






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 ViltImageProcessor
class 							A_      (  unittest.TestCase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A=7							,					_A=3							,					_A=18							,					_A=30							,					_A=400							,					_A=True							,					_A=None							,					_A=2							,					_A=True							,					_A=[0.5, 0.5, 0.5]							,					_A=[0.5, 0.5, 0.5]							,					)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			size if size is not None else {'''shortest_edge''': 30}
     _UpperCAmelCase			: str     					=			parent
     _UpperCAmelCase			: Tuple     					=			batch_size
     _UpperCAmelCase			: Any     					=			num_channels
     _UpperCAmelCase			: Any     					=			image_size
     _UpperCAmelCase			: Dict     					=			min_resolution
     _UpperCAmelCase			: Union[str, Any]     					=			max_resolution
     _UpperCAmelCase			: List[Any]     					=			do_resize
     _UpperCAmelCase			: List[Any]     					=			size
     _UpperCAmelCase			: Optional[int]     					=			size_divisor
     _UpperCAmelCase			: Any     					=			do_normalize
     _UpperCAmelCase			: Optional[int]     					=			image_mean
     _UpperCAmelCase			: Union[str, Any]     					=			image_std


 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "size_divisor": self.size_divisor,
     }


 def 	snake_case__			(		self							,					_A							,					_A=False)	->						Any:

     """simple docstring"""



     if not batched:
         _UpperCAmelCase			: Dict     					=			self.size['''shortest_edge''']
         _UpperCAmelCase			: List[Any]     					=			image_inputs[0]
         if isinstance(_A							,					Image.Image):
             _UpperCAmelCase			: str     					=			image.size
         else:
             _UpperCAmelCase			: List[Any]     					=			image.shape[1], image.shape[2]
         _UpperCAmelCase			: List[str]     					=			size / min(_A							,					_A)
         if h < w:
             _UpperCAmelCase			: str     					=			size, scale * w
         else:
             _UpperCAmelCase			: List[str]     					=			scale * h, size

         _UpperCAmelCase			: str     					=			int((1333 / 800) * size)
         if max(_A							,					_A) > max_size:
             _UpperCAmelCase			: Dict     					=			max_size / max(_A							,					_A)
             _UpperCAmelCase			: List[Any]     					=			newh * scale
             _UpperCAmelCase			: Union[str, Any]     					=			neww * scale

         _UpperCAmelCase			: int     					=			int(newh + 0.5), int(neww + 0.5)
         _UpperCAmelCase			: Dict     					=			(
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

     else:
         _UpperCAmelCase			: List[str]     					=			[]
         for image in image_inputs:
             _UpperCAmelCase			: Union[str, Any]     					=			self.get_expected_values([image])
             expected_values.append((expected_height, expected_width))
         _UpperCAmelCase			: Dict     					=			max(_A							,					key=lambda _A: item[0])[0]
         _UpperCAmelCase			: Tuple     					=			max(_A							,					key=lambda _A: item[1])[1]

     return expected_height, expected_width



@require_torch
@require_vision
class 							A_      (  lowerCAmelCase__	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     str	=   ViltImageProcessor if is_vision_available() else None


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			ViltImageProcessingTester(self)


 @property
 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     return self.image_processor_tester.prepare_image_processor_dict()


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(_A							,					'''image_mean'''))
     self.assertTrue(hasattr(_A							,					'''image_std'''))
     self.assertTrue(hasattr(_A							,					'''do_normalize'''))
     self.assertTrue(hasattr(_A							,					'''do_resize'''))
     self.assertTrue(hasattr(_A							,					'''size'''))
     self.assertTrue(hasattr(_A							,					'''size_divisor'''))


 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size							,					{'''shortest_edge''': 30})

     _UpperCAmelCase			: List[Any]     					=			self.image_processing_class.from_dict(self.image_processor_dict							,					size=42)
     self.assertEqual(image_processor.size							,					{'''shortest_edge''': 42})


 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



     pass


 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     _UpperCAmelCase			: Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					Image.Image)

     # Test not batched input
     _UpperCAmelCase			: Optional[int]     					=			image_processing(image_inputs[0]							,					return_tensors='''pt''').pixel_values

     _UpperCAmelCase			: List[str]     					=			self.image_processor_tester.get_expected_values(_A)
     self.assertEqual(
         encoded_images.shape							,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,					)

     # Test batched
     _UpperCAmelCase			: Any     					=			image_processing(_A							,					return_tensors='''pt''').pixel_values

     _UpperCAmelCase			: Optional[Any]     					=			self.image_processor_tester.get_expected_values(_A							,					batched=_A)
     self.assertEqual(
         encoded_images.shape							,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,					)


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

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     _UpperCAmelCase			: Dict     					=			prepare_image_inputs(self.image_processor_tester							,					equal_resolution=_A							,					numpify=_A)
     for image in image_inputs:
         self.assertIsInstance(_A							,					np.ndarray)

     # Test not batched input
     _UpperCAmelCase			: List[Any]     					=			image_processing(image_inputs[0]							,					return_tensors='''pt''').pixel_values

     _UpperCAmelCase			: Optional[Any]     					=			self.image_processor_tester.get_expected_values(_A)
     self.assertEqual(
         encoded_images.shape							,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,					)

     # Test batched
     _UpperCAmelCase			: List[str]     					=			image_processing(_A							,					return_tensors='''pt''').pixel_values

     _UpperCAmelCase			: int     					=			self.image_processor_tester.get_expected_values(_A							,					batched=_A)
     self.assertEqual(
         encoded_images.shape							,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,					)


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

     """simple docstring"""



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

     # Test not batched input
     _UpperCAmelCase			: List[Any]     					=			image_processing(image_inputs[0]							,					return_tensors='''pt''').pixel_values

     _UpperCAmelCase			: Union[str, Any]     					=			self.image_processor_tester.get_expected_values(_A)
     self.assertEqual(
         encoded_images.shape							,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,					)

     # Test batched
     _UpperCAmelCase			: Optional[Any]     					=			image_processing(_A							,					return_tensors='''pt''').pixel_values

     _UpperCAmelCase			: Any     					=			self.image_processor_tester.get_expected_values(_A							,					batched=_A)
     self.assertEqual(
         encoded_images.shape							,					(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )							,					)












def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int	) ->   str:
    if not root or root not in binary_tree_mirror_dictionary:
        return
    _UpperCAmelCase					,	_UpperCAmelCase			: int     					=			binary_tree_mirror_dictionary[root][:2]
    _UpperCAmelCase			: int     					=			[right_child, left_child]
    binary_tree_mirror_dict(__A						,						__A	)
    binary_tree_mirror_dict(__A						,						__A	)

def        _lowerCamelCase		(  __A      :							dict						,						__A      :							int = 1	) ->   dict:
    if not binary_tree:
        raise ValueError('''binary tree cannot be empty'''	)
    if root not in binary_tree:
        _UpperCAmelCase			: Dict     					=			f'''root {root} is not present in the binary_tree'''
        raise ValueError(__A	)
    _UpperCAmelCase			: int     					=			dict(__A	)
    binary_tree_mirror_dict(__A						,						__A	)
    return binary_tree_mirror_dictionary


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




