


'''simple docstring'''






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


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




@require_torch
class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							str:
      return FSMTTokenizer.from_pretrained(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[Any]:
      _a      =							FSMTForConditionalGeneration.from_pretrained(__magic_name__  ).to(__magic_name__  )
      if torch_device == "cuda":
           model.half()
      return model




 @parameterized.expand(
     [
         ['en-ru', 2_6.0],
         ['ru-en', 2_2.0],
         ['en-de', 2_2.0],
         ['de-en', 2_9.0],
     ]  )
 @slow
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      # note: this test is not testing the best performance since it only evals a small batch
      # but it should be enough to detect a regression in the output quality
      _a      =							f'facebook/wmt19-{pair}'
      _a      =							self.get_tokenizer(__magic_name__  )
      _a      =							self.get_model(__magic_name__  )

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

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





'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline






@pytest.mark.integration
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
          with pytest.raises(lowerCAmelCase__						):
               requests.request('GET'			,  'https://huggingface.co'						)
          with pytest.raises(requests.exceptions.ConnectTimeout						):
               requests.request('GET'			,  'https://huggingface.co'			,  timeout=1.0						)






@pytest.mark.integration
def 		_A			()				->					Any:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_FAILS						):
          with pytest.raises(requests.exceptions.ConnectionError						):
               requests.request('GET'			,  'https://huggingface.co'						)






def 		_A			()				->					Dict:




     '''simple docstring'''



     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
          with pytest.raises(lowerCAmelCase__						):
               http_head('https://huggingface.co'						)

