





'''simple docstring'''







import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 				__SCREAMING_SNAKE_CASE      (							lowerCamelCase__   ):
      '''simple docstring'''



      lowerCAmelCase__							:   List[str]					=		["""image_processor""", """tokenizer"""]
      lowerCAmelCase__							:   List[str]					=		"""AutoImageProcessor"""
      lowerCAmelCase__							:   Dict					=		"""AutoTokenizer"""

      def __init__(				self     :			Any ,UpperCamelCase     :			Union[str, Any]=None ,UpperCamelCase     :			List[Any]=None ,**UpperCamelCase     :			str	)		->	Any:
           _lowercase				:   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.' ,snake_case__ ,)
                _lowercase				:   Optional[int]	    =				kwargs.pop('feature_extractor'	)

           _lowercase				:   List[str]	    =				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__(snake_case__ ,snake_case__	)
           _lowercase				:   Union[str, Any]	    =				self.image_processor
           _lowercase				:   int	    =				False

      def __call__(				self     :			List[Any] ,*UpperCamelCase     :			List[str] ,**UpperCamelCase     :			str	)		->	Optional[int]:
           # For backward compatibility
           if self._in_target_context_manager:
                return self.current_processor(*snake_case__ ,**snake_case__	)

           _lowercase				:   Tuple	    =				kwargs.pop('images' ,snake_case__	)
           _lowercase				:   Optional[int]	    =				kwargs.pop('text' ,snake_case__	)
           if len(snake_case__	) > 0:
                _lowercase				:   Dict	    =				args[0]
                _lowercase				:   Tuple	    =				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:
                _lowercase				:   Any	    =				self.image_processor(snake_case__ ,*snake_case__ ,**snake_case__	)
           if text is not None:
                _lowercase				:   Union[str, Any]	    =				self.tokenizer(snake_case__ ,**snake_case__	)

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

      def 	_lowerCamelCase		(				self     :			str ,*UpperCamelCase     :			Union[str, Any] ,**UpperCamelCase     :			Optional[Any]	)		->	Any:
           return self.tokenizer.batch_decode(*snake_case__ ,**snake_case__	)

      def 	_lowerCamelCase		(				self     :			Union[str, Any] ,*UpperCamelCase     :			Tuple ,**UpperCamelCase     :			Union[str, Any]	)		->	Any:
           return self.tokenizer.decode(*snake_case__ ,**snake_case__	)

      @contextmanager
      def 	_lowerCamelCase		(				self     :			Optional[int]	)		->	int:
           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.'	)
           _lowercase				:   str	    =				True
           _lowercase				:   int	    =				self.tokenizer
           yield
           _lowercase				:   Dict	    =				self.image_processor
           _lowercase				:   List[str]	    =				False

      def 	_lowerCamelCase		(				self     :			int ,UpperCamelCase     :			Tuple ,UpperCamelCase     :			Any=False ,UpperCamelCase     :			Dict=None	)		->	Any:
           if added_vocab is None:
                _lowercase				:   Tuple	    =				self.tokenizer.get_added_vocab()

           _lowercase				:   int	    =				{}

           while tokens:
                _lowercase				:   Optional[Any]	    =				re.search(R'<s_(.*?)>' ,snake_case__ ,re.IGNORECASE	)
                if start_token is None:
                     break
                _lowercase				:   Dict	    =				start_token.group(1	)
                _lowercase				:   Optional[int]	    =				re.search(RF'''</s_{key}>''' ,snake_case__ ,re.IGNORECASE	)
                _lowercase				:   Any	    =				start_token.group()
                if end_token is None:
                     _lowercase				:   Union[str, Any]	    =				tokens.replace(snake_case__ ,''	)
                else:
                     _lowercase				:   Any	    =				end_token.group()
                     _lowercase				:   Dict	    =				re.escape(snake_case__	)
                     _lowercase				:   Tuple	    =				re.escape(snake_case__	)
                     _lowercase				:   Any	    =				re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''' ,snake_case__ ,re.IGNORECASE	)
                     if content is not None:
                          _lowercase				:   Optional[int]	    =				content.group(1	).strip()
                          if r"<s_" in content and r"</s_" in content:  # non-leaf node
                               _lowercase				:   Any	    =				self.tokenajson(snake_case__ ,is_inner_value=snake_case__ ,added_vocab=snake_case__	)
                               if value:
                                    if len(snake_case__	) == 1:
                                         _lowercase				:   Tuple	    =				value[0]
                                    _lowercase				:   Tuple	    =				value
                          else:  # leaf nodes
                               _lowercase				:   List[Any]	    =				[]
                               for leaf in content.split(R'<sep/>'	):
                                    _lowercase				:   Optional[Any]	    =				leaf.strip()
                                    if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                         _lowercase				:   str	    =				leaf[1:-2]  # for categorical special tokens
                                    output[key].append(snake_case__	)
                               if len(output[key]	) == 1:
                                    _lowercase				:   int	    =				output[key][0]

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

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

      @property
      def 	_lowerCamelCase		(				self     :			List[str]	)		->	str:
           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,snake_case__ ,)
           return self.image_processor_class



      @property
      def 	_lowerCamelCase		(				self     :			Any	)		->	Dict:
           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,snake_case__ ,)
           return self.image_processor






def  __magic_name__  (				lowercase = 100				)				-> int:

							"""simple docstring"""



							lowercase_	:   Dict  				=     (n * (n + 1) // 2) ** 2
							lowercase_	:   List[str]  				=     n * (n + 1) * (2 * n + 1) // 6
							return sum_cubes - sum_squares


if __name__ == "__main__":
							print(F'''{solution() = }''')