
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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       str:
					return FSMTTokenizer.from_pretrained(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			FSMTForConditionalGeneration.from_pretrained(lowercase ).to(lowercase )
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       str:
					# 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
					lowerCamelCase_						=			f'facebook/wmt19-{pair}'
					lowerCamelCase_						=			self.get_tokenizer(lowercase )
					lowerCamelCase_						=			self.get_model(lowercase )

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

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








import warnings

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


__lowerCAmelCase		:				List[Any]       			=		logging.get_logger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Union[str, Any]					,      *__lowerCamelCase				:     Optional[Any]					,      **__lowerCamelCase				:     Dict							)					->     None:
							warnings.warn(
							    "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use DonutImageProcessor instead."					,      __lowerCamelCase					,      )
							super().__init__(*__lowerCamelCase					,      **__lowerCamelCase							)






