

"""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        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''image_processor''', '''tokenizer''']
     _lowerCamelCase     =       '''LayoutLMv3ImageProcessor'''
     _lowerCamelCase     =       ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







     def __init__(      self ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						**UpperCamelCase_       ):
      __magic_name__     =      None
      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.''' ,						UpperCamelCase_ ,						)
       __magic_name__     =      kwargs.pop('''feature_extractor'''       )

      __magic_name__     =      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 ,						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_ ,						):
      # 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.'''       )

      # first, apply the image processor
      __magic_name__     =      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_       ):
        __magic_name__     =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
       __magic_name__     =      features['''words''']

      __magic_name__     =      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
      __magic_name__     =      features.pop('''pixel_values'''       )
      if return_overflowing_tokens is True:
       __magic_name__     =      self.get_overflowing_images(UpperCamelCase_ ,						encoded_inputs['''overflow_to_sample_mapping''']       )
      __magic_name__     =      images

      return encoded_inputs







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
      __magic_name__     =      []
      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 	lowerCAmelCase__    (      self ,						*UpperCamelCase_ ,						**UpperCamelCase_       ):
      return self.tokenizer.batch_decode(*UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						*UpperCamelCase_ ,						**UpperCamelCase_       ):
      return self.tokenizer.decode(*UpperCamelCase_ ,						**UpperCamelCase_       )







     @property
     def 	lowerCAmelCase__    (      self       ):
      return ["input_ids", "bbox", "attention_mask", "pixel_values"]







     @property
     def 	lowerCAmelCase__    (      self       ):
      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 	lowerCAmelCase__    (      self       ):
      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,						UpperCamelCase_ ,						)
      return self.image_processor




"""simple docstring"""






from collections.abc import Sequence
from queue import Queue
class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None ,						UpperCamelCase_=None       ):
      __magic_name__     =      start
      __magic_name__     =      end
      __magic_name__     =      val
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      left
      __magic_name__     =      right







     def __repr__(      self       ):
      return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''


class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      collection
      __magic_name__     =      function
      if self.collection:
       __magic_name__     =      self._build_tree(0 ,						len(UpperCamelCase_       ) - 1       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      self._update_tree(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      return self._query_range(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if start == end:
       return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.collection[start]       )
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      self._build_tree(UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self._build_tree(mid + 1 ,						UpperCamelCase_       )
      return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.fn(left.val ,						right.val       ) ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == i:
       __magic_name__     =      val
       return
      if i <= node.mid:
       self._update_tree(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
      else:
       self._update_tree(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.fn(node.left.val ,						node.right.val       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == j:
       return node.val

      if i <= node.mid:
       if j <= node.mid:
        # range in left child tree
        return self._query_range(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
       else:
        # range in left child tree and right child tree
        return self.fn(
            self._query_range(node.left ,						UpperCamelCase_ ,						node.mid       ) ,						self._query_range(node.right ,						node.mid + 1 ,						UpperCamelCase_       ) ,						)
      else:
       # range in right child tree
       return self._query_range(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      if self.root is not None:
       __magic_name__     =      Queue()
       queue.put(self.root       )
       while not queue.empty():
        __magic_name__     =      queue.get()
        yield node

        if node.left is not None:
         queue.put(node.left       )

        if node.right is not None:
         queue.put(node.right       )


if __name__ == "__main__":
 import operator

 for fn in [operator.add, max, min]:
  print("*" * 50)
  __lowerCamelCase										=		SegmentTree([2, 1, 5, 3, 4], fn)
  for node in arr.traverse():
   print(node)
  print()

  arr.update(1, 5)
  for node in arr.traverse():
   print(node)
  print()

  print(arr.query_range(3, 4))  # 7
  print(arr.query_range(2, 2))  # 5
  print(arr.query_range(1, 3))  # 13
  print()

