






"""simple docstring"""



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class 					_UpperCAmelCase       :
					SCREAMING_SNAKE_CASE_		:				int
					SCREAMING_SNAKE_CASE_		:				TreeNode | None							    =   None
					SCREAMING_SNAKE_CASE_		:				TreeNode | None							    =   None
__A		:       List[str]  				=		namedtuple('''CoinsDistribResult''', '''moves excess''')



def 						lowercase    (				__snake_case				: TreeNode | None	):

		if root is None:
				return 0

		# Validation
		def count_nodes(__snake_case				: TreeNode | None	) -> int:
				if node is None:
						return 0

				return count_nodes(node.left	) + count_nodes(node.right	) + 1

		def count_coins(__snake_case				: TreeNode | None	) -> int:
				if node is None:
						return 0

				return count_coins(node.left	) + count_coins(node.right	) + node.data

		if count_nodes(__snake_case	) != count_coins(__snake_case	):
				raise ValueError('''The nodes number should be same as the number of coins'''	)

		# Main calculation
		def get_distrib(__snake_case				: TreeNode | None	) -> CoinsDistribResult:

				if node is None:
						return CoinsDistribResult(0		,					1	)

				lowercase_       ,					lowercase_ : List[Any]							       =   get_distrib(node.left	)
				lowercase_       ,					lowercase_ : Optional[Any]							       =   get_distrib(node.right	)

				lowercase_ : Any							       =   1 - left_distrib_excess
				lowercase_ : List[Any]							       =   1 - right_distrib_excess

				lowercase_ : Optional[int]							       =   (
				    left_distrib_moves
				    + right_distrib_moves
				    + abs(__snake_case	)
				    + abs(__snake_case	)
				)
				lowercase_ : Any							       =   node.data - coins_to_left - coins_to_right

				return CoinsDistribResult(__snake_case		,					__snake_case	)

		return get_distrib(__snake_case	)[0]


if __name__ == "__main__":
						import doctest

						doctest.testmod()







'''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 MobileNetVaImageProcessor





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=7    ,					__snake_case=3    ,					__snake_case=18    ,					__snake_case=30    ,					__snake_case=400    ,					__snake_case=True    ,					__snake_case=None    ,					__snake_case=True    ,					__snake_case=None    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      size if size is not None else {"""shortest_edge""": 20}
											_SCREAMING_SNAKE_CASE :       int     		=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											_SCREAMING_SNAKE_CASE :       Dict     		=      parent
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_channels
											_SCREAMING_SNAKE_CASE :       Dict     		=      image_size
											_SCREAMING_SNAKE_CASE :       Any     		=      min_resolution
											_SCREAMING_SNAKE_CASE :       Any     		=      max_resolution
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      do_resize
											_SCREAMING_SNAKE_CASE :       Tuple     		=      size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      do_center_crop
											_SCREAMING_SNAKE_CASE :       str     		=      crop_size






							def UpperCAmelCase_	(    self  ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Union[str, Any]      	=  MobileNetVaImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      MobileNetVaImageProcessingTester(self  )






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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_center_crop"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""crop_size"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 20}  )
											self.assertEqual(image_processor.crop_size    ,					{"""height""": 18, """width""": 18}  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      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 UpperCAmelCase_	(    self  ):
											pass






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

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

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       Optional[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
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image_processing(__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 UpperCAmelCase_	(    self  ):
											# Initialize image_processing
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_SCREAMING_SNAKE_CASE :       str     		=      prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=__snake_case    ,					torchify=__snake_case  )
											for image in image_inputs:
															self.assertIsInstance(__snake_case    ,					torch.Tensor  )

											# Test not batched input
											_SCREAMING_SNAKE_CASE :       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
											_SCREAMING_SNAKE_CASE :       str     		=      image_processing(__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"""],
											    )    ,					)




