



'''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'''







 __lowerCamelCase       :			Union[str, Any]		=			['image_processor']
 __lowerCamelCase       :			Dict		=			'SamImageProcessor'



 def __init__(self					,  A			)       ->	List[Any]:


      """simple docstring"""



      super().__init__(A			)
      _a           =    self.image_processor
      _a           =    -10
      _a           =    self.image_processor.size['''longest_edge''']



 def __call__(self					,  A=None					,  A=None					,  A=None					,  A=None					,  A = None					,  **A					,  )       ->	BatchEncoding:


      """simple docstring"""



      _a           =    self.image_processor(
          A					,  return_tensors=A					,  **A					,  )

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

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

      _a    ,       _a    ,       _a           =    self._check_and_preprocess_points(
          input_points=A					,  input_labels=A					,  input_boxes=A					,  )

      _a           =    self._normalize_and_convert(
          A					,  A					,  input_points=A					,  input_labels=A					,  input_boxes=A					,  return_tensors=A					,  )

      return encoding_image_processor



 def    a__    (self					,  A					,  A					,  A=None					,  A=None					,  A=None					,  A="pt"					,  )       ->	Dict:


      """simple docstring"""



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

           _a           =    np.array(A			)

      if input_labels is not None:
           _a           =    np.array(A			)

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

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

      return encoding_image_processor



 def    a__    (self					,  A					,  A			)       ->	Dict:


      """simple docstring"""



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



 def    a__    (self					,  A					,  A					,  A					,  A=False			)       ->	np.ndarray:


      """simple docstring"""



      _a    ,       _a           =    original_size
      _a    ,       _a           =    self.image_processor._get_preprocess_shape(A					,  longest_edge=A			)
      _a           =    deepcopy(A			).astype(A			)

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

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

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

      return coords



 def    a__    (self					,  A=None					,  A=None					,  A=None					,  )       ->	Dict:


      """simple docstring"""



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

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

      if input_labels is not None:
           if hasattr(A					,  '''numpy'''			):
                _a           =    input_labels.numpy().tolist()

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

      if input_boxes is not None:
           if hasattr(A					,  '''numpy'''			):
                _a           =    input_boxes.numpy().tolist()

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

      return input_points, input_labels, input_boxes



 @property
 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    self.image_processor.model_input_names
      return list(dict.fromkeys(A			)			)



 def    a__    (self					,  *A					,  **A			)       ->	List[str]:


      """simple docstring"""



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








from __future__ import annotations

import queue


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= data
											a__	:							Tuple      						= None
											a__	:							Optional[Any]      						= None






def     SCREAMING_SNAKE_CASE(      )	->						TreeNode:
				print("\n********Press N to stop entering at any point of time********\n" )
				a__	:							Tuple      						= input("Enter the value of the root node: " ).strip().lower()
				a__	:							queue.Queue      						= queue.Queue()
				a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							str      						= q.get()
								a__	:							Optional[Any]      						= F'Enter the left node of {node_found.data}: '
								a__	:							List[Any]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							Optional[int]      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							List[str]      						= left_node
								q.put(__UpperCamelCase )
								a__	:							Dict      						= F'Enter the right node of {node_found.data}: '
								a__	:							List[str]      						= input(__UpperCamelCase ).strip().lower() or "n"
								if check == "n":
												return tree_node
								a__	:							str      						= TreeNode(int(__UpperCamelCase ) )
								a__	:							Any      						= right_node
								q.put(__UpperCamelCase )
				raise






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				print(node.data	,				end="," )
				pre_order(node.left )
				pre_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				in_order(node.left )
				print(node.data	,				end="," )
				in_order(node.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				post_order(node.left )
				post_order(node.right )
				print(node.data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							Tuple      						= q.get()
								print(node_dequeued.data	,				end="," )
								if node_dequeued.left:
												q.put(node_dequeued.left )
								if node_dequeued.right:
												q.put(node_dequeued.right )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							queue.Queue      						= queue.Queue()
				q.put(__UpperCamelCase )
				while not q.empty():
								a__	:							int      						= []
								while not q.empty():
												a__	:							Union[str, Any]      						= q.get()
												print(node_dequeued.data	,				end="," )
												if node_dequeued.left:
																list_.append(node_dequeued.left )
												if node_dequeued.right:
																list_.append(node_dequeued.right )
								print()
								for node in list_:
												q.put(__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Optional[int]      						= node
				while n or stack:
								while n:  # start from root node, find its left child
												print(n.data	,				end="," )
												stack.append(__UpperCamelCase )
												a__	:							int      						= n.left
								# end of while means current node doesn't have left child
								a__	:							Dict      						= stack.pop()
								# start to traverse its right child
								a__	:							str      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__	:							list[TreeNode]      						= []
				a__	:							Dict      						= node
				while n or stack:
								while n:
												stack.append(__UpperCamelCase )
												a__	:							List[Any]      						= n.left
								a__	:							str      						= stack.pop()
								print(n.data	,				end="," )
								a__	:							Any      						= n.right






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						None:
				if not isinstance(__UpperCamelCase	,				__UpperCamelCase ) or not node:
								return
				a__						,   a__	:							List[str]      						= [], []
				a__	:							List[Any]      						= node
				stacka.append(__UpperCamelCase )
				while stacka:  # to find the reversed order of post order, store it in stack2
								a__	:							Optional[Any]      						= stacka.pop()
								if n.left:
												stacka.append(n.left )
								if n.right:
												stacka.append(n.right )
								stacka.append(__UpperCamelCase )
				while stacka:  # pop up from stack2 will be the post order
								print(stacka.pop().data	,				end="," )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = ""	,				__UpperCamelCase=50	,				__UpperCamelCase="*" )	->						str:
				if not s:
								return "\n" + width * char
				a__						,   a__	:							int      						= divmod(width - len(__UpperCamelCase ) - 2	,				2 )
				return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt("""Binary Tree Traversals"""))

						lowerCamelCase		       =      build_tree()
						print(prompt("""Pre Order Traversal"""))
						pre_order(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal"""))
						in_order(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal"""))
						post_order(node)
						print(prompt() + """\n""")

						print(prompt("""Level Order Traversal"""))
						level_order(node)
						print(prompt() + """\n""")

						print(prompt("""Actual Level Order Traversal"""))
						level_order_actual(node)
						print("""*""" * 50 + """\n""")

						print(prompt("""Pre Order Traversal - Iteration Version"""))
						pre_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""In Order Traversal - Iteration Version"""))
						in_order_iter(node)
						print(prompt() + """\n""")

						print(prompt("""Post Order Traversal - Iteration Version"""))
						post_order_iter(node)
						print(prompt())


