





'''simple docstring'''






from __future__ import annotations

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

A__				:   List[Any]      =(3, 9, -11, 0, 7, 5, 1, -1)
A__				:   str      =(4, 6, 2, 0, 8, 10, 3, -2)




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




class  UpperCAmelCase     :




      def __init__(			self			: List[str]			,			__snake_case			: Iterable[int]  )				->      None:
            _lowerCAmelCase							     =							None
            for i in sorted(__snake_case			,			reverse=__snake_case  ):
                  _lowerCAmelCase							     =							Node(__snake_case			,			self.head  )




      def __iter__(			self			: Union[str, Any]  )				->      Iterator[int]:
            _lowerCAmelCase							     =							self.head
            while node:
                  yield node.data
                  _lowerCAmelCase							     =							node.next_node




      def __len__(			self			: Tuple  )				->      int:
            return sum(1 for _ in self  )




      def __str__(			self			: List[Any]  )				->      str:
            return " -> ".join([str(__snake_case  ) for node in self]  )




def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      return SortedLinkedList(list(lowerCAmelCase  ) + list(lowerCAmelCase  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      A__				:   Optional[int]      =SortedLinkedList
      print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))








'''simple docstring'''







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  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



