"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor
class 		__A							(				unittest.TestCase	):






     def __init__(	self       ,  a__       ,  a__=7       ,  a__=3       ,  a__=18       ,  a__=30       ,  a__=400       ,  a__=True       ,  a__=None       ,  a__=True       ,  a__=None       ,  a__=True       ,  a__=[0.5, 0.5, 0.5]       ,  a__=[0.5, 0.5, 0.5]       ,  ):
       _lowerCAmelCase		: List[Any]						    =  size if size is not None else {"""shortest_edge""": 18}
       _lowerCAmelCase		: Optional[Any]						    =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
       _lowerCAmelCase		: Optional[Any]						    =  parent
       _lowerCAmelCase		: Union[str, Any]						    =  batch_size
       _lowerCAmelCase		: Union[str, Any]						    =  num_channels
       _lowerCAmelCase		: Tuple						    =  image_size
       _lowerCAmelCase		: Any						    =  min_resolution
       _lowerCAmelCase		: List[str]						    =  max_resolution
       _lowerCAmelCase		: List[Any]						    =  do_resize
       _lowerCAmelCase		: Tuple						    =  size
       _lowerCAmelCase		: Optional[Any]						    =  do_center_crop
       _lowerCAmelCase		: Dict						    =  crop_size
       _lowerCAmelCase		: Any						    =  do_normalize
       _lowerCAmelCase		: List[Any]						    =  image_mean
       _lowerCAmelCase		: Optional[int]						    =  image_std






     def 					__A						(	self						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }







@require_torch
@require_vision
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Tuple	      =	LevitImageProcessor if is_vision_available() else None






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  LevitImageProcessingTester(self						)






     @property
     def 					__A						(	self						):
       return self.image_processor_tester.prepare_image_processor_dict()






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(_lowerCAmelCase       ,  """image_mean"""						)						)
       self.assertTrue(hasattr(_lowerCAmelCase       ,  """image_std"""						)						)
       self.assertTrue(hasattr(_lowerCAmelCase       ,  """do_normalize"""						)						)
       self.assertTrue(hasattr(_lowerCAmelCase       ,  """do_resize"""						)						)
       self.assertTrue(hasattr(_lowerCAmelCase       ,  """do_center_crop"""						)						)
       self.assertTrue(hasattr(_lowerCAmelCase       ,  """size"""						)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size       ,  {"""shortest_edge""": 18}						)
       self.assertEqual(image_processor.crop_size       ,  {"""height""": 18, """width""": 18}						)

       _lowerCAmelCase		: int						    =  self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42       ,  crop_size=84						)
       self.assertEqual(image_processor.size       ,  {"""shortest_edge""": 42}						)
       self.assertEqual(image_processor.crop_size       ,  {"""height""": 84, """width""": 84}						)






     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       # Initialize image_processing
       _lowerCAmelCase		: str						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       _lowerCAmelCase		: List[Any]						    =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
         self.assertIsInstance(_lowerCAmelCase       ,  Image.Image						)

       # Test not batched input
       _lowerCAmelCase		: Any						    =  image_processing(image_inputs[0]       ,  return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )

       # Test batched
       _lowerCAmelCase		: Optional[Any]						    =  image_processing(_lowerCAmelCase       ,  return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )






     def 					__A						(	self						):
       # Initialize image_processing
       _lowerCAmelCase		: str						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       _lowerCAmelCase		: Dict						    =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowerCAmelCase       ,  numpify=_lowerCAmelCase						)
       for image in image_inputs:
         self.assertIsInstance(_lowerCAmelCase       ,  np.ndarray						)

       # Test not batched input
       _lowerCAmelCase		: List[Any]						    =  image_processing(image_inputs[0]       ,  return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )

       # Test batched
       _lowerCAmelCase		: str						    =  image_processing(_lowerCAmelCase       ,  return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )






     def 					__A						(	self						):
       # Initialize image_processing
       _lowerCAmelCase		: Optional[Any]						    =  self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       _lowerCAmelCase		: Union[str, Any]						    =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowerCAmelCase       ,  torchify=_lowerCAmelCase						)
       for image in image_inputs:
         self.assertIsInstance(_lowerCAmelCase       ,  torch.Tensor						)

       # Test not batched input
       _lowerCAmelCase		: Any						    =  image_processing(image_inputs[0]       ,  return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,  (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )

       # Test batched
       _lowerCAmelCase		: Tuple						    =  image_processing(_lowerCAmelCase       ,  return_tensors="""pt"""						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,  (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )       ,  )





"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    Node | None	      =	None
     _UpperCamelCase							:    Node | None	      =	None







def 							SCREAMING_SNAKE_CASE  (    ) ->   Node | None:
  _lowerCAmelCase		: Tuple						    =  Node(1			)
  _lowerCAmelCase		: int						    =  Node(2			)
  _lowerCAmelCase		: int						    =  Node(3			)
  _lowerCAmelCase		: Any						    =  Node(4			)
  _lowerCAmelCase		: Dict						    =  Node(5			)
  return tree





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   int:
  return (max(height(root.left			)    ,height(root.right			)			) + 1) if root else 0





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  if root is None:
    return output

  _lowerCAmelCase		: Union[str, Any]						    =  deque([root]			)

  while process_queue:
    _lowerCAmelCase		: Optional[Any]						    =  process_queue.popleft()
    output.append(node.data			)

    if node.left:
      process_queue.append(node.left			)
    if node.right:
      process_queue.append(node.right			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.left    ,level - 1			)
      populate_output(root.right    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.right    ,level - 1			)
      populate_output(root.left    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  _lowerCAmelCase		: list[Sequence[Node | None]]						    =  []

  _lowerCAmelCase		: Tuple						    =  0
  _lowerCAmelCase		: Dict						    =  height(_lowerCamelCase			)

  for h in range(1    ,height_tree + 1			):
    if not flag:
      output.append(get_nodes_from_left_to_right(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Any						    =  1
    else:
      output.append(get_nodes_from_right_to_left(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Optional[int]						    =  0

  return output





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:  # Main function for testing.
  _lowerCAmelCase		: int						    =  make_tree()
  print(f"In-order Traversal: {inorder(_lowerCamelCase			)}"			)
  print(f"Pre-order Traversal: {preorder(_lowerCamelCase			)}"			)
  print(f"Post-order Traversal: {postorder(_lowerCamelCase			)}"    ,"""\n"""			)

  print(f"Height of Tree: {height(_lowerCamelCase			)}"    ,"""\n"""			)

  print("""Complete Level Order Traversal: """			)
  print(level_order(_lowerCamelCase			)    ,"""\n"""			)

  print("""Level-wise order Traversal: """			)

  for level in range(1    ,height(_lowerCamelCase			) + 1			):
    print(f"Level {level}:"    ,get_nodes_from_left_to_right(_lowerCamelCase    ,level=_lowerCamelCase			)			)

  print("""\nZigZag order Traversal: """			)
  print(zigzag(_lowerCamelCase			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




