






'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				List[str]							 =			["""image_processor""", """tokenizer"""]
      a						:				Optional[int]							 =			"""AutoImageProcessor"""
      a						:				Optional[Any]							 =			"""AutoTokenizer"""
      def __init__(   self      :    Tuple	,   __lowercase      :    Dict=None	,   __lowercase      :    Optional[int]=None	,   **__lowercase      :    Optional[int]      )							->   Any:
        __UpperCAmelCase							:			Dict     =						None
        if "feature_extractor" in kwargs:
          warnings.warn(
              """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
              """ instead."""	,   _A	,   )
          __UpperCAmelCase							:			Union[str, Any]     =						kwargs.pop("""feature_extractor"""      )

        __UpperCAmelCase							:			str     =						image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError("""You have to specify an image_processor."""      )
        if tokenizer is None:
          raise ValueError("""You have to specify a tokenizer."""      )

        super().__init__(_A	,   _A      )
        __UpperCAmelCase							:			str     =						self.image_processor
      def __call__(   self      :    str	,   __lowercase      :    Dict=None	,   __lowercase      :    Optional[int]=None	,   __lowercase      :    Any=None	,   **__lowercase      :    str      )							->   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:
          __UpperCAmelCase							:			Optional[Any]     =						self.tokenizer(_A	,   return_tensors=_A	,   **_A      )

        if images is not None:
          __UpperCAmelCase							:			Any     =						self.image_processor(_A	,   return_tensors=_A	,   **_A      )

        if text is not None and images is not None:
          __UpperCAmelCase							:			int     =						image_features.pixel_values
          return encoding
        elif text is not None:
          return encoding
        else:
          return BatchEncoding(data=dict(**_A      )	,   tensor_type=_A      )
      def 			UpperCAmelCase   (   self      :    Optional[int]	,   *__lowercase      :    List[str]	,   **__lowercase      :    str      )							->   Dict:
        return self.tokenizer.batch_decode(*_A	,   **_A      )
      def 			UpperCAmelCase   (   self      :    Any	,   *__lowercase      :    Tuple	,   **__lowercase      :    Union[str, Any]      )							->   List[str]:
        return self.tokenizer.decode(*_A	,   **_A      )
      @property
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Optional[Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						self.tokenizer.model_input_names
        __UpperCAmelCase							:			Any     =						self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )
      @property
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   List[str]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,   _A	,   )
        return self.image_processor_class




      @property
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   Optional[Any]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,   _A	,   )
        return self.image_processor










a	: List[str]      =				[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class 					a					:



      """simple docstring"""


      def __init__(   self      :    Union[str, Any]	,   __lowercase      :    List[str]      )							->   Optional[int]:
        __UpperCAmelCase							:			Dict     =						name
        __UpperCAmelCase							:			Optional[Any]     =						[]
        __UpperCAmelCase							:			List[Any]     =						[]
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    int      )							->   str:
        self.inbound.append(__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Optional[Any]      )							->   List[str]:
        self.outbound.append(__lowercase      )




      def __repr__(   self      :    Any      )							->   int:
        return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  str=3						,	__lowerCamelCase    :  int=0.8_5	):
  __UpperCAmelCase							:			Optional[int]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			str     =						1

  __UpperCAmelCase							:			Union[str, Any]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			Dict     =						len(node.outbound	)

  for i in range(__lowerCamelCase	):
    print(f"""======= Iteration {i + 1} ======="""	)
    for _, node in enumerate(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						(1 - d) + d * sum(
          ranks[ib] / outbounds[ib] for ib in node.inbound	)
    print(__lowerCamelCase	)






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						list(input("""Enter Names of the Nodes: """	).split()	)

  __UpperCAmelCase							:			Dict     =						[Node(__lowerCamelCase	) for name in names]

  for ri, row in enumerate(__lowerCamelCase	):
    for ci, col in enumerate(__lowerCamelCase	):
      if col == 1:
        nodes[ci].add_inbound(names[ri]	)
        nodes[ri].add_outbound(names[ci]	)

  print("""======= Nodes ======="""	)
  for node in nodes:
    print(__lowerCamelCase	)

  page_rank(__lowerCamelCase	)


if __name__ == "__main__":
       main()


