


'''simple docstring'''



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	UpperCamelCase__(       UpperCamelCase__	):
				__magic_name__    :				List[str]  		=	['''image_processor''', '''tokenizer''']
				__magic_name__    :				List[str]  		=	'''BlipImageProcessor'''
				__magic_name__    :				Optional[Any]  		=	('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Optional[Any]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     False
										super().__init__(UpperCamelCase_				,   UpperCamelCase_				)
										UpperCAmelCase					    =     self.image_processor




				def __call__(		self      :			List[Any]				,   lowerCAmelCase      :			ImageInput = None				,   lowerCAmelCase      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Union[bool, str, PaddingStrategy] = False				,   lowerCAmelCase      :			Union[bool, str, TruncationStrategy] = None				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			int = 0				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			Optional[bool] = None				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Optional[Union[str, TensorType]] = None				,   **lowerCAmelCase      :			Tuple				,   )->					BatchEncoding:

										"""simple docstring"""




										if images is None and text is None:
																raise ValueError('''You have to specify either images or text.'''				)

										# Get only text
										if images is None:
																UpperCAmelCase					    =     self.tokenizer
																UpperCAmelCase					    =     self.tokenizer(
																    text=UpperCamelCase_				,   add_special_tokens=UpperCamelCase_				,   padding=UpperCamelCase_				,   truncation=UpperCamelCase_				,   max_length=UpperCamelCase_				,   stride=UpperCamelCase_				,   pad_to_multiple_of=UpperCamelCase_				,   return_attention_mask=UpperCamelCase_				,   return_overflowing_tokens=UpperCamelCase_				,   return_special_tokens_mask=UpperCamelCase_				,   return_offsets_mapping=UpperCamelCase_				,   return_token_type_ids=UpperCamelCase_				,   return_length=UpperCamelCase_				,   verbose=UpperCamelCase_				,   return_tensors=UpperCamelCase_				,   **UpperCamelCase_				,   )
																return text_encoding

										# add pixel_values
										UpperCAmelCase					    =     self.image_processor(UpperCamelCase_				,   return_tensors=UpperCamelCase_				)

										if text is not None:
																UpperCAmelCase					    =     self.tokenizer(
																    text=UpperCamelCase_				,   add_special_tokens=UpperCamelCase_				,   padding=UpperCamelCase_				,   truncation=UpperCamelCase_				,   max_length=UpperCamelCase_				,   stride=UpperCamelCase_				,   pad_to_multiple_of=UpperCamelCase_				,   return_attention_mask=UpperCamelCase_				,   return_overflowing_tokens=UpperCamelCase_				,   return_special_tokens_mask=UpperCamelCase_				,   return_offsets_mapping=UpperCamelCase_				,   return_token_type_ids=UpperCamelCase_				,   return_length=UpperCamelCase_				,   verbose=UpperCamelCase_				,   return_tensors=UpperCamelCase_				,   **UpperCamelCase_				,   )
										else:
																UpperCAmelCase					    =     None

										if text_encoding is not None:
																encoding_image_processor.update(UpperCamelCase_				)

										return encoding_image_processor




				def 					a__(		self      :			Dict				,   *lowerCAmelCase      :			Dict				,   **lowerCAmelCase      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




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




				def 					a__(		self      :			int				,   *lowerCAmelCase      :			Tuple				,   **lowerCAmelCase      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




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





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.tokenizer.model_input_names
										UpperCAmelCase					    =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)









'''simple docstring'''



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				int
				__magic_name__    :				TreeNode | None  		=	None
				__magic_name__    :				TreeNode | None  		=	None







_lowercase      :      Tuple            =					namedtuple("""CoinsDistribResult""", """moves excess""")


def 				lowerCamelCase__				(	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					)

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

												UpperCAmelCase					    =     1 - left_distrib_excess
												UpperCAmelCase					    =     1 - right_distrib_excess

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

												return CoinsDistribResult(A				,       A					)

						return get_distrib(A					)[0]


if __name__ == "__main__":
	import doctest

	doctest.testmod()





