





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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["image_processor", "tokenizer"]
							lowerCamelCase_							     =       "LayoutLMv3ImageProcessor"
							lowerCamelCase_							     =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")


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



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								=   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__(__A				,					__A		)


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



									"""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."""		)

									# first, apply the image processor
									SCREAMING_SNAKE_CASE__								=   self.image_processor(images=__A				,					return_tensors=__A		)

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

									SCREAMING_SNAKE_CASE__								=   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=__A				,					add_special_tokens=__A				,					padding=__A				,					truncation=__A				,					max_length=__A				,					stride=__A				,					pad_to_multiple_of=__A				,					return_token_type_ids=__A				,					return_attention_mask=__A				,					return_overflowing_tokens=__A				,					return_special_tokens_mask=__A				,					return_offsets_mapping=__A				,					return_length=__A				,					verbose=__A				,					return_tensors=__A				,					**__A				,					)

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

									return encoded_inputs


							def   _snake_case	(				self     :str				,					__A     :Optional[int]				,					__A     :int		) -> Any:



									"""simple docstring"""



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

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

									return images_with_overflow


							def   _snake_case	(				self     :List[Any]				,					*__A     :int				,					**__A     :Optional[int]		) -> Any:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Tuple				,					*__A     :int				,					**__A     :str		) -> Optional[Any]:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[Any]:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :Dict		) -> Any:



									"""simple docstring"""



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


from __future__ import annotations

from dataclasses import dataclass

@dataclass
class 							UpperCAmelCase_						:
							"""simple docstring"""







							UpperCAmelCase__			:		float
							UpperCAmelCase__			:		TreeNode | None             =       None
							UpperCAmelCase__			:		TreeNode | None             =       None



def   __UpperCAmelCase					(      __a :     TreeNode | None      )     ->	bool:

					"""simple docstring"""

					def is_valid_tree(__a :     TreeNode | None      ) -> bool:
										if node is None:
															return True

										if not isinstance(__a						,__a      ):
															return False

										try:
															float(node.data      )
										except (TypeError, ValueError):
															return False

										return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

					if not is_valid_tree(__a      ):
										raise ValueError(
										    '''Each node should be type of TreeNode and data should be float.'''      )

					def is_binary_search_tree_recursive_check(
					    __a :     TreeNode | None						,__a :     float						,__a :     float      ) -> bool:

										if node is None:
															return True

										return (
										    left_bound < node.data < right_bound
										    and is_binary_search_tree_recursive_check(node.left						,__a						,node.data      )
										    and is_binary_search_tree_recursive_check(
										        node.right						,node.data						,__a      )
										)

					return is_binary_search_tree_recursive_check(__a						,-float('''inf'''      )						,float('''inf'''      )      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



