






'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	list							)			->			list:





						"""simple docstring"""
						for i in range(len(a							) - 1						,			0						,			-1							):
												lowercase_      :   Any        =			False

												for j in range(a						,			0						,			-1							):
																		if unsorted[j] < unsorted[j - 1]:
																								lowercase_      :   Any        =			unsorted[j - 1], unsorted[j]
																								lowercase_      :   int        =			True

												for j in range(a							):
																		if unsorted[j] > unsorted[j + 1]:
																								lowercase_      :   Union[str, Any]        =			unsorted[j + 1], unsorted[j]
																								lowercase_      :   Optional[Any]        =			True

												if not swapped:
																		break
						return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A:      Union[str, Any]  				=				input("Enter numbers separated by a comma:\n").strip()
							A:      Tuple  				=				[int(item) for item in user_input.split(",")]
							print(f"""{cocktail_shaker_sort(unsorted) = }""")








'''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:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# 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_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
