
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        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		return FSMTTokenizer.from_pretrained(snake_case_       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       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
		lowercase       =    F'''facebook/wmt19-{pair}'''
		lowercase       =    self.get_tokenizer(snake_case_       )
		lowercase       =    self.get_model(snake_case_       )

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

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








'''simple docstring'''




import math
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     return math.sqrt(__lowercase       ) * math.sqrt(__lowercase       ) == num
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     _UpperCAmelCase     							=   0
     _UpperCAmelCase     							=   n
     while left <= right:
          _UpperCAmelCase     							=   (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               _UpperCAmelCase     							=   mid - 1
          else:
               _UpperCAmelCase     							=   mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()




