



import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase          =       get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')



class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Optional[Any]        =  BartphoTokenizer
       UpperCAmelCase__				:					Any        =  False
       UpperCAmelCase__				:					str        =  True
       def 					snake_case_       (			self       )     ->		str:
          super().setUp()

          UpperCamelCase    :  Any							=	['▁This', '▁is', '▁a', '▁t', 'est']
          UpperCamelCase    :  Optional[int]							=	dict(zip(SCREAMING_SNAKE_CASE_,			range(len(SCREAMING_SNAKE_CASE_       )       )       )       )
          UpperCamelCase    :  List[Any]							=	{'unk_token': '<unk>'}

          UpperCamelCase    :  int							=	os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['monolingual_vocab_file']       )
          with open(self.monolingual_vocab_file,			'w',			encoding='utf-8'       ) as fp:
             for token in vocab_tokens:
                fp.write(F"""{token} {vocab_tokens[token]}\n"""       )

          UpperCamelCase    :  List[str]							=	BartphoTokenizer(SCREAMING_SNAKE_CASE_,			self.monolingual_vocab_file,			**self.special_tokens_map       )
          tokenizer.save_pretrained(self.tmpdirname       )
       def 					snake_case_       (			self,			**SCREAMING_SNAKE_CASE_       )     ->		Dict:
          kwargs.update(self.special_tokens_map       )
          return BartphoTokenizer.from_pretrained(self.tmpdirname,			**SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          UpperCamelCase    :  Optional[Any]							=	'This is a là test'
          UpperCamelCase    :  List[Any]							=	'This is a<unk><unk> test'
          return input_text, output_text






       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	BartphoTokenizer(SCREAMING_SNAKE_CASE_,			self.monolingual_vocab_file,			**self.special_tokens_map       )
          UpperCamelCase    :  Optional[int]							=	'This is a là test'
          UpperCamelCase    :  Any							=	'▁This ▁is ▁a ▁l à ▁t est'.split()
          UpperCamelCase    :  Dict							=	tokenizer.tokenize(SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  str							=	tokens + [tokenizer.unk_token]
          UpperCamelCase    :  int							=	[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_       )








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


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



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		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
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



