




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''image_processor''', '''tokenizer''']
			snake_case_   		=       '''AutoImageProcessor'''
			snake_case_   		=       '''AutoTokenizer'''


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'			,						lowerCamelCase__			,						)
											__lowerCamelCase		       =    kwargs.pop('feature_extractor'							)

							__lowerCamelCase		       =    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__(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    self.image_processor
							__lowerCamelCase		       =    False


			def __call__(      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      str:



							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
											return self.current_processor(*lowerCamelCase__			,						**lowerCamelCase__							)

							__lowerCamelCase		       =    kwargs.pop('images'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('text'			,						lowerCamelCase__							)
							if len(lowerCamelCase__							) > 0:
											__lowerCamelCase		       =    args[0]
											__lowerCamelCase		       =    args[1:]

							if images is None and text is None:
											raise ValueError('You need to specify either an `images` or `text` input to process.'							)

							if images is not None:
											__lowerCamelCase		       =    self.image_processor(lowerCamelCase__			,						*lowerCamelCase__			,						**lowerCamelCase__							)
							if text is not None:
											__lowerCamelCase		       =    self.tokenizer(lowerCamelCase__			,						**lowerCamelCase__							)

							if text is None:
											return inputs
							elif images is None:
											return encodings
							else:
											__lowerCamelCase		       =    encodings['input_ids']
											return inputs


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[str]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			@contextmanager
			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							warnings.warn(
							    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
							    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
							    'your images inputs, or in a separate call.'							)
							__lowerCamelCase		       =    True
							__lowerCamelCase		       =    self.tokenizer
							yield
							__lowerCamelCase		       =    self.image_processor
							__lowerCamelCase		       =    False


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=False			,						lowerCamelCase__=None							)    ->      Dict:



							'''simple docstring'''
							if added_vocab is None:
											__lowerCamelCase		       =    self.tokenizer.get_added_vocab()

							__lowerCamelCase		       =    {}

							while tokens:
											__lowerCamelCase		       =    re.search(R'<s_(.*?)>'			,						lowerCamelCase__			,						re.IGNORECASE							)
											if start_token is None:
															break
											__lowerCamelCase		       =    start_token.group(1							)
											__lowerCamelCase		       =    re.search(Rf"""</s_{key}>"""			,						lowerCamelCase__			,						re.IGNORECASE							)
											__lowerCamelCase		       =    start_token.group()
											if end_token is None:
															__lowerCamelCase		       =    tokens.replace(lowerCamelCase__			,						''							)
											else:
															__lowerCamelCase		       =    end_token.group()
															__lowerCamelCase		       =    re.escape(lowerCamelCase__							)
															__lowerCamelCase		       =    re.escape(lowerCamelCase__							)
															__lowerCamelCase		       =    re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""			,						lowerCamelCase__			,						re.IGNORECASE							)
															if content is not None:
																			__lowerCamelCase		       =    content.group(1							).strip()
																			if r"<s_" in content and r"</s_" in content:  # non-leaf node
																							__lowerCamelCase		       =    self.tokenajson(lowerCamelCase__			,						is_inner_value=lowerCamelCase__			,						added_vocab=lowerCamelCase__							)
																							if value:
																											if len(lowerCamelCase__							) == 1:
																															__lowerCamelCase		       =    value[0]
																											__lowerCamelCase		       =    value
																			else:  # leaf nodes
																							__lowerCamelCase		       =    []
																							for leaf in content.split(R'<sep/>'							):
																											__lowerCamelCase		       =    leaf.strip()
																											if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																															__lowerCamelCase		       =    leaf[1:-2]  # for categorical special tokens
																											output[key].append(lowerCamelCase__							)
																							if len(output[key]							) == 1:
																											__lowerCamelCase		       =    output[key][0]

															__lowerCamelCase		       =    tokens[tokens.find(lowerCamelCase__							) + len(lowerCamelCase__							) :].strip()
															if tokens[:6] == r"<sep/>":  # non-leaf nodes
																			return [output] + self.tokenajson(tokens[6:]			,						is_inner_value=lowerCamelCase__			,						added_vocab=lowerCamelCase__							)

							if len(lowerCamelCase__							):
											return [output] if is_inner_value else output
							else:
											return [] if is_inner_value else {"text_sequence": tokens}


			@property
			def       lowercase_    (      self							)    ->      Dict:



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






			@property
			def       lowercase_    (      self							)    ->      Union[str, Any]:



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




from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

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

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

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

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

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

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

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

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
