





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     __lowerCAmelCase	(   __UpperCamelCase):
      _lowercase    : Optional[int]            =      ["""image_processor"""]
      _lowercase    : Union[str, Any]            =      """SamImageProcessor"""







      def __init__(   self		,				lowerCAmelCase__							)       ->						Any:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__							)
        a__     : Any  =self.image_processor
        a__     : Tuple  =-1_0
        a__     : Tuple  =self.image_processor.size["""longest_edge"""]







      def __call__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : List[Any]  =self.image_processor(
            lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

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

        if hasattr(lowerCAmelCase__		,				"numpy"							):  # Checks if Torch or TF tensor
          a__     : Union[str, Any]  =original_sizes.numpy()

        a__     : List[str]  =self._check_and_preprocess_points(
            input_points=lowerCAmelCase__		,				input_labels=lowerCAmelCase__		,				input_boxes=lowerCAmelCase__		,				)

        a__     : Optional[int]  =self._normalize_and_convert(
            lowerCAmelCase__		,				lowerCAmelCase__		,				input_points=lowerCAmelCase__		,				input_labels=lowerCAmelCase__		,				input_boxes=lowerCAmelCase__		,				return_tensors=lowerCAmelCase__		,				)

        return encoding_image_processor







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__="pt"		,				)       ->						Optional[int]:



        '''simple docstring'''




        if input_points is not None:
          if len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            a__     : List[Any]  =[
                self._normalize_coordinates(self.target_size		,				lowerCAmelCase__		,				original_sizes[0]							) for point in input_points
            ]
          else:
            a__     : Tuple  =[
                self._normalize_coordinates(self.target_size		,				lowerCAmelCase__		,				lowerCAmelCase__							)
                for point, original_size in zip(lowerCAmelCase__		,				lowerCAmelCase__							)
            ]
          # 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__     : Union[str, Any]  =self._pad_points_and_labels(lowerCAmelCase__		,				lowerCAmelCase__							)

          a__     : str  =np.array(lowerCAmelCase__							)

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

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

        if input_boxes is not None:
          if return_tensors == "pt":
            a__     : Optional[int]  =torch.from_numpy(lowerCAmelCase__							)
            # boxes batch size of 1 by default
            a__     : List[Any]  =input_boxes.unsqueeze(1							) if len(input_boxes.shape							) != 3 else input_boxes
          elif return_tensors == "tf":
            a__     : str  =tf.convert_to_tensor(lowerCAmelCase__							)
            # boxes batch size of 1 by default
            a__     : Tuple  =tf.expand_dims(lowerCAmelCase__		,				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__     : Union[str, Any]  =torch.from_numpy(lowerCAmelCase__							)
            # point batch size of 1 by default
            a__     : List[Any]  =input_points.unsqueeze(1							) if len(input_points.shape							) != 4 else input_points
          elif return_tensors == "tf":
            a__     : str  =tf.convert_to_tensor(lowerCAmelCase__							)
            # point batch size of 1 by default
            a__     : Any  =tf.expand_dims(lowerCAmelCase__		,				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__     : Dict  =torch.from_numpy(lowerCAmelCase__							)
            # point batch size of 1 by default
            a__     : List[Any]  =input_labels.unsqueeze(1							) if len(input_labels.shape							) != 3 else input_labels
          elif return_tensors == "tf":
            a__     : int  =tf.convert_to_tensor(lowerCAmelCase__							)
            # point batch size of 1 by default
            a__     : Dict  =tf.expand_dims(lowerCAmelCase__		,				1							) if len(input_labels.shape							) != 3 else input_labels
          encoding_image_processor.update({"input_labels": input_labels}							)

        return encoding_image_processor







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Any:



        '''simple docstring'''




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







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=False							)       ->						Tuple:



        '''simple docstring'''




        a__     : List[Any]  =original_size
        a__     : Optional[int]  =self.image_processor._get_preprocess_shape(lowerCAmelCase__		,				longest_edge=lowerCAmelCase__							)
        a__     : Optional[int]  =deepcopy(lowerCAmelCase__							).astype(lowerCAmelCase__							)

        if is_bounding_box:
          a__     : Optional[int]  =coords.reshape(-1		,				2		,				2							)

        a__     : List[str]  =coords[..., 0] * (new_w / old_w)
        a__     : str  =coords[..., 1] * (new_h / old_h)

        if is_bounding_box:
          a__     : List[str]  =coords.reshape(-1		,				4							)

        return coords







      def        _lowercase  (   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				)       ->						Union[str, Any]:



        '''simple docstring'''




        if input_points is not None:
          if hasattr(lowerCAmelCase__		,				"numpy"							):  # Checks for TF or Torch tensor
            a__     : Optional[int]  =input_points.numpy().tolist()

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

        if input_labels is not None:
          if hasattr(lowerCAmelCase__		,				"numpy"							):
            a__     : List[str]  =input_labels.numpy().tolist()

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

        if input_boxes is not None:
          if hasattr(lowerCAmelCase__		,				"numpy"							):
            a__     : str  =input_boxes.numpy().tolist()

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

        return input_points, input_labels, input_boxes







      @property
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.image_processor.model_input_names
        return list(dict.fromkeys(lowerCAmelCase__							)							)







      def        _lowercase  (   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




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













from __future__ import annotations







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =TypeError(
            "Matrices must be formed from a list of zero or more lists containing at "
            "least one and the same number of values, each of which must be of type "
            "int or float."							)
        if len(lowerCAmelCase__							) != 0:
          a__     : List[str]  =len(rows[0]							)
          if cols == 0:
            raise error
          for row in rows:
            if len(lowerCAmelCase__							) != cols:
              raise error
            for value in row:
              if not isinstance(lowerCAmelCase__		,				(int, float)							):
                raise error
          a__     : List[Any]  =rows
        else:
          a__     : str  =[]







      def        _lowercase  (   self							)       ->						list[list[int]]:



        '''simple docstring'''




        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows[0]							)







      @property
      def        _lowercase  (   self							)       ->						tuple[int, int]:



        '''simple docstring'''




        return (self.num_rows, self.num_columns)







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return self.order[0] == self.order[1]







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : str  =[
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
            for row_num in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        if not self.is_square:
          return 0
        if self.order == (0, 0):
          return 1
        if self.order == (1, 1):
          return int(self.rows[0][0]							)
        if self.order == (2, 2):
          return int(
              (self.rows[0][0] * self.rows[1][1])
              - (self.rows[0][1] * self.rows[1][0])							)
        else:
          return sum(
              self.rows[0][column] * self.cofactors().rows[0][column]
              for column in range(self.num_columns							)							)







      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return bool(self.determinant()							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  =[
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns							)
                if other_column != column
            ]
            for other_row in range(self.num_rows							)
            if other_row != row
        ]
        return Matrix(lowerCAmelCase__							).determinant()







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        if (row + column) % 2 == 0:
          return self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)
        return -1 * self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							) for column in range(self.num_columns							)]
                for row in range(self.num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns							)
                ]
                for row in range(self.minors().num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Dict  =[
            [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
            for row in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.determinant()
        if not determinant:
          raise TypeError("Only matrices with a non-zero determinant have an inverse"							)
        return self.adjugate() * (1 / determinant)







      def __repr__(   self							)       ->						str:



        '''simple docstring'''




        return str(self.rows							)







      def __str__(   self							)       ->						str:



        '''simple docstring'''




        if self.num_rows == 0:
          return "[]"
        if self.num_rows == 1:
          return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    "[" + ". ".join([str(lowerCAmelCase__							) for value in row]							) + ".]"
                    for row in self.rows
                ]							)
            + "]"
        )







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : List[str]  =TypeError("Row must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in row:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_columns:
          raise ValueError(
              "Row must be equal in length to the other rows in the matrix"							)
        if position is None:
          self.rows.append(lowerCAmelCase__							)
        else:
          a__     : Tuple  =self.rows[0:position] + [row] + self.rows[position:]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : str  =TypeError(
            "Column must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in column:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_rows:
          raise ValueError(
              "Column must be equal in length to the other columns in the matrix"							)
        if position is None:
          a__     : Optional[Any]  =[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
        else:
          a__     : Any  =[
              self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
              for i in range(self.num_rows							)
          ]







      def __eq__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          return NotImplemented
        return self.rows == other.rows







      def __ne__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        return not self == other







      def __neg__(   self							)       ->						Matrix:



        '''simple docstring'''




        return self * -1







      def __add__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Addition requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __sub__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Subtraction requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __mul__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				(int, float)							):
          return Matrix(
              [[int(element * other							) for element in row] for row in self.rows]							)
        elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          if self.num_columns != other.num_rows:
            raise ValueError(
                "The number of columns in the first matrix must "
                "be equal to the number of rows in the second"							)
          return Matrix(
              [
                  [Matrix.dot_product(lowerCAmelCase__		,				lowerCAmelCase__							) for column in other.columns()]
                  for row in self.rows
              ]							)
        else:
          raise TypeError(
              "A Matrix can only be multiplied by an int, float, or another matrix"							)







      def __pow__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise TypeError("A Matrix can only be raised to the power of an int"							)
        if not self.is_square:
          raise ValueError("Only square matrices can be raised to a power"							)
        if other == 0:
          return self.identity()
        if other < 0:
          if self.is_invertable():
            return self.inverse() ** (-other)
          raise ValueError(
              "Only invertable matrices can be raised to a negative power"							)
        a__     : Tuple  =self
        for _ in range(other - 1							):
          result *= self
        return result







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        return sum(row[i] * column[i] for i in range(len(lowerCAmelCase__							)							)							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






