






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      (UpperCAmelCase__			):







		'''simple docstring'''



		_snake_case    :    int       						=  ["""image_processor""", """tokenizer"""]
		_snake_case    :    List[Any]       						=  """LayoutLMv2ImageProcessor"""
		_snake_case    :    int       						=  ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



		def __init__(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							int:
							if "feature_extractor" in kwargs:
												warnings.warn(
												    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
												    ' instead.'				,		__lowercase				,		)
												UpperCAmelCase_    :      List[str]            =						kwargs.pop('feature_extractor'   )

							UpperCAmelCase_    :      List[Any]            =						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__(__lowercase				,		__lowercase   )



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = True				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 0				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = False				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							BatchEncoding:
							# verify input
							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
							UpperCAmelCase_    :      Optional[int]            =						self.image_processor(images=__lowercase				,		return_tensors=__lowercase   )

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

							UpperCAmelCase_    :      Union[str, Any]            =						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=__lowercase				,		add_special_tokens=__lowercase				,		padding=__lowercase				,		truncation=__lowercase				,		max_length=__lowercase				,		stride=__lowercase				,		pad_to_multiple_of=__lowercase				,		return_token_type_ids=__lowercase				,		return_attention_mask=__lowercase				,		return_overflowing_tokens=__lowercase				,		return_special_tokens_mask=__lowercase				,		return_offsets_mapping=__lowercase				,		return_length=__lowercase				,		verbose=__lowercase				,		return_tensors=__lowercase				,		**__lowercase				,		)

							# add pixel values
							UpperCAmelCase_    :      Any            =						features.pop('pixel_values'   )
							if return_overflowing_tokens is True:
												UpperCAmelCase_    :      str            =						self.get_overflowing_images(__lowercase				,		encoded_inputs['overflow_to_sample_mapping']   )
							UpperCAmelCase_    :      List[str]            =						images

							return encoded_inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							UpperCAmelCase_    :      int            =						[]
							for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]   )

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

							return images_with_overflow



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Tuple:
							return self.tokenizer.batch_decode(*__lowercase				,		**__lowercase   )



		def 					__UpperCAmelCase			(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							Any:
							return self.tokenizer.decode(*__lowercase				,		**__lowercase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							return ["input_ids", "bbox", "attention_mask", "image"]



		@property
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		__lowercase				,		)
							return self.image_processor_class



		@property
		def 					__UpperCAmelCase			(							self   )	->							Any:
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		__lowercase				,		)
							return self.image_processor










from __future__ import annotations

import math







class  snake_case__:







						'''simple docstring'''




						def __init__( self      ,  __lowercase							)    ->			None:
										lowerCAmelCase_       :					str			 = size
										# approximate the overall size of segment tree with given value
										lowerCAmelCase_       :					Dict			 = [0 for i in range(0      ,  4 * size							)]
										# create array to store lazy update
										lowerCAmelCase_       :					Dict			 = [0 for i in range(0      ,  4 * size							)]
										lowerCAmelCase_       :					Optional[int]			 = [0 for i in range(0      ,  4 * size							)]  # flag for lazy update




						def   lowercase_       ( self      ,  __lowercase							)    ->			int:
										return idx * 2




						def   lowercase_       ( self      ,  __lowercase							)    ->			int:
										return idx * 2 + 1




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			None:
										if left_element == right_element:
														lowerCAmelCase_       :					Tuple			 = a[left_element - 1]
										else:
														lowerCAmelCase_       :					int			 = (left_element + right_element) // 2
														self.build(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
														self.build(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase							)
														lowerCAmelCase_       :					Any			 = max(
														    self.segment_tree[self.left(__lowercase							)]      ,  self.segment_tree[self.right(__lowercase							)]							)




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			bool:
										if self.flag[idx] is True:
														lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
														lowerCAmelCase_       :					Union[str, Any]			 = False
														if left_element != right_element:
																		lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
																		lowerCAmelCase_       :					Any			 = self.lazy[idx]
																		lowerCAmelCase_       :					List[str]			 = True
																		lowerCAmelCase_       :					Optional[Any]			 = True

										if right_element < a or left_element > b:
														return True
										if left_element >= a and right_element <= b:
														lowerCAmelCase_       :					Dict			 = val
														if left_element != right_element:
																		lowerCAmelCase_       :					Union[str, Any]			 = val
																		lowerCAmelCase_       :					List[Any]			 = val
																		lowerCAmelCase_       :					Optional[Any]			 = True
																		lowerCAmelCase_       :					List[str]			 = True
														return True
										lowerCAmelCase_       :					Optional[Any]			 = (left_element + right_element) // 2
										self.update(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										self.update(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										lowerCAmelCase_       :					int			 = max(
										    self.segment_tree[self.left(__lowercase							)]      ,  self.segment_tree[self.right(__lowercase							)]							)
										return True




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)    ->			int | float:
										if self.flag[idx] is True:
														lowerCAmelCase_       :					Union[str, Any]			 = self.lazy[idx]
														lowerCAmelCase_       :					Optional[Any]			 = False
														if left_element != right_element:
																		lowerCAmelCase_       :					List[Any]			 = self.lazy[idx]
																		lowerCAmelCase_       :					Dict			 = self.lazy[idx]
																		lowerCAmelCase_       :					Optional[int]			 = True
																		lowerCAmelCase_       :					Optional[int]			 = True
										if right_element < a or left_element > b:
														return -math.inf
										if left_element >= a and right_element <= b:
														return self.segment_tree[idx]
										lowerCAmelCase_       :					List[Any]			 = (left_element + right_element) // 2
										lowerCAmelCase_       :					Tuple			 = self.query(self.left(__lowercase							)      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										lowerCAmelCase_       :					List[Any]			 = self.query(self.right(__lowercase							)      ,  mid + 1      ,  __lowercase      ,  __lowercase      ,  __lowercase							)
										return max(__lowercase      ,  __lowercase							)




						def __str__( self							)    ->			str:
										return str([self.query(1      ,  1      ,  self.size      ,  __lowercase      ,  __lowercase							) for i in range(1      ,  self.size + 1							)]							)


if __name__ == "__main__":
						_UpperCAmelCase	:     str   =[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
						_UpperCAmelCase	:     List[str]   =15
						_UpperCAmelCase	:     Any   =SegmentTree(size)
						segt.build(1, 1, size, A)
						print(segt.query(1, 1, size, 4, 6))
						print(segt.query(1, 1, size, 7, 11))
						print(segt.query(1, 1, size, 7, 12))
						segt.update(1, 1, size, 1, 3, 111)
						print(segt.query(1, 1, size, 1, 15))
						segt.update(1, 1, size, 7, 8, 235)
						print(segt)