def 				__lowerCamelCase		(						):
       return [list(range(1_0_0_0 - i     ,  -1_0_0_0 - i     ,  -1     )     ) for i in range(1_0_0_0     )]


lowerCAmelCase__													=			generate_large_matrix()
lowerCAmelCase__													=			(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       assert all(row == sorted(a__     ,  reverse=a__     ) for row in grid     )
       assert all(list(a__     ) == sorted(a__     ,  reverse=a__     ) for col in zip(*a__     )     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 0
       lowerCAmelCase__					     = len(a__     ) - 1

       # Edge cases such as no values or all numbers are negative.
       if not array or array[0] < 0:
              return 0

       while right + 1 > left:
              lowerCAmelCase__					     = (left + right) // 2
              lowerCAmelCase__					     = array[mid]

              # Num must be negative and the index must be greater than or equal to 0.
              if num < 0 and array[mid - 1] >= 0:
                     return mid

              if num >= 0:
                     lowerCAmelCase__					     = mid + 1
              else:
                     lowerCAmelCase__					     = mid - 1
              # No negative numbers so return the last index of the array + 1 which is the length.
       return len(a__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 0
       lowerCAmelCase__					     = len(grid[0]     )

       for i in range(len(a__     )     ):
              lowerCAmelCase__					     = find_negative_index(grid[i][:bound]     )
              total += bound
       return (len(a__     ) * len(grid[0]     )) - total






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return len([number for row in grid for number in row if number < 0]     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 0
       for row in grid:
              for i, number in enumerate(a__     ):
                     if number < 0:
                            total += len(a__     ) - i
                            break
       return total






def 				__lowerCamelCase		(						):
       from timeit import timeit

       print('Running benchmarks'     )
       lowerCAmelCase__					     = (
           'from __main__ import count_negatives_binary_search, '
           'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
       )
       for func in (
           "count_negatives_binary_search",  # took 0.7727 seconds
           "count_negatives_brute_force_with_break",  # took 4.6505 seconds
           "count_negatives_brute_force",  # took 12.8160 seconds
       ):
              lowerCAmelCase__					     = timeit(F"""{func}(grid=grid)"""     ,  setup=a__     ,  number=5_0_0     )
              print(F"""{func}() took {time:0.4f} seconds"""     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()

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     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['image_processor']
 UpperCAmelCase_     			= 'SamImageProcessor'




 def __init__(      self :  Tuple      ,   lowercase__ :  Dict):







        '''simple docstring'''



        super().__init__(lowercase__)
        lowerCAmelCase__					     = self.image_processor
        lowerCAmelCase__					     = -10
        lowerCAmelCase__					     = self.image_processor.size['longest_edge']




 def __call__(      self :  List[Any]      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  Any=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processor(
            lowercase__      ,   return_tensors=lowercase__      ,   **lowercase__      ,   )

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

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

        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = self._check_and_preprocess_points(
            input_points=lowercase__      ,   input_labels=lowercase__      ,   input_boxes=lowercase__      ,   )

        lowerCAmelCase__					     = self._normalize_and_convert(
            lowercase__      ,   lowercase__      ,   input_points=lowercase__      ,   input_labels=lowercase__      ,   input_boxes=lowercase__      ,   return_tensors=lowercase__      ,   )

        return encoding_image_processor




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  List[str]      ,   lowercase__ :  str=None      ,   lowercase__ :  Optional[int]=None      ,   lowercase__ :  str=None      ,   lowercase__ :  Optional[Any]="pt"      ,   ):







        '''simple docstring'''



        if input_points is not None:
               if len(lowercase__) != len(lowercase__):
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   original_sizes[0]) for point in input_points
                      ]
               else:
                      lowerCAmelCase__					     = [
                          self._normalize_coordinates(self.target_size      ,   lowercase__      ,   lowercase__)
                          for point, original_size in zip(lowercase__      ,   lowercase__)
                      ]
               # 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:
                             lowerCAmelCase__     , lowerCAmelCase__					     = self._pad_points_and_labels(lowercase__      ,   lowercase__)

               lowerCAmelCase__					     = np.array(lowercase__)

        if input_labels is not None:
               lowerCAmelCase__					     = np.array(lowercase__)

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

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

        return encoding_image_processor




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  int      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  int      ,   lowercase__ :  Optional[Any]=False):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = original_size
        lowerCAmelCase__     , lowerCAmelCase__					     = self.image_processor._get_preprocess_shape(lowercase__      ,   longest_edge=lowercase__)
        lowerCAmelCase__					     = deepcopy(lowercase__).astype(lowercase__)

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

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

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

        return coords




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  int=None      ,   ):







        '''simple docstring'''



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

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

        if input_labels is not None:
               if hasattr(lowercase__      ,   'numpy'):
                      lowerCAmelCase__					     = input_labels.numpy().tolist()

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

        if input_boxes is not None:
               if hasattr(lowercase__      ,   'numpy'):
                      lowerCAmelCase__					     = input_boxes.numpy().tolist()

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

        return input_points, input_labels, input_boxes




 @property
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.image_processor.model_input_names
        return list(dict.fromkeys(lowercase__))




 def 		__snake_case						(      self :  int      ,   *lowercase__ :  int      ,   **lowercase__ :  int):







        '''simple docstring'''



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