





from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
      import torch

if is_tf_available():
      import tensorflow as tf





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						List[str]							=   ["""image_processor"""]
  __lowerCAmelCase   :						Optional[Any]							=   """SamImageProcessor"""


  def __init__(							self ,__snake_case ):



       """simple docstring"""





       super().__init__(__snake_case )
       A_          =       self.image_processor
       A_          =       -1_0
       A_          =       self.image_processor.size['''longest_edge''']


  def __call__(							self ,__snake_case=None ,__snake_case=None ,__snake_case=None ,__snake_case=None ,__snake_case = None ,**__snake_case ,):



       """simple docstring"""





       A_          =       self.image_processor(
           __snake_case ,return_tensors=__snake_case ,**__snake_case ,)

       # pop arguments that are not used in the foward but used nevertheless
       A_          =       encoding_image_processor['''original_sizes''']

       if hasattr(__snake_case ,'''numpy''' ):  # Checks if Torch or TF tensor
            A_          =       original_sizes.numpy()

       A_   ,    A_   ,    A_          =       self._check_and_preprocess_points(
           input_points=__snake_case ,input_labels=__snake_case ,input_boxes=__snake_case ,)

       A_          =       self._normalize_and_convert(
           __snake_case ,__snake_case ,input_points=__snake_case ,input_labels=__snake_case ,input_boxes=__snake_case ,return_tensors=__snake_case ,)

       return encoding_image_processor


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,__snake_case=None ,__snake_case="pt" ,):



       """simple docstring"""





       if input_points is not None:
            if len(__snake_case ) != len(__snake_case ):
                 A_          =       [
                     self._normalize_coordinates(self.target_size ,__snake_case ,original_sizes[0] ) for point in input_points
                 ]
            else:
                 A_          =       [
                     self._normalize_coordinates(self.target_size ,__snake_case ,__snake_case )
                     for point, original_size in zip(__snake_case ,__snake_case )
                 ]
            # check that all arrays have the same shape
            if not all(point.shape == input_points[0].shape for point in input_points ):
                 if input_labels is not None:
                      A_   ,    A_          =       self._pad_points_and_labels(__snake_case ,__snake_case )

            A_          =       np.array(__snake_case )

       if input_labels is not None:
            A_          =       np.array(__snake_case )

       if input_boxes is not None:
            if len(__snake_case ) != len(__snake_case ):
                 A_          =       [
                     self._normalize_coordinates(self.target_size ,__snake_case ,original_sizes[0] ,is_bounding_box=__snake_case )
                     for box in input_boxes
                 ]
            else:
                 A_          =       [
                     self._normalize_coordinates(self.target_size ,__snake_case ,__snake_case ,is_bounding_box=__snake_case )
                     for box, original_size in zip(__snake_case ,__snake_case )
                 ]
            A_          =       np.array(__snake_case )

       if input_boxes is not None:
            if return_tensors == "pt":
                 A_          =       torch.from_numpy(__snake_case )
                 # boxes batch size of 1 by default
                 A_          =       input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
            elif return_tensors == "tf":
                 A_          =       tf.convert_to_tensor(__snake_case )
                 # boxes batch size of 1 by default
                 A_          =       tf.expand_dims(__snake_case ,1 ) if len(input_boxes.shape ) != 3 else input_boxes
            encoding_image_processor.update({'''input_boxes''': input_boxes} )
       if input_points is not None:
            if return_tensors == "pt":
                 A_          =       torch.from_numpy(__snake_case )
                 # point batch size of 1 by default
                 A_          =       input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
            elif return_tensors == "tf":
                 A_          =       tf.convert_to_tensor(__snake_case )
                 # point batch size of 1 by default
                 A_          =       tf.expand_dims(__snake_case ,1 ) if len(input_points.shape ) != 4 else input_points
            encoding_image_processor.update({'''input_points''': input_points} )
       if input_labels is not None:
            if return_tensors == "pt":
                 A_          =       torch.from_numpy(__snake_case )
                 # point batch size of 1 by default
                 A_          =       input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
            elif return_tensors == "tf":
                 A_          =       tf.convert_to_tensor(__snake_case )
                 # point batch size of 1 by default
                 A_          =       tf.expand_dims(__snake_case ,1 ) if len(input_labels.shape ) != 3 else input_labels
            encoding_image_processor.update({'''input_labels''': input_labels} )

       return encoding_image_processor


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       max([point.shape[0] for point in input_points] )
       A_          =       []
       for i, point in enumerate(__snake_case ):
            if point.shape[0] != expected_nb_points:
                 A_          =       np.concatenate(
                     [point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] ,axis=0 )
                 A_          =       np.append(input_labels[i] ,[self.point_pad_value] )
            processed_input_points.append(__snake_case )
       A_          =       processed_input_points
       return input_points, input_labels


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case=False ):



       """simple docstring"""





       A_   ,    A_          =       original_size
       A_   ,    A_          =       self.image_processor._get_preprocess_shape(__snake_case ,longest_edge=__snake_case )
       A_          =       deepcopy(__snake_case ).astype(__snake_case )

       if is_bounding_box:
            A_          =       coords.reshape(-1 ,2 ,2 )

       A_          =       coords[..., 0] * (new_w / old_w)
       A_          =       coords[..., 1] * (new_h / old_h)

       if is_bounding_box:
            A_          =       coords.reshape(-1 ,4 )

       return coords


  def 						__UpperCAmelCase     (							self ,__snake_case=None ,__snake_case=None ,__snake_case=None ,):



       """simple docstring"""





       if input_points is not None:
            if hasattr(__snake_case ,'''numpy''' ):  # Checks for TF or Torch tensor
                 A_          =       input_points.numpy().tolist()

            if not isinstance(__snake_case ,__snake_case ) or not isinstance(input_points[0] ,__snake_case ):
                 raise ValueError('''Input points must be a list of list of floating points.''' )
            A_          =       [np.array(__snake_case ) for input_point in input_points]
       else:
            A_          =       None

       if input_labels is not None:
            if hasattr(__snake_case ,'''numpy''' ):
                 A_          =       input_labels.numpy().tolist()

            if not isinstance(__snake_case ,__snake_case ) or not isinstance(input_labels[0] ,__snake_case ):
                 raise ValueError('''Input labels must be a list of list integers.''' )
            A_          =       [np.array(__snake_case ) for label in input_labels]
       else:
            A_          =       None

       if input_boxes is not None:
            if hasattr(__snake_case ,'''numpy''' ):
                 A_          =       input_boxes.numpy().tolist()

            if (
                not isinstance(__snake_case ,__snake_case )
                or not isinstance(input_boxes[0] ,__snake_case )
                or not isinstance(input_boxes[0][0] ,__snake_case )
            ):
                 raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
            A_          =       [np.array(__snake_case ).astype(np.floataa ) for box in input_boxes]
       else:
            A_          =       None

       return input_points, input_labels, input_boxes


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.image_processor.model_input_names
       return list(dict.fromkeys(__snake_case ) )




  def 						__UpperCAmelCase     (							self ,*__snake_case ,**__snake_case ):



       """simple docstring"""





       return self.image_processor.post_process_masks(*__snake_case ,**__snake_case )










from __future__ import annotations

def      UpperCAmelCase_    (    _UpperCAmelCase							:list[float]  ,						_UpperCAmelCase							:list[float]				)	->				float:
     '''simple docstring'''


     A_          =       sorted(numsa + numsa				)
     A_   ,    A_          =       divmod(len(_UpperCAmelCase				)  ,						2				)
     if mod == 1:
          return all_numbers[div]
     else:
          return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      a__      :     Optional[Any]         =		[float(x) for x in input('Enter the elements of first array: ').split()]
      a__      :     int         =		[float(x) for x in input('Enter the elements of second array: ').split()]
      print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')



