




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


@require_torch
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           return FSMTTokenizer.from_pretrained(_A       )
       def 					A   (			self		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	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		:			Union[str, Any]     ,       lowercase		:			Any     ,       lowercase		:			str       ):



           '''simple docstring'''

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

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

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










'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


A									=logging.get_logger(__name__)


class     _a						(			__a				):
       def __init__(			self		:			List[str]     ,       *lowercase		:			List[str]     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           warnings.warn(
               '''The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use CLIPImageProcessor instead.'''     ,       lowercase     ,       )
           super().__init__(*lowercase     ,       **lowercase       )




