


"""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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Union[str, Any]        =   ["image_processor"]
       __A			:      Union[str, Any]        =   "SamImageProcessor"






       def __init__(   self  :    Tuple  ,     snake_case__  :    List[Any]				):

              '''simple docstring'''






              super().__init__(snake_case__				)
              lowercase		:List[Any]       							=				self.image_processor
              lowercase		:List[str]       							=				-1_0
              lowercase		:Dict       							=				self.image_processor.size['''longest_edge''']






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

              '''simple docstring'''






              lowercase		:List[Any]       							=				self.image_processor(
                  snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__  ,     )

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

              if hasattr(snake_case__  ,     '''numpy'''				):  # Checks if Torch or TF tensor
                     lowercase		:Optional[Any]       							=				original_sizes.numpy()

              lowercase							,  lowercase							,  lowercase		:int       							=				self._check_and_preprocess_points(
                  input_points=snake_case__  ,     input_labels=snake_case__  ,     input_boxes=snake_case__  ,     )

              lowercase		:Union[str, Any]       							=				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  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Dict  ,     snake_case__  :    Tuple=None  ,     snake_case__  :    str=None  ,     snake_case__  :    List[Any]=None  ,     snake_case__  :    Any="pt"  ,     ):

              '''simple docstring'''






              if input_points is not None:
                     if len(snake_case__				) != len(snake_case__				):
                            lowercase		:List[Any]       							=				[
                                self._normalize_coordinates(self.target_size  ,     snake_case__  ,     original_sizes[0]				) for point in input_points
                            ]
                     else:
                            lowercase		:Any       							=				[
                                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:
                                   lowercase							,  lowercase		:Optional[int]       							=				self._pad_points_and_labels(snake_case__  ,     snake_case__				)

                     lowercase		:Union[str, Any]       							=				np.array(snake_case__				)

              if input_labels is not None:
                     lowercase		:str       							=				np.array(snake_case__				)

              if input_boxes is not None:
                     if len(snake_case__				) != len(snake_case__				):
                            lowercase		:Optional[Any]       							=				[
                                self._normalize_coordinates(self.target_size  ,     snake_case__  ,     original_sizes[0]  ,     is_bounding_box=snake_case__				)
                                for box in input_boxes
                            ]
                     else:
                            lowercase		:List[Any]       							=				[
                                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__				)
                            ]
                     lowercase		:Any       							=				np.array(snake_case__				)

              if input_boxes is not None:
                     if return_tensors == "pt":
                            lowercase		:Optional[int]       							=				torch.from_numpy(snake_case__				)
                            # boxes batch size of 1 by default
                            lowercase		:Optional[int]       							=				input_boxes.unsqueeze(1				) if len(input_boxes.shape				) != 3 else input_boxes
                     elif return_tensors == "tf":
                            lowercase		:List[str]       							=				tf.convert_to_tensor(snake_case__				)
                            # boxes batch size of 1 by default
                            lowercase		:int       							=				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":
                            lowercase		:Optional[int]       							=				torch.from_numpy(snake_case__				)
                            # point batch size of 1 by default
                            lowercase		:List[str]       							=				input_points.unsqueeze(1				) if len(input_points.shape				) != 4 else input_points
                     elif return_tensors == "tf":
                            lowercase		:Optional[int]       							=				tf.convert_to_tensor(snake_case__				)
                            # point batch size of 1 by default
                            lowercase		:List[Any]       							=				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":
                            lowercase		:Any       							=				torch.from_numpy(snake_case__				)
                            # point batch size of 1 by default
                            lowercase		:List[Any]       							=				input_labels.unsqueeze(1				) if len(input_labels.shape				) != 3 else input_labels
                     elif return_tensors == "tf":
                            lowercase		:List[str]       							=				tf.convert_to_tensor(snake_case__				)
                            # point batch size of 1 by default
                            lowercase		:List[str]       							=				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  __snake_case						(   self  :    Dict  ,     snake_case__  :    Any  ,     snake_case__  :    Dict				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    int  ,     snake_case__  :    np.ndarray  ,     snake_case__  :    List[str]  ,     snake_case__  :    Any=False				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[str]       							=				original_size
              lowercase							,  lowercase		:Optional[int]       							=				self.image_processor._get_preprocess_shape(snake_case__  ,     longest_edge=snake_case__				)
              lowercase		:str       							=				deepcopy(snake_case__				).astype(snake_case__				)

              if is_bounding_box:
                     lowercase		:Union[str, Any]       							=				coords.reshape(-1  ,     2  ,     2				)

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

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

              return coords






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Optional[Any]=None  ,     snake_case__  :    List[Any]=None  ,     snake_case__  :    Any=None  ,     ):

              '''simple docstring'''






              if input_points is not None:
                     if hasattr(snake_case__  ,     '''numpy'''				):  # Checks for TF or Torch tensor
                            lowercase		:List[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.'''				)
                     lowercase		:List[str]       							=				[np.array(snake_case__				) for input_point in input_points]
              else:
                     lowercase		:Union[str, Any]       							=				None

              if input_labels is not None:
                     if hasattr(snake_case__  ,     '''numpy'''				):
                            lowercase		: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.'''				)
                     lowercase		:Optional[int]       							=				[np.array(snake_case__				) for label in input_labels]
              else:
                     lowercase		:Optional[Any]       							=				None

              if input_boxes is not None:
                     if hasattr(snake_case__  ,     '''numpy'''				):
                            lowercase		:Optional[int]       							=				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.'''				)
                     lowercase		:int       							=				[np.array(snake_case__				).astype(np.floataa				) for box in input_boxes]
              else:
                     lowercase		:Tuple       							=				None

              return input_points, input_labels, input_boxes






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

              '''simple docstring'''






              lowercase		:int       							=				self.image_processor.model_input_names
              return list(dict.fromkeys(snake_case__				)				)







       def  __snake_case						(   self  :    Tuple  ,     *snake_case__  :    str  ,     **snake_case__  :    int				):

              '''simple docstring'''






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





"""simple docstring"""



from __future__ import annotations

from decimal import Decimal

from numpy import array






def       lowerCamelCase					(a_						:list[list[float]])     ->							list[list[float]]:
       lowercase		:List[str]       							=				Decimal

       # Check if the provided matrix has 2 rows and 2 columns
       # since this implementation only works for 2x2 matrices
       if len(a_) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
              # Calculate the determinant of the matrix
              lowercase		:List[str]       							=				float(
                  d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
              if determinant == 0:
                     raise ValueError('''This matrix has no inverse.''')

              # Creates a copy of the matrix with swapped positions of the elements
              lowercase		:Optional[Any]       							=				[[0.0, 0.0], [0.0, 0.0]]
              lowercase							,  lowercase		:List[Any]       							=				matrix[1][1], matrix[0][0]
              lowercase							,  lowercase		:Union[str, Any]       							=				-matrix[1][0], -matrix[0][1]

              # Calculate the inverse of the matrix
              return [
                  [(float(d(a_)) / determinant) or 0.0 for n in row] for row in swapped_matrix
              ]
       elif (
           len(a_) == 3
           and len(matrix[0]) == 3
           and len(matrix[1]) == 3
           and len(matrix[2]) == 3
       ):
              # Calculate the determinant of the matrix using Sarrus rule
              lowercase		:Any       							=				float(
                  (
                      (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
                      + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
                      + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
                  )
                  - (
                      (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
                      + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
                      + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
                  ))
              if determinant == 0:
                     raise ValueError('''This matrix has no inverse.''')

              # Creating cofactor matrix
              lowercase		:Optional[Any]       							=				[
                  [d(0.0), d(0.0), d(0.0)],
                  [d(0.0), d(0.0), d(0.0)],
                  [d(0.0), d(0.0), d(0.0)],
              ]
              lowercase		:Optional[int]       							=				(d(matrix[1][1]) * d(matrix[2][2])) - (
                  d(matrix[1][2]) * d(matrix[2][1])
              )
              lowercase		:str       							=				-(
                  (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[1][0]) * d(matrix[2][1])) - (
                  d(matrix[1][1]) * d(matrix[2][0])
              )
              lowercase		:Union[str, Any]       							=				-(
                  (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[0][0]) * d(matrix[2][2])) - (
                  d(matrix[0][2]) * d(matrix[2][0])
              )
              lowercase		:List[str]       							=				-(
                  (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[0][1]) * d(matrix[1][2])) - (
                  d(matrix[0][2]) * d(matrix[1][1])
              )
              lowercase		:Union[str, Any]       							=				-(
                  (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
              )
              lowercase		:Any       							=				(d(matrix[0][0]) * d(matrix[1][1])) - (
                  d(matrix[0][1]) * d(matrix[1][0])
              )

              # Transpose the cofactor matrix (Adjoint matrix)
              lowercase		:Union[str, Any]       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            lowercase		:Any       							=				cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
              lowercase		:str       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            inverse_matrix[i][j] /= d(a_)

        # Calculate the inverse of the matrix
              return [[float(d(a_)) or 0.0 for n in row] for row in inverse_matrix]
       raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''')

