

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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     ['''image_processor''']
			_UpperCAmelCase   :							List[str]										=     '''SamImageProcessor'''



			def __init__( self  :     str   ,						lowerCAmelCase__  :     Any):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.image_processor
									SCREAMING_SNAKE_CASE_: List[Any]  		=       -10
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.image_processor.size["longest_edge"]



			def __call__( self  :     str   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     List[Any]=None   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     Optional[Union[str, TensorType]] = None   ,						**lowerCAmelCase__  :     str   ,						):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.image_processor(
									    lowerCAmelCase__   ,						return_tensors=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									# pop arguments that are not used in the foward but used nevertheless
									SCREAMING_SNAKE_CASE_: List[str]  		=       encoding_image_processor["original_sizes"]

									if hasattr(lowerCAmelCase__   ,						"numpy"):  # Checks if Torch or TF tensor
															SCREAMING_SNAKE_CASE_: Any  		=       original_sizes.numpy()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._check_and_preprocess_points(
									    input_points=lowerCAmelCase__   ,						input_labels=lowerCAmelCase__   ,						input_boxes=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: int  		=       self._normalize_and_convert(
									    lowerCAmelCase__   ,						lowerCAmelCase__   ,						input_points=lowerCAmelCase__   ,						input_labels=lowerCAmelCase__   ,						input_boxes=lowerCAmelCase__   ,						return_tensors=lowerCAmelCase__   ,						)

									return encoding_image_processor



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     str=None   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     int="pt"   ,						):
									if input_points is not None:
															if len(lowerCAmelCase__) != len(lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Any  		=       [
																					    self._normalize_coordinates(self.target_size   ,						lowerCAmelCase__   ,						original_sizes[0]) for point in input_points
																					]
															else:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       [
																					    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:
																											SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self._pad_points_and_labels(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.array(lowerCAmelCase__)

									if input_labels is not None:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       np.array(lowerCAmelCase__)

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

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     np.ndarray   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[Any]=False):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       original_size
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.image_processor._get_preprocess_shape(lowerCAmelCase__   ,						longest_edge=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       deepcopy(lowerCAmelCase__).astype(lowerCAmelCase__)

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

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

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

									return coords



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Any=None   ,						lowerCAmelCase__  :     List[str]=None   ,						lowerCAmelCase__  :     Dict=None   ,						):
									if input_points is not None:
															if hasattr(lowerCAmelCase__   ,						"numpy"):  # Checks for TF or Torch tensor
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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.")
															SCREAMING_SNAKE_CASE_: List[Any]  		=       [np.array(lowerCAmelCase__) for input_point in input_points]
									else:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       None

									if input_labels is not None:
															if hasattr(lowerCAmelCase__   ,						"numpy"):
																					SCREAMING_SNAKE_CASE_: 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.")
															SCREAMING_SNAKE_CASE_: Any  		=       [np.array(lowerCAmelCase__) for label in input_labels]
									else:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       None

									if input_boxes is not None:
															if hasattr(lowerCAmelCase__   ,						"numpy"):
																					SCREAMING_SNAKE_CASE_: Tuple  		=       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.")
															SCREAMING_SNAKE_CASE_: List[str]  		=       [np.array(lowerCAmelCase__).astype(np.floataa) for box in input_boxes]
									else:
															SCREAMING_SNAKE_CASE_: Dict  		=       None

									return input_points, input_labels, input_boxes



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.image_processor.model_input_names
									return list(dict.fromkeys(lowerCAmelCase__))




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						*lowerCAmelCase__  :     Optional[Any]   ,						**lowerCAmelCase__  :     Union[str, Any]):
									return self.image_processor.post_process_masks(*lowerCAmelCase__   ,						**lowerCAmelCase__)











'''simple docstring'''




from collections.abc import Iterable
from typing import Any






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Optional[int]							,     lowerCAmelCase__     : int | None = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		value
					__SCREAMING_SNAKE_CASE       :		Node | None           =		None  # Added in order to delete a node easier
					__SCREAMING_SNAKE_CASE       :		Node | None           =		None
					__SCREAMING_SNAKE_CASE       :		Node | None           =		None



			def __repr__(							self     : Optional[Any]						):





					"""simple docstring"""






					from pprint import pformat

					if self.left is None and self.right is None:
							return str(self.value						)
					return pformat({F"{self.value}": (self.left, self.right)}							,     indent=1						)






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Optional[int]							,     lowerCAmelCase__     : Node | None = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		root

			def __str__(							self     : Union[str, Any]						):





					"""simple docstring"""






					return str(self.root						)

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Node							,     lowerCAmelCase__     : Node | None						):





					"""simple docstring"""






					if new_children is not None:  # reset its kids
							__SCREAMING_SNAKE_CASE       :		List[str]           =		node.parent
					if node.parent is not None:  # reset its parent
							if self.is_right(lowerCAmelCase__						):  # If it is the right children
									__SCREAMING_SNAKE_CASE       :		Any           =		new_children
							else:
									__SCREAMING_SNAKE_CASE       :		int           =		new_children
					else:
							__SCREAMING_SNAKE_CASE       :		int           =		new_children

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Node						):





					"""simple docstring"""






					if node.parent and node.parent.right:
							return node == node.parent.right
					return False

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return self.root is None

			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		Node(lowerCAmelCase__						)  # create a new Node
					if self.empty():  # if Tree is empty
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		new_node  # set its root
					else:  # Tree is not empty
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.root  # from root
							if parent_node is None:
									return
							while True:  # While we don't get to a leaf
									if value < parent_node.value:  # We go left
											if parent_node.left is None:
													__SCREAMING_SNAKE_CASE       :		List[str]           =		new_node  # We insert the new node in a leaf
													break
											else:
													__SCREAMING_SNAKE_CASE       :		Any           =		parent_node.left
									else:
											if parent_node.right is None:
													__SCREAMING_SNAKE_CASE       :		Tuple           =		new_node
													break
											else:
													__SCREAMING_SNAKE_CASE       :		List[str]           =		parent_node.right
							__SCREAMING_SNAKE_CASE       :		Tuple           =		parent_node

			def UpperCamelCase__					(							self     : str							,     *lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					for value in values:
							self.__insert(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					if self.empty():
							raise IndexError("""Warning: Tree is empty! please use another."""						)
					else:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.root
							# use lazy evaluation here to avoid NoneType Attribute error
							while node is not None and node.value is not value:
									__SCREAMING_SNAKE_CASE       :		Any           =		node.left if value < node.value else node.right
							return node

			def UpperCamelCase__					(							self     : List[Any]							,     lowerCAmelCase__     : Node | None = None						):





					"""simple docstring"""






					if node is None:
							if self.root is None:
									return None
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.root

					if not self.empty():
							while node.right is not None:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		node.right
					return node

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Node | None = None						):





					"""simple docstring"""






					if node is None:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.root
					if self.root is None:
							return None
					if not self.empty():
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.root
							while node.left is not None:
									__SCREAMING_SNAKE_CASE       :		Any           =		node.left
					return node

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.search(lowerCAmelCase__						)  # Look for the node with that label
					if node is not None:
							if node.left is None and node.right is None:  # If it has no children
									self.__reassign_nodes(lowerCAmelCase__							,     lowerCAmelCase__						)
							elif node.left is None:  # Has only right children
									self.__reassign_nodes(lowerCAmelCase__							,     node.right						)
							elif node.right is None:  # Has only left children
									self.__reassign_nodes(lowerCAmelCase__							,     node.left						)
							else:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		self.get_max(
									    node.left						)  # Gets the max value of the left branch
									self.remove(tmp_node.value						)  # type: ignore
									__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		(
									    tmp_node.value  # type: ignore
									)  # Assigns the value to the node to delete and keep tree structure

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : Node | None						):





					"""simple docstring"""






					if node is not None:
							yield node  # Preorder Traversal
							yield from self.preorder_traverse(node.left						)
							yield from self.preorder_traverse(node.right						)

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Optional[Any]=None						):





					"""simple docstring"""






					if traversal_function is None:
							return self.preorder_traverse(self.root						)
					else:
							return traversal_function(self.root						)

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : list							,     lowerCAmelCase__     : Node | None						):





					"""simple docstring"""






					if node:
							self.inorder(lowerCAmelCase__							,     node.left						)
							arr.append(node.value						)
							self.inorder(lowerCAmelCase__							,     node.right						)



			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : Node						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		list[int]           =		[]
					self.inorder(lowerCAmelCase__							,     lowerCAmelCase__						)  # append all values to list using inorder traversal
					return arr[k - 1]


def 				lowerCAmelCase_   (	_lowerCamelCase:   Node | None					):
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[]
		if curr_node is not None:
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		postorder(curr_node.left					) + postorder(curr_node.right					) + [curr_node]
		return node_list


def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		str           =		(8, 3, 6, 1, 10, 14, 13, 4, 7)
		__SCREAMING_SNAKE_CASE       :		Dict           =		BinarySearchTree()
		for i in testlist:
				t.insert(_lowerCamelCase					)

		# Prints all the elements of the list in order traversal
		print(_lowerCamelCase					)

		if t.search(6					) is not None:
				print("""The value 6 exists"""					)
		else:
				print("""The value 6 doesn't exist"""					)

		if t.search(-1					) is not None:
				print("""The value -1 exists"""					)
		else:
				print("""The value -1 doesn't exist"""					)

		if not t.empty():
				print("""Max Value: """       ,      t.get_max().value					)  # type: ignore
				print("""Min Value: """       ,      t.get_min().value					)  # type: ignore

		for i in testlist:
				t.remove(_lowerCamelCase					)
				print(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)