



from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge



@total_ordering
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __lt__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						return self[-1] < other[-1]




	def __eq__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						return self[-1] == other[-1]






def     a(      A       :    list       )		->       list:
					"""simple docstring"""





					a					     =   []
					# sort into stacks
					for element in collection:
										a					     =   Stack([element]       )
										a					     =   bisect_left(A ,     A       )
										if i != len(A       ):
															stacks[i].append(A       )
										else:
															stacks.append(A       )

    # use a heap-based merge to merge stack efficiently
					a					     =   merge(*(reversed(A       ) for stack in stacks)       )
					return collection


if __name__ == "__main__":
				_lowercase: List[Any]      			=		input("Enter numbers separated by a comma:\n").strip()
				_lowercase: int      			=		[int(item) for item in user_input.split(",")]
				print(patience_sort(unsorted))





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


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



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						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		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

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