
'''simple docstring'''

from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class  _UpperCAmelCase :

     """simple docstring"""

     a_    	=					42
     a_    	=					None
     a_    	=					None







__snake_case:  int								=			namedtuple("CoinsDistribResult", "moves excess")






def 	_snake_case						(      A_       :    TreeNode | None   ):
  """simple docstring"""







  if root is None:
    return 0

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

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

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

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

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

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

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

    a_      , a_      :	Union[str, Any]      =							get_distrib(node.left   )
    a_      , a_      :	Dict      =							get_distrib(node.right   )

    a_      :	List[Any]      =							1 - left_distrib_excess
    a_      :	Union[str, Any]      =							1 - right_distrib_excess

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

    return CoinsDistribResult(A_     ,		A_   )

  return get_distrib(A_   )[0]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=7							,		lowerCAmelCase_=3							,		lowerCAmelCase_=18							,		lowerCAmelCase_=30							,		lowerCAmelCase_=4_00							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							parent
       a_      :	Union[str, Any]      =							batch_size
       a_      :	Any      =							num_channels
       a_      :	Tuple      =							image_size
       a_      :	Any      =							min_resolution
       a_      :	Dict      =							max_resolution
       a_      :	Optional[Any]      =							do_resize
       a_      :	Tuple      =							size if size is not None else {"""height""": 18, """width""": 20}
       a_      :	int      =							do_thumbnail
       a_      :	Optional[int]      =							do_align_axis
       a_      :	Tuple      =							do_pad
       a_      :	int      =							do_normalize
       a_      :	Tuple      =							image_mean
       a_      :	Optional[Any]      =							image_std






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







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

     """simple docstring"""

     a_    	=					DonutImageProcessor if is_vision_available() else None


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DonutImageProcessingTester(self )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_resize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""size""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_thumbnail""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_align_long_axis""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_pad""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_normalize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_mean""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_std""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size							,		{"""height""": 18, """width""": 20} )

       a_      :	str      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=42 )
       self.assertEqual(image_processor.size							,		{"""height""": 42, """width""": 42} )

       # Previous config had dimensions in (width, height) order
       a_      :	Any      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=(42, 84) )
       self.assertEqual(image_processor.size							,		{"""height""": 84, """width""": 42} )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       # Test not batched input
       a_      :	Dict      =							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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	int      =							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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a_      :	int      =							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
       a_      :	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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Dict      =							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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)






     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a_      :	Optional[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
       a_      :	Optional[int]      =							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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	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.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


