


'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Tuple       =				["""image_processor""", """tokenizer"""]
      lowerCAmelCase__   :	List[Any]       =				"""LayoutLMv2ImageProcessor"""
      lowerCAmelCase__   :	str       =				("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(self       :       Any  ,		UpperCamelCase       :       int=None  ,		UpperCamelCase       :       Optional[int]=None  ,		**UpperCamelCase       :       List[Any]					):

             '''simple docstring'''







             if "feature_extractor" in kwargs:
                    warnings.warn(
                        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                        ''' instead.'''  ,		UpperCamelCase  ,		)
                    lowercase__      =					kwargs.pop('''feature_extractor'''					)

             lowercase__      =					image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                    raise ValueError('''You need to specify an `image_processor`.'''					)
             if tokenizer is None:
                    raise ValueError('''You need to specify a `tokenizer`.'''					)

             super().__init__(UpperCamelCase  ,		UpperCamelCase					)



      def __call__(self       :       int  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,		UpperCamelCase       :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,		UpperCamelCase       :       Union[List[List[int]], List[List[List[int]]]] = None  ,		UpperCamelCase       :       Optional[Union[List[int], List[List[int]]]] = None  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[bool, str, PaddingStrategy] = False  ,		UpperCamelCase       :       Union[bool, str, TruncationStrategy] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       int = 0  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       bool = False  ,		UpperCamelCase       :       bool = False  ,		UpperCamelCase       :       bool = False  ,		UpperCamelCase       :       bool = False  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		**UpperCamelCase       :       Any  ,		):

             '''simple docstring'''







             if self.image_processor.apply_ocr and (boxes is not None):
                    raise ValueError(
                        '''You cannot provide bounding boxes '''
                        '''if you initialized the image processor with apply_ocr set to True.'''					)

             if self.image_processor.apply_ocr and (word_labels is not None):
                    raise ValueError(
                        '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''					)

             if return_overflowing_tokens is True and return_offsets_mapping is False:
                    raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''					)

             # first, apply the image processor
             lowercase__      =					self.image_processor(images=UpperCamelCase  ,		return_tensors=UpperCamelCase					)

             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                    if isinstance(UpperCamelCase  ,		UpperCamelCase					):
                           lowercase__      =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
                    lowercase__      =					features['''words''']

             lowercase__      =					self.tokenizer(
                 text=text if text is not None else features['''words''']  ,		text_pair=text_pair if text_pair is not None else None  ,		boxes=boxes if boxes is not None else features['''boxes''']  ,		word_labels=UpperCamelCase  ,		add_special_tokens=UpperCamelCase  ,		padding=UpperCamelCase  ,		truncation=UpperCamelCase  ,		max_length=UpperCamelCase  ,		stride=UpperCamelCase  ,		pad_to_multiple_of=UpperCamelCase  ,		return_token_type_ids=UpperCamelCase  ,		return_attention_mask=UpperCamelCase  ,		return_overflowing_tokens=UpperCamelCase  ,		return_special_tokens_mask=UpperCamelCase  ,		return_offsets_mapping=UpperCamelCase  ,		return_length=UpperCamelCase  ,		verbose=UpperCamelCase  ,		return_tensors=UpperCamelCase  ,		**UpperCamelCase  ,		)

             # add pixel values
             lowercase__      =					features.pop('''pixel_values'''					)
             if return_overflowing_tokens is True:
                    lowercase__      =					self.get_overflowing_images(UpperCamelCase  ,		encoded_inputs['''overflow_to_sample_mapping''']					)
             lowercase__      =					images

             return encoded_inputs



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					[]
             for sample_idx in overflow_to_sample_mapping:
                    images_with_overflow.append(images[sample_idx]					)

             if len(UpperCamelCase					) != len(UpperCamelCase					):
                    raise ValueError(
                        '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                        f" {len(UpperCamelCase					)} and {len(UpperCamelCase					)}"					)

             return images_with_overflow



      def  UpperCamelCase__						(self       :       str  ,		*UpperCamelCase       :       Optional[Any]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.tokenizer.batch_decode(*UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		*UpperCamelCase       :       Optional[int]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer.decode(*UpperCamelCase  ,		**UpperCamelCase					)



      @property
      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,		UpperCamelCase  ,		)
             return self.image_processor_class






      @property
      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,		UpperCamelCase  ,		)
             return self.image_processor








'''simple docstring'''

from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class     __lowerCAmelCase		:





      '''simple docstring'''






      lowerCAmelCase__   :	int
      lowerCAmelCase__   :	TreeNode | None       =				None
      lowerCAmelCase__   :	TreeNode | None       =				None






lowerCamelCase				:  Optional[Any]    		=      namedtuple('CoinsDistribResult', 'moves excess')

def _SCREAMING_SNAKE_CASE (A			)			->			int:






       """simple docstring"""







       if root is None:
              return 0

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

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

       def count_coins(A			) -> 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			) -> CoinsDistribResult:

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

              lowercase__     ,lowercase__      =					get_distrib(node.left			)
              lowercase__     ,lowercase__      =					get_distrib(node.right			)

              lowercase__      =					1 - left_distrib_excess
              lowercase__      =					1 - right_distrib_excess

              lowercase__      =					(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A			)
                  + abs(A			)
              )
              lowercase__      =					node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A    ,					A			)

       return get_distrib(A			)[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




