
"""simple docstring"""




# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


a		:       Dict     						=       re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')







@total_ordering
@dataclass
class     lowercase:
     __snake_case:	str
     __snake_case:	Optional[str]						=     None
     __snake_case:	Optional[Union[str, int]]						=     None
     __snake_case:	Optional[Union[str, int]]						=     None
     __snake_case:	Optional[Union[str, int]]						=     None



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__	,     a__	,     a__		=			_str_to_version_tuple(self.version_str   )



     def __repr__(							self   )		->     Union[str, Any]:







         """simple docstring"""



         return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'



     @property
     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         return self.major, self.minor, self.patch



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             return Version(__SCREAMING_SNAKE_CASE   )
         elif isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
             return other
         raise TypeError(f'{other} (type {type(__SCREAMING_SNAKE_CASE   )}) cannot be compared to version.'   )



     def __eq__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         try:
             a__		=			self._validate_operand(__SCREAMING_SNAKE_CASE   )
         except (TypeError, ValueError):
             return False
         else:
             return self.tuple == other.tuple



     def __lt__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			self._validate_operand(__SCREAMING_SNAKE_CASE   )
         return self.tuple < other.tuple



     def __hash__(							self   )		->     Union[str, Any]:







         """simple docstring"""



         return hash(_version_tuple_to_str(self.tuple   )   )



     @classmethod
     def 			lowercase__  (							cls     ,				__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         a__		=			{f.name for f in dataclasses.fields(cls   )}
         return cls(**{k: v for k, v in dic.items() if k in field_names}   )




     def 			lowercase__  (							self   )		->     str:







         """simple docstring"""



         return self.version_str




def     __magic_name__		(					UpperCamelCase     :		Dict     )     ->						List[Any]:
    a__		=			_VERSION_REG.match(UpperCamelCase     )
    if not res:
        raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'     )
    return tuple(int(UpperCamelCase     ) for v in [res.group('major'     ), res.group('minor'     ), res.group('patch'     )]     )



def     __magic_name__		(					UpperCamelCase     :		Optional[Any]     )     ->						str:
    return ".".join(str(UpperCamelCase     ) for v in version_tuple     )




"""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		:       Any     						=       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     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

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

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


