




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def __init__(			self      :	str					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	Optional[int]=3					, UpperCAmelCase__      :	Union[str, Any]=18					, UpperCAmelCase__      :	Optional[Any]=30					, UpperCAmelCase__      :	str=400					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	Tuple=None					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	List[Any]=None					, UpperCAmelCase__      :	str=True					, UpperCAmelCase__      :	List[str]=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Dict=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Optional[int]=False					, )						->List[str]:





           '''simple docstring'''

           A__       =    size if size is not None else {"""height""": 20, """width""": 20}
           A__       =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           A__       =    parent
           A__       =    batch_size
           A__       =    num_channels
           A__       =    image_size
           A__       =    min_resolution
           A__       =    max_resolution
           A__       =    do_resize
           A__       =    size
           A__       =    do_center_crop
           A__       =    crop_size
           A__       =    do_normalize
           A__       =    image_mean
           A__       =    image_std
           A__       =    do_reduce_labels





    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->int:





           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }







def SCREAMING_SNAKE_CASE				(       )   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset('''hf-internal-testing/fixtures_ade20k''' ,				split='''test'''						)

       A__       =    Image.open(dataset[0]['''file''']						)
       A__       =    Image.open(dataset[1]['''file''']						)

       return image, map







def SCREAMING_SNAKE_CASE				(       )   ->       str:



       """simple docstring"""

       A__       =    load_dataset('''hf-internal-testing/fixtures_ade20k''' ,				split='''test'''						)

       A__       =    Image.open(ds[0]['''file''']						)
       A__       =    Image.open(ds[1]['''file''']						)
       A__       =    Image.open(ds[2]['''file''']						)
       A__       =    Image.open(ds[3]['''file''']						)

       return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      BeitImageProcessor if is_vision_available() else None


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->str:





           '''simple docstring'''

           A__       =    BeitImageProcessingTester(self)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[str]:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Tuple:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''do_resize'''))
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''size'''))
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''do_center_crop'''))
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''center_crop'''))
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''do_normalize'''))
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''image_mean'''))
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					, '''image_std'''))


    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           A__       =    self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size					, {'''height''': 20, '''width''': 20})
           self.assertEqual(image_processor.crop_size					, {'''height''': 18, '''width''': 18})
           self.assertEqual(image_processor.do_reduce_labels					, _SCREAMING_SNAKE_CASE)

           A__       =    self.image_processing_class.from_dict(
               self.image_processor_dict					, size=42					, crop_size=84					, reduce_labels=_SCREAMING_SNAKE_CASE)
           self.assertEqual(image_processor.size					, {'''height''': 42, '''width''': 42})
           self.assertEqual(image_processor.crop_size					, {'''height''': 84, '''width''': 84})
           self.assertEqual(image_processor.do_reduce_labels					, _SCREAMING_SNAKE_CASE)


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=_SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(_SCREAMING_SNAKE_CASE					, Image.Image)

           # Test not batched input
           A__       =    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
           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, 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        SCREAMING_SNAKE_CASE							(			self      :	str)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=_SCREAMING_SNAKE_CASE					, numpify=_SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(_SCREAMING_SNAKE_CASE					, np.ndarray)

           # Test not batched input
           A__       =    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
           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, 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        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[Any]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=_SCREAMING_SNAKE_CASE					, torchify=_SCREAMING_SNAKE_CASE)
           for image in image_inputs:
                  self.assertIsInstance(_SCREAMING_SNAKE_CASE					, torch.Tensor)

           # Test not batched input
           A__       =    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
           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, 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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[int]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__       =    prepare_image_inputs(self.image_processor_tester					, equal_resolution=_SCREAMING_SNAKE_CASE					, torchify=_SCREAMING_SNAKE_CASE)
           A__       =    []
           for image in image_inputs:
                  self.assertIsInstance(_SCREAMING_SNAKE_CASE					, torch.Tensor)
                  maps.append(torch.zeros(image.shape[-2:]).long())

           # Test not batched input
           A__       =    image_processing(image_inputs[0]					, maps[0]					, return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape					, (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(
               encoding['''labels'''].shape					, (
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(encoding['''labels'''].dtype					, torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test batched
           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].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'''],
               )					, )
           self.assertEqual(
               encoding['''labels'''].shape					, (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(encoding['''labels'''].dtype					, torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test not batched input (PIL images)
           A__       =    prepare_semantic_single_inputs()

           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape					, (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(
               encoding['''labels'''].shape					, (
                   1,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(encoding['''labels'''].dtype					, torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)

           # Test batched input (PIL images)
           A__       =    prepare_semantic_batch_inputs()

           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, return_tensors='''pt''')
           self.assertEqual(
               encoding['''pixel_values'''].shape					, (
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(
               encoding['''labels'''].shape					, (
                   2,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )					, )
           self.assertEqual(encoding['''labels'''].dtype					, torch.long)
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[int]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)

           # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
           A__       =    prepare_semantic_single_inputs()
           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 150)

           A__       =    True
           A__       =    image_processing(_SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 255)







"""simple docstring"""
from __future__ import annotations



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	data
    UpperCAmelCase	:					Node | None	=	None
    UpperCAmelCase	:					Node | None	=	None

def       _snake_case						(						UpperCamelCase						:		Node | None						):  # In Order traversal of the tree
 if tree:
  display(tree.left						)
  print(tree.data						)
  display(tree.right						)

def       _snake_case						(						UpperCamelCase						:		Node | None						):
 return 1 + max(depth_of_tree(tree.left						)			,		depth_of_tree(tree.right						)						) if tree else 0

def       _snake_case						(						UpperCamelCase						:		Node						):
 if not tree:
  return True
 if tree.left and tree.right:
  return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
 else:
  return not tree.left and not tree.right

def       _snake_case						(						):  # Main function for testing.
 UpperCAmelCase	:					int	=	Node(1						)
 UpperCAmelCase	:					Tuple	=	Node(2						)
 UpperCAmelCase	:					Any	=	Node(3						)
 UpperCAmelCase	:					Optional[int]	=	Node(4						)
 UpperCAmelCase	:					Any	=	Node(5						)
 UpperCAmelCase	:					Optional[int]	=	Node(6						)
 UpperCAmelCase	:					int	=	Node(7						)
 UpperCAmelCase	:					str	=	Node(8						)
 UpperCAmelCase	:					str	=	Node(9						)

 print(is_full_binary_tree(UpperCamelCase						)						)
 print(depth_of_tree(UpperCamelCase						)						)
 print("""Tree is: """						)
 display(UpperCamelCase						)


if __name__ == "__main__":
      main()
