


'''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	:	int     =     get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__A	:	Dict     =     json.load(f)
@require_torch
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:List[Any]			):
									return FSMTTokenizer.from_pretrained(_UpperCamelCase			)





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:Optional[int]			):
									snake_case_		:							Dict			       =						FSMTForConditionalGeneration.from_pretrained(_UpperCamelCase			).to(_UpperCamelCase			)
									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 		a__   (  self		:List[str]  ,_UpperCamelCase		:str  ,_UpperCamelCase		: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
									snake_case_		:							Any			       =						F'''facebook/wmt19-{pair}'''
									snake_case_		:							Dict			       =						self.get_tokenizer(_UpperCamelCase			)
									snake_case_		:							str			       =						self.get_model(_UpperCamelCase			)

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

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



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
							__A	:	Any     =     input('Enter numbers separated by a comma:\n').strip()
							__A	:	Union[str, Any]     =     [int(item) for item in user_input.split(',')]
							__A	:	Optional[int]     =     int(input('Enter the number to be searched:\n'))
							__A	:	int     =     jump_search(arr, x)
							if res == -1:
														print('Number not found!')
							else:
														print(F'Number {x} is at index {res}')