






'''simple docstring'''

from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
A:      List[Any]  				=				1_0






def 	_UpperCAmelCase    (  a		:	int						,			a		:	int						,			a		:	list[int]						,			a		:	int							)			->			int:





						"""simple docstring"""
						for i in range(a						,			a							):
												if array[i] == target:
																		return i
						return -1






def 	_UpperCAmelCase    (  a		:	list[int]						,			a		:	int							)			->			int:





						"""simple docstring"""
						lowercase_      :   List[str]        =			0
						lowercase_      :   Any        =			len(a							)
						while left <= right:
												if right - left < precision:
																		return lin_search(a						,			a						,			a						,			a							)

												lowercase_      :   Optional[Any]        =			(left + right) // 3 + 1
												lowercase_      :   str        =			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		lowercase_      :   List[str]        =			one_third - 1
												elif array[two_third] < target:
																		lowercase_      :   Optional[int]        =			two_third + 1

												else:
																		lowercase_      :   Optional[int]        =			one_third + 1
																		lowercase_      :   Any        =			two_third - 1
						else:
												return -1






def 	_UpperCAmelCase    (  a		:	int						,			a		:	int						,			a		:	list[int]						,			a		:	int							)			->			int:





						"""simple docstring"""
						if left < right:
												if right - left < precision:
																		return lin_search(a						,			a						,			a						,			a							)
												lowercase_      :   Tuple        =			(left + right) // 3 + 1
												lowercase_      :   str        =			2 * (left + right) // 3 + 1

												if array[one_third] == target:
																		return one_third
												elif array[two_third] == target:
																		return two_third

												elif target < array[one_third]:
																		return rec_ternary_search(a						,			one_third - 1						,			a						,			a							)
												elif array[two_third] < target:
																		return rec_ternary_search(two_third + 1						,			a						,			a						,			a							)
												else:
																		return rec_ternary_search(one_third + 1						,			two_third - 1						,			a						,			a							)
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A:      Union[str, Any]  				=				input("Enter numbers separated by comma:\n").strip()
						A:      Optional[Any]  				=				[int(item.strip()) for item in user_input.split(",")]
						assert collection == sorted(collection), f"List must be ordered.\n{collection}."
						A:      Dict  				=				int(input("Enter the number to be found in the list:\n").strip())
						A:      int  				=				ite_ternary_search(collection, target)
						A:      List[str]  				=				rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(f"""Iterative search: {target} found at positions: {resulta}""")
												print(f"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("Not found")








'''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       )
