

from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A__						=							(3, 9, -11, 0, 7, 5, 1, -1)
A__						=							(4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class 				__UpperCamelCase				:
	_lowercase  :   int
	_lowercase  :   Node | None




class 				__UpperCamelCase				:







	def __init__(   self: Tuple  , __UpperCamelCase: Iterable[int]      ):

						'''simple docstring'''





						__magic_name__							=    None
						for i in sorted(__UpperCamelCase  , reverse=__UpperCamelCase      ):
											__magic_name__							=    Node(__UpperCamelCase  , self.head      )







	def __iter__(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.head
						while node:
											yield node.data
											__magic_name__							=    node.next_node







	def __len__(   self: List[str]      ):

						'''simple docstring'''





						return sum(1 for _ in self      )







	def __str__(   self: int      ):

						'''simple docstring'''





						return " -> ".join([str(__UpperCamelCase      ) for node in self]      )




def 					_lowercase     (				a_					,a_				)				->	SortedLinkedList:




					'''simple docstring'''







					return SortedLinkedList(list(a_				) + list(a_				)				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						A__						=							SortedLinkedList
						print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




