




"""simple docstring"""




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 							lowerCamelCase		( a__				):
   lowercase       :  Optional[int]      =				["image_processor", "tokenizer"]
   lowercase       :  Any      =				"EfficientNetImageProcessor"
   lowercase       :  List[str]      =				("BertTokenizer", "BertTokenizerFast")







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
       super().__init__(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )







   def __call__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="max_length"    ,      SCREAMING_SNAKE_CASE_=64    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
       if text is None and images is None:
           raise ValueError("""You have to specify either text or images. Both cannot be none.""" )

       if text is not None:
           UpperCamelCase				:   str          =							self.tokenizer(
               SCREAMING_SNAKE_CASE_    ,      padding=SCREAMING_SNAKE_CASE_    ,      max_length=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )

       if images is not None:
           UpperCamelCase				:   Union[str, Any]          =							self.image_processor(SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )

       if text is not None and images is not None:
           UpperCamelCase				:   Tuple          =							image_features.pixel_values
           return encoding
       elif text is not None:
           return encoding
       else:
           return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_ )    ,      tensor_type=SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )







   @property
   def       a_							(							self ):
       UpperCamelCase				:   Optional[Any]          =							self.tokenizer.model_input_names
       UpperCamelCase				:   List[str]          =							self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )












"""simple docstring"""




class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
       UpperCamelCase				:   Optional[Any]          =							data
       UpperCamelCase				:   str          =							None







   def __repr__(							self ):
       UpperCamelCase				:   str          =							""""""
       UpperCamelCase				:   Any          =							self
       while temp:
           string_rep += f'<{temp.data}> ---> '
           UpperCamelCase				:   Optional[int]          =							temp.next
       string_rep += "<END>"
       return string_rep







def 					A_			(		snake_case_     :       Tuple    ):







    '''simple docstring'''

    # if elements_list is empty
    if not elements_list:
        raise Exception("""The Elements List is empty"""    )

    # Set first element as Head
    UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
    UpperCamelCase				:   str          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
        UpperCamelCase				:   Optional[Any]          =							current.next
    return head


__A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('''Creating Linked List from List.''')
__A :		int      							=    make_linked_list(list_data)
print('''Linked List:''')


print(linked_list)




