set(lowercase					), [start]

      while stack:
            lowerCamelCase_							=   stack.pop()
            explored.add(lowercase					)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]					):
                  if adj not in explored:
                        stack.append(lowercase					)
      return explored


lowerCamelCase    :		int			   =       {
    "A": ["B", "C", "D"],
    "B": ["A", "D", "E"],
    "C": ["A", "F"],
    "D": ["B", "D"],
    "E": ["B", "F"],
    "F": ["C", "E", "G"],
    "G": ["F"],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(depth_first_search(G, "A"))



