


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


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

@require_torch
class 						A__							(		unittest.TestCase  ):




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      Tuple  )							->     Tuple:


										"""simple docstring"""





										return FSMTTokenizer.from_pretrained(_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      Dict  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      Any				,    _a					:      Any  )							->     Tuple:


										"""simple docstring"""





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

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

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



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )