


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 	__lowerCamelCase			(_a						):
       _lowercase											=     ["""image_processor""", """tokenizer"""]
       _lowercase											=     """CLIPImageProcessor"""
       _lowercase											=     ("""CLIPTokenizer""", """CLIPTokenizerFast""")







       def __init__(			self: Tuple,A_: Dict=None,A_: Dict=None,**A_: str					):






            '''simple docstring'''




            __UpperCamelCase =			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 =			kwargs.pop('feature_extractor'					)

            __UpperCamelCase =			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__(A_,A_					)







       def __call__(			self: Optional[int],A_: Union[str, Any]=None,A_: int=None,A_: List[Any]=None,**A_: Optional[Any]					):






            '''simple docstring'''




            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 =			self.tokenizer(A_,return_tensors=A_,**A_					)

            if images is not None:
                 __UpperCamelCase =			self.image_processor(A_,return_tensors=A_,**A_					)

            if text is not None and images is not None:
                 __UpperCamelCase =			image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**A_					),tensor_type=A_					)







       def 		snake_case_	(			self: Optional[int],*A_: Tuple,**A_: Union[str, Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict,*A_: int,**A_: List[Any]					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            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 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def 		_A		(		_lowercase					,  _lowercase=0 )					-> Dict:


     """simple docstring"""


     return sorted(_lowercase					,  key=lambda _lowercase : x[column] )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> List[Any]:


     """simple docstring"""


     for i in range(points_counts - 1 ):
          for j in range(i + 1					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> Tuple:


     """simple docstring"""


     for i in range(min(6					,  points_counts - 1 )					,  _lowercase ):
          for j in range(max(0					,  i - 6 )					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase					,  _lowercase )

     # recursion
     __UpperCamelCase =			points_counts // 2
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[:mid]					,  _lowercase )
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[mid:]					,  points_counts - mid )
     __UpperCamelCase =			min(_lowercase					,  _lowercase )
     __UpperCamelCase =			[]
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
               cross_strip.append(_lowercase )

     __UpperCamelCase =			dis_between_closest_in_strip(
         _lowercase					,  len(_lowercase )					,  _lowercase )
     return min(_lowercase					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			column_based_sort(_lowercase					,  column=0 )
     __UpperCamelCase =			column_based_sort(_lowercase					,  column=1 )
     return (
         closest_pair_of_points_sqr(
             _lowercase					,  _lowercase					,  _lowercase )
     ) ** 0.5


if __name__ == "__main__":
    __snake_case   							=						[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
    print('''Distance:''', closest_pair_of_points(points, len(points)))


