


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




@require_torch
class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							List[str]:
      return FSMTTokenizer.from_pretrained(__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Tuple:
      _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 warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

