'''simple docstring'''




def       A_ (			_lowerCamelCase      :				list	,	_lowerCamelCase      :				list     ):
      _validate_point(__lowerCAmelCase     )
      _validate_point(__lowerCAmelCase     )
      if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
            raise ValueError('Both points must be in the same n-dimensional space'     )

      return float(sum(abs(a - b     ) for a, b in zip(__lowerCAmelCase	,	__lowerCAmelCase     )     )     )







def       A_ (			_lowerCamelCase      :				list[float]     ):
      if point:
            if isinstance(__lowerCAmelCase	,	__lowerCAmelCase     ):
                  for item in point:
                        if not isinstance(__lowerCAmelCase	,	(int, float)     ):
                              _lowerCAmelCase		 = (
                                  """Expected a list of numbers as input, found """
                                  F"{type(__lowerCAmelCase     ).__name__}"
                              )
                              raise TypeError(__lowerCAmelCase     )
            else:
                  _lowerCAmelCase		 = F"Expected a list of numbers as input, found {type(__lowerCAmelCase     ).__name__}"
                  raise TypeError(__lowerCAmelCase     )
      else:
            raise ValueError('Missing an input'     )







def       A_ (			_lowerCamelCase      :				list	,	_lowerCamelCase      :				list     ):
      _validate_point(__lowerCAmelCase     )
      _validate_point(__lowerCAmelCase     )
      if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
            raise ValueError('Both points must be in the same n-dimensional space'     )

      return float(sum(abs(x - y     ) for x, y in zip(__lowerCAmelCase	,	__lowerCAmelCase     )     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()













from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Tuple						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			str						=					'''BlipImageProcessor'''
  lowercase_						:			str						=					('''BertTokenizer''', '''BertTokenizerFast''')

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					False
     super().__init__(lowerCamelCase__   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					self.image_processor

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	ImageInput = None   ,				lowerCamelCase__						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Union[bool, str, PaddingStrategy] = False   ,				lowerCamelCase__						:	Union[bool, str, TruncationStrategy] = None   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	Optional[int] = None   ,				lowerCamelCase__						:	Optional[bool] = None   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	Optional[Union[str, TensorType]] = None   ,				**lowerCamelCase__						:	int   ,				):
     """simple docstring"""

     if images is None and text is None:
        raise ValueError("""You have to specify either images or text."""			)

     # Get only text
     if images is None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer
        __UpperCamelCase	:			Dict					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
        return text_encoding

     # add pixel_values
     __UpperCamelCase	:			str					=					self.image_processor(lowerCamelCase__   ,				return_tensors=lowerCamelCase__			)

     if text is not None:
        __UpperCamelCase	:			Optional[int]					=					self.tokenizer(
            text=lowerCamelCase__   ,				add_special_tokens=lowerCamelCase__   ,				padding=lowerCamelCase__   ,				truncation=lowerCamelCase__   ,				max_length=lowerCamelCase__   ,				stride=lowerCamelCase__   ,				pad_to_multiple_of=lowerCamelCase__   ,				return_attention_mask=lowerCamelCase__   ,				return_overflowing_tokens=lowerCamelCase__   ,				return_special_tokens_mask=lowerCamelCase__   ,				return_offsets_mapping=lowerCamelCase__   ,				return_token_type_ids=lowerCamelCase__   ,				return_length=lowerCamelCase__   ,				verbose=lowerCamelCase__   ,				return_tensors=lowerCamelCase__   ,				**lowerCamelCase__   ,				)
     else:
        __UpperCamelCase	:			Optional[Any]					=					None

     if text_encoding is not None:
        encoding_image_processor.update(lowerCamelCase__			)

     return encoding_image_processor

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	Any   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	List[str]   ,				*lowerCamelCase__						:	int   ,				**lowerCamelCase__						:	Tuple			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.tokenizer.model_input_names
     __UpperCamelCase	:			str					=					self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



