'''simple docstring'''

from __future__ import annotations



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase=None )  ->  Dict:
        lowercase__				:    int  =					data
        lowercase__				:    str  =					None


       def __repr__(						self )  ->  Dict:
        lowercase__				:    int  =					[]
        lowercase__				:    Dict  =					self
        while temp:
         string_rep.append(F"""{temp.data}""" )
         lowercase__				:    Dict  =					temp.next
        return "->".join(__lowerCAmelCase )


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if not elements_list:
  raise Exception('''The Elements List is empty'''     )

 lowercase__				:    List[str]  =					Node(elements_list[0]     )
 for i in range(1		,     len(UpperCAmelCase     )     ):
  lowercase__				:    List[Any]  =					Node(elements_list[i]     )
  lowercase__				:    Tuple  =					current.next
 return head
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 if head_node is not None and isinstance(UpperCAmelCase		,     UpperCAmelCase     ):
  print_reverse(head_node.next     )
  print(head_node.data     )
def 			__UpperCamelCase       (   ):
 from doctest import testmod

 testmod()

 lowercase__				:    Tuple  =					make_linked_list([14, 52, 14, 12, 43]     )
 print('''Linked List:'''     )
 print(UpperCAmelCase     )
 print('''Elements in Reverse:'''     )
 print_reverse(UpperCAmelCase     )


if __name__ == "__main__":
  main()


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

