

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 DeiTImageProcessor






class 		__magic_name__ (				unittest.TestCase							):






     def __init__(							self						,		_lowercase						,		_lowercase=7						,		_lowercase=3						,		_lowercase=18						,		_lowercase=30						,		_lowercase=400						,		_lowercase=True						,		_lowercase=None						,		_lowercase=True						,		_lowercase=None						,		_lowercase=True						,		_lowercase=[0.5, 0.5, 0.5]						,		_lowercase=[0.5, 0.5, 0.5]						,		)->			int:
         UpperCamelCase_          =		size if size is not None else {"height": 20, "width": 20}
         UpperCamelCase_          =		crop_size if crop_size is not None else {"height": 18, "width": 18}

         UpperCamelCase_          =		parent
         UpperCamelCase_          =		batch_size
         UpperCamelCase_          =		num_channels
         UpperCamelCase_          =		image_size
         UpperCamelCase_          =		min_resolution
         UpperCamelCase_          =		max_resolution
         UpperCamelCase_          =		do_resize
         UpperCamelCase_          =		size
         UpperCamelCase_          =		do_center_crop
         UpperCamelCase_          =		crop_size
         UpperCamelCase_          =		do_normalize
         UpperCamelCase_          =		image_mean
         UpperCamelCase_          =		image_std






     def      UpperCAmelCase_							(							self     )->			str:
         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,
         }






@require_torch
@require_vision
class 		__magic_name__ (				snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :str									=	DeiTImageProcessor if is_vision_available() else None
     UpperCamelCase_     :List[str]									=	True






     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		DeiTImageProcessingTester(self     )






     @property
     def      UpperCAmelCase_							(							self     )->			Any:
         return self.image_processor_tester.prepare_image_processor_dict()






     def      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_          =		self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_lowercase						,		"do_resize"     )     )
         self.assertTrue(hasattr(_lowercase						,		"size"     )     )
         self.assertTrue(hasattr(_lowercase						,		"do_center_crop"     )     )
         self.assertTrue(hasattr(_lowercase						,		"center_crop"     )     )
         self.assertTrue(hasattr(_lowercase						,		"do_normalize"     )     )
         self.assertTrue(hasattr(_lowercase						,		"image_mean"     )     )
         self.assertTrue(hasattr(_lowercase						,		"image_std"     )     )






     def      UpperCAmelCase_							(							self     )->			int:
         UpperCamelCase_          =		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}     )

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






     def      UpperCAmelCase_							(							self     )->			Tuple:
         pass






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         # Initialize image_processing
         UpperCamelCase_          =		self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         UpperCamelCase_          =		prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase						,		Image.Image     )

         # Test not batched input
         UpperCamelCase_          =		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
         UpperCamelCase_          =		image_processing(_lowercase						,		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      UpperCAmelCase_							(							self     )->			Optional[int]:
         # Initialize image_processing
         UpperCamelCase_          =		self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         UpperCamelCase_          =		prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_lowercase						,		numpify=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase						,		np.ndarray     )

         # Test not batched input
         UpperCamelCase_          =		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
         UpperCamelCase_          =		image_processing(_lowercase						,		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      UpperCAmelCase_							(							self     )->			List[str]:
         # Initialize image_processing
         UpperCamelCase_          =		self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         UpperCamelCase_          =		prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_lowercase						,		torchify=_lowercase     )
         for image in image_inputs:
             self.assertIsInstance(_lowercase						,		torch.Tensor     )

         # Test not batched input
         UpperCamelCase_          =		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
         UpperCamelCase_          =		image_processing(_lowercase						,		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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if not root or root not in binary_tree_mirror_dictionary:
        return
    UpperCamelCase_   ,    UpperCamelCase_          =		binary_tree_mirror_dictionary[root][:2]
    UpperCamelCase_          =		[right_child, left_child]
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		dict:



    """simple docstring"""






    if not binary_tree:
        raise ValueError("binary tree cannot be empty"       )
    if root not in binary_tree:
        UpperCamelCase_          =		f"root {root} is not present in the binary_tree"
        raise ValueError(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		dict(SCREAMING_SNAKE_CASE_       )
    binary_tree_mirror_dict(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:Dict      =			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
 print(F'''Binary tree: {binary_tree}''')
 SCREAMING_SNAKE_CASE						:str      =			binary_tree_mirror(binary_tree, 5)
 print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')
