


from __future__ import annotations






class _lowerCamelCase:
       def __init__(					self,      lowerCamelCase=None)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		data
           _lowercase       :							List[str]      		=		None




       def __repr__(					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		[]
           _lowercase       :							Dict      		=		self
           while temp:
               string_rep.append(F'''{temp.data}''')
               _lowercase       :							int      		=		temp.next
           return "->".join(lowerCamelCase)



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						List[str]:
    if not elements_list:
        raise Exception('The Elements List is empty' )

    _lowercase       :							List[str]      		=		Node(elements_list[0] )
    for i in range(1							, len(lowerCamelCase_ ) ):
        _lowercase       :							Optional[int]      		=		Node(elements_list[i] )
        _lowercase       :							str      		=		current.next
    return head



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						None:
    if head_node is not None and isinstance(lowerCamelCase_							, lowerCamelCase_ ):
        print_reverse(head_node.next )
        print(head_node.data )



def 				UpperCamelCase_(      )    ->						Dict:
    from doctest import testmod

    testmod()

    _lowercase       :							str      		=		make_linked_list([14, 52, 14, 12, 43] )
    print('Linked List:' )
    print(lowerCamelCase_ )
    print('Elements in Reverse:' )
    print_reverse(lowerCamelCase_ )


if __name__ == "__main__":
 main()




'''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 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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

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

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

