


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			(_a						):
       _lowercase											=     ["""image_processor"""]
       _lowercase											=     """SamImageProcessor"""







       def __init__(			self: Dict,A_: Union[str, Any]					):






            '''simple docstring'''




            super().__init__(A_					)
            __UpperCamelCase =			self.image_processor
            __UpperCamelCase =			-10
            __UpperCamelCase =			self.image_processor.size['longest_edge']







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






            '''simple docstring'''




            __UpperCamelCase =			self.image_processor(
                A_,return_tensors=A_,**A_,)

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

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

            __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			self._check_and_preprocess_points(
                input_points=A_,input_labels=A_,input_boxes=A_,)

            __UpperCamelCase =			self._normalize_and_convert(
                A_,A_,input_points=A_,input_labels=A_,input_boxes=A_,return_tensors=A_,)

            return encoding_image_processor







       def 		snake_case_	(			self: Tuple,A_: Any,A_: str,A_: Dict=None,A_: Dict=None,A_: int=None,A_: List[Any]="pt",):






            '''simple docstring'''




            if input_points is not None:
                 if len(A_					) != len(A_					):
                      __UpperCamelCase =			[
                          self._normalize_coordinates(self.target_size,A_,original_sizes[0]					) for point in input_points
                      ]
                 else:
                      __UpperCamelCase =			[
                          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:
                           __UpperCamelCase,    __UpperCamelCase =			self._pad_points_and_labels(A_,A_					)

                 __UpperCamelCase =			np.array(A_					)

            if input_labels is not None:
                 __UpperCamelCase =			np.array(A_					)

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

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






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[int],A_: int,A_: np.ndarray,A_: Union[str, Any],A_: int=False					):






            '''simple docstring'''




            __UpperCamelCase,    __UpperCamelCase =			original_size
            __UpperCamelCase,    __UpperCamelCase =			self.image_processor._get_preprocess_shape(A_,longest_edge=A_					)
            __UpperCamelCase =			deepcopy(A_					).astype(A_					)

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

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

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

            return coords







       def 		snake_case_	(			self: Dict,A_: Optional[Any]=None,A_: List[str]=None,A_: Tuple=None,):






            '''simple docstring'''




            if input_points is not None:
                 if hasattr(A_,'numpy'					):  # Checks for TF or Torch tensor
                      __UpperCamelCase =			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.'					)
                 __UpperCamelCase =			[np.array(A_					) for input_point in input_points]
            else:
                 __UpperCamelCase =			None

            if input_labels is not None:
                 if hasattr(A_,'numpy'					):
                      __UpperCamelCase =			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.'					)
                 __UpperCamelCase =			[np.array(A_					) for label in input_labels]
            else:
                 __UpperCamelCase =			None

            if input_boxes is not None:
                 if hasattr(A_,'numpy'					):
                      __UpperCamelCase =			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.'					)
                 __UpperCamelCase =			[np.array(A_					).astype(np.floataa					) for box in input_boxes]
            else:
                 __UpperCamelCase =			None

            return input_points, input_labels, input_boxes







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






            '''simple docstring'''




            __UpperCamelCase =			self.image_processor.model_input_names
            return list(dict.fromkeys(A_					)					)







       def 		snake_case_	(			self: int,*A_: int,**A_: Tuple					):






            '''simple docstring'''




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






from typing import Any



class 	__lowerCamelCase			:







       def __init__(			self: int,A_: Any					):






            '''simple docstring'''




            __UpperCamelCase =			data
            __UpperCamelCase =			None







       def __repr__(			self: Any					):






            '''simple docstring'''




            return F'''Node({self.data})'''



class 	__lowerCamelCase			:







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






            '''simple docstring'''




            __UpperCamelCase =			None







       def __iter__(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			self.head
            while node:
                 yield node.data
                 __UpperCamelCase =			node.next







       def __len__(			self: List[str]					):






            '''simple docstring'''




            return sum(1 for _ in self					)







       def __repr__(			self: Any					):






            '''simple docstring'''




            return "->".join([str(A_					) for item in self]					)







       def __getitem__(			self: int,A_: int					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            for i, node in enumerate(self					):
                 if i == index:
                      return node
            return None







       def __setitem__(			self: int,A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index < len(self					):
                 raise ValueError('list index out of range.'					)
            __UpperCamelCase =			self.head
            for _ in range(A_					):
                 __UpperCamelCase =			current.next
            __UpperCamelCase =			data







       def 		snake_case_	(			self: Union[str, Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(len(self					),A_					)







       def 		snake_case_	(			self: List[Any],A_: Any					):






            '''simple docstring'''




            self.insert_nth(0,A_					)







       def 		snake_case_	(			self: Optional[Any],A_: int,A_: Any					):






            '''simple docstring'''




            if not 0 <= index <= len(self					):
                 raise IndexError('list index out of range'					)
            __UpperCamelCase =			Node(A_					)
            if self.head is None:
                 __UpperCamelCase =			new_node
            elif index == 0:
                 __UpperCamelCase =			self.head  # link new_node to head
                 __UpperCamelCase =			new_node
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			new_node







       def 		snake_case_	(			self: str					):  # print every node data






            '''simple docstring'''




            print(self					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self.delete_nth(0					)







       def 		snake_case_	(			self: str					):  # delete from tail






            '''simple docstring'''




            return self.delete_nth(len(self					) - 1					)







       def 		snake_case_	(			self: Any,A_: int = 0					):






            '''simple docstring'''




            if not 0 <= index <= len(self					) - 1:  # test if index is valid
                 raise IndexError('List index out of range.'					)
            __UpperCamelCase =			self.head  # default first node
            if index == 0:
                 __UpperCamelCase =			self.head.next
            else:
                 __UpperCamelCase =			self.head
                 for _ in range(index - 1					):
                      __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next
                 __UpperCamelCase =			temp.next.next
            return delete_node.data







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            return self.head is None







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			None
            __UpperCamelCase =			self.head

            while current:
                 # Store the current node's next node.
                 __UpperCamelCase =			current.next
                 # Make the current node's next point backwards
                 __UpperCamelCase =			prev
                 # Make the previous node be the current node
                 __UpperCamelCase =			current
                 # Make the current node the next node (to progress iteration)
                 __UpperCamelCase =			next_node
            # Return prev in order to put the head at the end
            __UpperCamelCase =			prev






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			LinkedList()
     assert linked_list.is_empty() is True
     assert str(_lowercase ) == ""

     try:
          linked_list.delete_head()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     try:
          linked_list.delete_tail()
          raise AssertionError  # This should not happen.
     except IndexError:
          assert True  # This should happen.

     for i in range(10 ):
          assert len(_lowercase ) == i
          linked_list.insert_nth(_lowercase					,  i + 1 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  11 ) )

     linked_list.insert_head(0 )
     linked_list.insert_tail(11 )
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(0					,  12 ) )

     assert linked_list.delete_head() == 0
     assert linked_list.delete_nth(9 ) == 10
     assert linked_list.delete_tail() == 11
     assert len(_lowercase ) == 9
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(1					,  10 ) )

     assert all(linked_list[i] == i + 1 for i in range(0					,  9 ) ) is True

     for i in range(0					,  9 ):
          __UpperCamelCase =			-i
     assert all(linked_list[i] == -i for i in range(0					,  9 ) ) is True

     linked_list.reverse()
     assert str(_lowercase ) == "->".join(str(_lowercase ) for i in range(-8					,  1 ) )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			[
         -9,
         1_00,
         Node(77_34_51_12 ),
         'dlrow olleH',
         7,
         55_55,
         0,
         -1_92.5_55_55,
         'Hello, world!',
         77.9,
         Node(10 ),
         None,
         None,
         12.20,
     ]
     __UpperCamelCase =			LinkedList()

     for i in test_input:
          linked_list.insert_tail(_lowercase )

     # Check if it's empty or not
     assert linked_list.is_empty() is False
     assert (
         str(_lowercase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
         "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the head
     __UpperCamelCase =			linked_list.delete_head()
     assert result == -9
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None->12.2"
     )

     # Delete the tail
     __UpperCamelCase =			linked_list.delete_tail()
     assert result == 12.2
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None->None"
     )

     # Delete a node in specific location in linked list
     __UpperCamelCase =			linked_list.delete_nth(10 )
     assert result is None
     assert (
         str(_lowercase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
         "Hello, world!->77.9->Node(10)->None"
     )

     # Add a Node instance to its head
     linked_list.insert_head(Node('Hello again, world!' ) )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
     )

     # Add None to its tail
     linked_list.insert_tail(_lowercase )
     assert (
         str(_lowercase )
         == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
         "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
     )

     # Reverse the linked list
     linked_list.reverse()
     assert (
         str(_lowercase )
         == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
         "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
     )






def 		_A		(		)					-> List[str]:


     """simple docstring"""


     from doctest import testmod

     testmod()

     __UpperCamelCase =			LinkedList()
     linked_list.insert_head(input('Inserting 1st at head ' ).strip() )
     linked_list.insert_head(input('Inserting 2nd at head ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     linked_list.insert_tail(input('\nInserting 1st at tail ' ).strip() )
     linked_list.insert_tail(input('Inserting 2nd at tail ' ).strip() )
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nDelete head' )
     linked_list.delete_head()
     print('Delete tail' )
     linked_list.delete_tail()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nReverse linked list' )
     linked_list.reverse()
     print('\nPrint list:' )
     linked_list.print_list()
     print('\nString representation of linked list:' )
     print(_lowercase )
     print('\nReading/changing Node data using indexing:' )
     print(f'''Element at Position 1: {linked_list[1]}''' )
     __UpperCamelCase =			input('Enter New Value: ' ).strip()
     print('New list:' )
     print(_lowercase )
     print(f'''length of linked_list is : {len(_lowercase )}''' )


if __name__ == "__main__":
    main()


