

import warnings

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






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						["""image_processor""", """tokenizer"""]
 UpperCAmelCase							    =						"""CLIPImageProcessor"""
 UpperCAmelCase							    =						("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")


 def __init__(						self		: int			,						lowerCAmelCase__		: List[str]=None			,						lowerCAmelCase__		: Tuple=None			,						**lowerCAmelCase__		: Optional[int]    )    ->      Union[str, Any]:
   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."			,						lowerCAmelCase__			,						)
     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__(lowerCAmelCase__			,						lowerCAmelCase__    )


 def __call__(						self		: Optional[Any]			,						lowerCAmelCase__		: str=None			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: Tuple=None			,						**lowerCAmelCase__		: List[str]    )    ->      Optional[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					      =  self.tokenizer(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )

   if images is not None:
     UpperCAmelCase					      =  self.image_processor(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )

   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(**lowerCAmelCase__    )			,						tensor_type=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[Any]			,						*lowerCAmelCase__		: Tuple			,						**lowerCAmelCase__		: Optional[Any]    )    ->      Any:
   return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[Any]			,						*lowerCAmelCase__		: Tuple			,						**lowerCAmelCase__		: str    )    ->      Union[str, Any]:
   return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      List[Any]:
   UpperCAmelCase					      =  self.tokenizer.model_input_names
   UpperCAmelCase					      =  self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







def      _lowerCAmelCase(	__A   ,       __A			):
  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def      _lowerCAmelCase(	__A   ,       __A=0			):
  return sorted(__A   ,       key=lambda __A			: x[column]			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(points_counts - 1			):
    for j in range(i + 1   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

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

  # base case
  if points_counts <= 3:
    return dis_between_closest_pair(__A   ,       __A			)

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

  UpperCAmelCase					      =  dis_between_closest_in_strip(
      __A   ,       len(__A			)   ,       __A			)
  return min(__A   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=0			)
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=1			)
  return (
      closest_pair_of_points_sqr(
          __A   ,       __A   ,       __A			)
  ) ** 0.5


if __name__ == "__main__":
      lowerCAmelCase__       = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))



