


'''simple docstring'''



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       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Any							     =     ["image_processor"]
     SCREAMING_SNAKE_CASE :				List[Any]							     =     "SamImageProcessor"

     def __init__(       self  :	Optional[Any]							,				snake_case  :	int    )		->							Union[str, Any]:
          super().__init__(snake_case    )
          __UpperCAmelCase			:				str													=    self.image_processor
          __UpperCAmelCase			:				Any													=    -10
          __UpperCAmelCase			:				Optional[int]													=    self.image_processor.size['''longest_edge''']

     def __call__(       self  :	Union[str, Any]							,				snake_case  :	Optional[int]=None							,				snake_case  :	Optional[int]=None							,				snake_case  :	List[Any]=None							,				snake_case  :	Tuple=None							,				snake_case  :	Optional[Union[str, TensorType]] = None							,				**snake_case  :	int							,				)		->							BatchEncoding:
          __UpperCAmelCase			:				Any													=    self.image_processor(
              snake_case							,				return_tensors=snake_case							,				**snake_case							,				)

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

          if hasattr(snake_case							,				'''numpy'''    ):  # Checks if Torch or TF tensor
               __UpperCAmelCase			:				int													=    original_sizes.numpy()

          __UpperCAmelCase			:				List[str]													=    self._check_and_preprocess_points(
              input_points=snake_case							,				input_labels=snake_case							,				input_boxes=snake_case							,				)

          __UpperCAmelCase			:				Tuple													=    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 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	Optional[Any]							,				snake_case  :	Any							,				snake_case  :	Dict=None							,				snake_case  :	Optional[int]=None							,				snake_case  :	Tuple=None							,				snake_case  :	Optional[Any]="pt"							,				)		->							int:
          if input_points is not None:
               if len(snake_case    ) != len(snake_case    ):
                    __UpperCAmelCase			:				List[str]													=    [
                        self._normalize_coordinates(self.target_size							,				snake_case							,				original_sizes[0]    ) for point in input_points
                    ]
               else:
                    __UpperCAmelCase			:				List[str]													=    [
                        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:
                         __UpperCAmelCase			:				List[Any]													=    self._pad_points_and_labels(snake_case							,				snake_case    )

               __UpperCAmelCase			:				str													=    np.array(snake_case    )

          if input_labels is not None:
               __UpperCAmelCase			:				Tuple													=    np.array(snake_case    )

          if input_boxes is not None:
               if len(snake_case    ) != len(snake_case    ):
                    __UpperCAmelCase			:				str													=    [
                        self._normalize_coordinates(self.target_size							,				snake_case							,				original_sizes[0]							,				is_bounding_box=snake_case    )
                        for box in input_boxes
                    ]
               else:
                    __UpperCAmelCase			:				List[str]													=    [
                        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    )
                    ]
               __UpperCAmelCase			:				Optional[int]													=    np.array(snake_case    )

          if input_boxes is not None:
               if return_tensors == "pt":
                    __UpperCAmelCase			:				Optional[Any]													=    torch.from_numpy(snake_case    )
                    # boxes batch size of 1 by default
                    __UpperCAmelCase			:				Any													=    input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
               elif return_tensors == "tf":
                    __UpperCAmelCase			:				Tuple													=    tf.convert_to_tensor(snake_case    )
                    # boxes batch size of 1 by default
                    __UpperCAmelCase			:				Union[str, Any]													=    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":
                    __UpperCAmelCase			:				Union[str, Any]													=    torch.from_numpy(snake_case    )
                    # point batch size of 1 by default
                    __UpperCAmelCase			:				Optional[Any]													=    input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
               elif return_tensors == "tf":
                    __UpperCAmelCase			:				List[Any]													=    tf.convert_to_tensor(snake_case    )
                    # point batch size of 1 by default
                    __UpperCAmelCase			:				Dict													=    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":
                    __UpperCAmelCase			:				List[str]													=    torch.from_numpy(snake_case    )
                    # point batch size of 1 by default
                    __UpperCAmelCase			:				Dict													=    input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
               elif return_tensors == "tf":
                    __UpperCAmelCase			:				Optional[Any]													=    tf.convert_to_tensor(snake_case    )
                    # point batch size of 1 by default
                    __UpperCAmelCase			:				Dict													=    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 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	str							,				snake_case  :	Dict    )		->							int:
          __UpperCAmelCase			:				Union[str, Any]													=    max([point.shape[0] for point in input_points]    )
          __UpperCAmelCase			:				List[str]													=    []
          for i, point in enumerate(snake_case    ):
               if point.shape[0] != expected_nb_points:
                    __UpperCAmelCase			:				Optional[Any]													=    np.concatenate(
                        [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]							,				axis=0    )
                    __UpperCAmelCase			:				List[str]													=    np.append(input_labels[i]							,				[self.point_pad_value]    )
               processed_input_points.append(snake_case    )
          __UpperCAmelCase			:				Dict													=    processed_input_points
          return input_points, input_labels

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	int							,				snake_case  :	np.ndarray							,				snake_case  :	str							,				snake_case  :	int=False    )		->							np.ndarray:
          __UpperCAmelCase			:				List[Any]													=    original_size
          __UpperCAmelCase			:				List[Any]													=    self.image_processor._get_preprocess_shape(snake_case							,				longest_edge=snake_case    )
          __UpperCAmelCase			:				str													=    deepcopy(snake_case    ).astype(snake_case    )

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

          __UpperCAmelCase			:				Union[str, Any]													=    coords[..., 0] * (new_w / old_w)
          __UpperCAmelCase			:				Any													=    coords[..., 1] * (new_h / old_h)

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

          return coords

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	List[Any]=None							,				snake_case  :	Dict=None							,				snake_case  :	Dict=None							,				)		->							Optional[int]:
          if input_points is not None:
               if hasattr(snake_case							,				'''numpy'''    ):  # Checks for TF or Torch tensor
                    __UpperCAmelCase			:				Optional[Any]													=    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.'''    )
               __UpperCAmelCase			:				int													=    [np.array(snake_case    ) for input_point in input_points]
          else:
               __UpperCAmelCase			:				str													=    None

          if input_labels is not None:
               if hasattr(snake_case							,				'''numpy'''    ):
                    __UpperCAmelCase			:				Dict													=    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.'''    )
               __UpperCAmelCase			:				str													=    [np.array(snake_case    ) for label in input_labels]
          else:
               __UpperCAmelCase			:				List[str]													=    None

          if input_boxes is not None:
               if hasattr(snake_case							,				'''numpy'''    ):
                    __UpperCAmelCase			:				Dict													=    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.'''    )
               __UpperCAmelCase			:				Union[str, Any]													=    [np.array(snake_case    ).astype(np.floataa    ) for box in input_boxes]
          else:
               __UpperCAmelCase			:				Optional[int]													=    None

          return input_points, input_labels, input_boxes

     @property
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Any:
          __UpperCAmelCase			:				Optional[int]													=    self.image_processor.model_input_names
          return list(dict.fromkeys(snake_case    )    )






     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				*snake_case  :	Dict							,				**snake_case  :	Optional[int]    )		->							List[str]:
          return self.image_processor.post_process_masks(*snake_case							,				**snake_case    )








'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	int    )		->							List[Any]:
          if dst_width < 0 or dst_height < 0:
               raise ValueError('''Destination width/height should be > 0'''    )

          __UpperCAmelCase			:				str													=    img
          __UpperCAmelCase			:				List[Any]													=    img.shape[1]
          __UpperCAmelCase			:				Optional[Any]													=    img.shape[0]
          __UpperCAmelCase			:				Dict													=    dst_width
          __UpperCAmelCase			:				List[str]													=    dst_height

          __UpperCAmelCase			:				Union[str, Any]													=    self.src_w / self.dst_w
          __UpperCAmelCase			:				List[str]													=    self.src_h / self.dst_h

          __UpperCAmelCase			:				Optional[int]													=    (
              np.ones((self.dst_h, self.dst_w, 3)							,				np.uinta    ) * 255
          )

     def 				lowerCamelCase__				(       self  :	Any    )		->							str:
          for i in range(self.dst_h    ):
               for j in range(self.dst_w    ):
                    __UpperCAmelCase			:				Any													=    self.img[self.get_y(snake_case    )][self.get_x(snake_case    )]

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	int    )		->							int:
          return int(self.ratio_x * x    )






     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	int    )		->							int:
          return int(self.ratio_y * y    )


if __name__ == "__main__":
      __UpperCAmelCase       ,					__UpperCAmelCase    :int				       =     8_0_0, 6_0_0
      __UpperCAmelCase    :Dict				       =     imread("image_data/lena.jpg", 1)
      __UpperCAmelCase    :int				       =     NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()