

'''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


_lowerCamelCase      					=     get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
					_lowerCamelCase      					=     json.load(f)
@require_torch
class 					_snake_case		(unittest.TestCase):






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				return FSMTTokenizer.from_pretrained(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      int						=  FSMTForConditionalGeneration.from_pretrained(_snake_case     ).to(_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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				# 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_     :      Dict						=  f'''facebook/wmt19-{pair}'''
				UpperCAmelCase_     :      Union[str, Any]						=  self.get_tokenizer(_snake_case     )
				UpperCAmelCase_     :      int						=  self.get_model(_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  bleu_data[pair]["src"]
				UpperCAmelCase_     :      Any						=  bleu_data[pair]["tgt"]

				UpperCAmelCase_     :      List[Any]						=  tokenizer(_snake_case			,return_tensors="pt"			,truncation=_snake_case			,padding="longest"     ).to(_snake_case     )
				UpperCAmelCase_     :      List[str]						=  model.generate(
				    input_ids=batch.input_ids			,num_beams=8			,)
				UpperCAmelCase_     :      List[str]						=  tokenizer.batch_decode(
				    _snake_case			,skip_special_tokens=_snake_case			,clean_up_tokenization_spaces=_snake_case     )
				UpperCAmelCase_     :      int						=  calculate_bleu(_snake_case			,_snake_case     )
				print(_snake_case     )
				self.assertGreaterEqual(scores["bleu"]			,_snake_case     )









import math
from collections.abc import Iterator
from itertools import takewhile






def     __a					(		__UpperCAmelCase       ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,  int(math.sqrt(__UpperCAmelCase       ) + 1       )							,  6       ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def     __a					(		):
 a__												= 2
 while True:
  if is_prime(__UpperCAmelCase       ):
   yield num
  num += 1






def     __a					(		__UpperCAmelCase = 200_0000       ):
 return sum(takewhile(lambda __UpperCAmelCase       : x < n							,  prime_generator()       )       )


if __name__ == "__main__":
     print(f'{solution() = }')





