
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


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





@require_torch
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[str] ):
					return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: int ):
					__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 				UpperCamelCase_  (       self					: Any							,A					: Union[str, Any]							,A					: List[str] ):
					# 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
					__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_ )





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def 		UpperCamelCase	(   )	->     tuple[list[int], int]:
   UpperCamelCase    :  int							=	[randint(-1000       ,     1000	) for i in range(10	)]
   UpperCamelCase    :  Dict							=	randint(-5000       ,     5000	)
   return (arr, r)


__UpperCAmelCase          =       make_dataset()
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, ...]:
   for triplet in permutations(snake_case__       ,     3	):
      if sum(snake_case__	) == target:
         return tuple(sorted(snake_case__	)	)
   return (0, 0, 0)
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, int, int]:
   arr.sort()
   UpperCamelCase    :  List[str]							=	len(snake_case__	)
   for i in range(n - 1	):
      UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	i + 1, n - 1
      while left < right:
         if arr[i] + arr[left] + arr[right] == target:
            return (arr[i], arr[left], arr[right])
         elif arr[i] + arr[left] + arr[right] < target:
            left += 1
         elif arr[i] + arr[left] + arr[right] > target:
            right -= 1
   return (0, 0, 0)
def 		UpperCamelCase	(   )	->     tuple[float, float]:
   UpperCamelCase    :  Any							=	'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
   UpperCamelCase    :  Optional[Any]							=	'\ntriplet_sum1(*dataset)\n'
   UpperCamelCase    :  Dict							=	'\ntriplet_sum2(*dataset)\n'
   UpperCamelCase    :  Optional[int]							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   UpperCamelCase    :  Any							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   return (min(snake_case__	), min(snake_case__	))


if __name__ == "__main__":
    from doctest import testmod

    testmod()
    __UpperCAmelCase          =       solution_times()
    print(F"""The time for naive implementation is {times[0]}.""")
    print(F"""The time for optimized implementation is {times[1]}.""")



