


from typing import Any
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			Dict:
             a_					:		Any							=  data
             a_					:		str							=  None
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[Any]     )		->			List[str]:
             a_					:		List[str]							=  None
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[int]:
             a_					:		List[str]							=  self.head
             while temp is not None:
                    print(temp.data     ,							end=' '     )
                    a_					:		Optional[Any]							=  temp.next
             print()
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Any     )		->			str:
             a_					:		int							=  Node(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.head
             a_					:		str							=  new_node

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Any:
             if node_data_a == node_data_a:
                    return
             else:
                    a_					:		Optional[Any]							=  self.head
                    while node_a is not None and node_a.data != node_data_a:
                           a_					:		List[str]							=  node_a.next

                    a_					:		Tuple							=  self.head
                    while node_a is not None and node_a.data != node_data_a:
                           a_					:		List[str]							=  node_a.next

                    if node_a is None or node_a is None:
                           return

                    a_							,		a_					:		Union[str, Any]							=  node_a.data, node_a.data


if __name__ == "__main__":
   UpperCAmelCase_      : List[Any]  					=       LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('After swapping')
   ll.print_list()






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Tuple        =						['''image_processor''', '''tokenizer''']
      snake_case__				:     Union[str, Any]        =						'''CLIPImageProcessor'''
      snake_case__				:     Dict        =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       List[str]=None     ,							**SCREAMING_SNAKE_CASE__				:       int     )		->			Any:
             a_					:		List[Any]							=  None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                        ' instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
                    a_					:		Tuple							=  kwargs.pop('feature_extractor'     )

             a_					:		Tuple							=  image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('You need to specify an `image_processor`.'     )
             if tokenizer is None:
                    raise ValueError('You need to specify a `tokenizer`.'     )

             super().__init__(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def __call__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							**SCREAMING_SNAKE_CASE__				:       str     )		->			Optional[Any]:

             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:
                    a_					:		List[str]							=  self.tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if images is not None:
                    a_					:		Dict							=  self.image_processor(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if text is not None and images is not None:
                    a_					:		Dict							=  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        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			List[Any]:
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       List[Any]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		str							=  self.tokenizer.model_input_names
             a_					:		Tuple							=  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             warnings.warn(
                 '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor_class

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             warnings.warn(
                 '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,							SCREAMING_SNAKE_CASE__     ,							)
             return self.image_processor


