





'''simple docstring'''







import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[str]												=["""image_processor""", """tokenizer"""]
  a_       :List[str]												="""AutoImageProcessor"""
  a_       :int												="""AutoTokenizer"""

  def __init__(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							**SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       __a       =   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       =   kwargs.pop("""feature_extractor"""						)

       __a       =   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__						)
       __a       =   self.image_processor
       __a       =   False

  def __call__(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							Optional[int]				,							**SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       if self._in_target_context_manager:
            return self.current_processor(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       __a       =   kwargs.pop("""images"""				,							SCREAMING_SNAKE_CASE__						)
       __a       =   kwargs.pop("""text"""				,							SCREAMING_SNAKE_CASE__						)
       if len(SCREAMING_SNAKE_CASE__						) > 0:
            __a       =   args[0]
            __a       =   args[1:]

       if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

       if images is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if text is not None:
            __a       =   self.tokenizer(SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       if text is None:
            return inputs
       elif images is None:
            return encodings
       else:
            __a       =   encodings["""input_ids"""]
            return inputs

  def __a						(					self     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							List[str]				,							**SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]				,							*SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							**SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  @contextmanager
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       warnings.warn(
           """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
           """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
           """your images inputs, or in a separate call."""						)
       __a       =   True
       __a       =   self.tokenizer
       yield
       __a       =   self.image_processor
       __a       =   False

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Dict=False				,							SCREAMING_SNAKE_CASE__     :							int=None						):





       '''simple docstring'''

       if added_vocab is None:
            __a       =   self.tokenizer.get_added_vocab()

       __a       =   {}

       while tokens:
            __a       =   re.search(r"""<s_(.*?)>"""				,							SCREAMING_SNAKE_CASE__				,							re.IGNORECASE						)
            if start_token is None:
                 break
            __a       =   start_token.group(1						)
            __a       =   re.search(rf'''</s_{key}>'''				,							SCREAMING_SNAKE_CASE__				,							re.IGNORECASE						)
            __a       =   start_token.group()
            if end_token is None:
                 __a       =   tokens.replace(SCREAMING_SNAKE_CASE__				,							""""""						)
            else:
                 __a       =   end_token.group()
                 __a       =   re.escape(SCREAMING_SNAKE_CASE__						)
                 __a       =   re.escape(SCREAMING_SNAKE_CASE__						)
                 __a       =   re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''				,							SCREAMING_SNAKE_CASE__				,							re.IGNORECASE						)
                 if content is not None:
                      __a       =   content.group(1						).strip()
                      if r"<s_" in content and r"</s_" in content:  # non-leaf node
                           __a       =   self.tokenajson(SCREAMING_SNAKE_CASE__				,							is_inner_value=SCREAMING_SNAKE_CASE__				,							added_vocab=SCREAMING_SNAKE_CASE__						)
                           if value:
                                if len(SCREAMING_SNAKE_CASE__						) == 1:
                                     __a       =   value[0]
                                __a       =   value
                      else:  # leaf nodes
                           __a       =   []
                           for leaf in content.split(r"""<sep/>"""						):
                                __a       =   leaf.strip()
                                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                     __a       =   leaf[1:-2]  # for categorical special tokens
                                output[key].append(SCREAMING_SNAKE_CASE__						)
                           if len(output[key]						) == 1:
                                __a       =   output[key][0]

                 __a       =   tokens[tokens.find(SCREAMING_SNAKE_CASE__						) + len(SCREAMING_SNAKE_CASE__						) :].strip()
                 if tokens[:6] == r"<sep/>":  # non-leaf nodes
                      return [output] + self.tokenajson(tokens[6:]				,							is_inner_value=SCREAMING_SNAKE_CASE__				,							added_vocab=SCREAMING_SNAKE_CASE__						)

       if len(SCREAMING_SNAKE_CASE__						):
            return [output] if is_inner_value else output
       else:
            return [] if is_inner_value else {"text_sequence": tokens}

  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       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 __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,							SCREAMING_SNAKE_CASE__				,							)
       return self.image_processor










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					bool:
     """simple docstring"""




     __a       =   [int(__SCREAMING_SNAKE_CASE		) for i in ip_va_address.split("""."""		) if i.isdigit()]
     return len(__SCREAMING_SNAKE_CASE		) == 4 and all(0 <= int(__SCREAMING_SNAKE_CASE		) <= 254 for octet in octets		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			input().strip()
 SCREAMING_SNAKE_CASE_      =			'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")






