



"""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 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				['image_processor', 'tokenizer']
					_a       =				'LayoutLMv2ImageProcessor'
					_a       =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    Any=None,    lowerCamelCase				:    int=None,    **lowerCamelCase				:    Any	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =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__						:						Dict						      =kwargs.pop('''feature_extractor'''	)

								lowerCamelCase__						:						Tuple						      =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	)



					def __call__(			self				:    Union[str, Any],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,    lowerCamelCase				:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,    lowerCamelCase				:    Union[List[List[int]], List[List[List[int]]]] = None,    lowerCamelCase				:    Optional[Union[List[int], List[List[int]]]] = None,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Union[bool, str, PaddingStrategy] = False,    lowerCamelCase				:    Union[bool, str, TruncationStrategy] = False,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    int = 0,    lowerCamelCase				:    Optional[int] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    Optional[bool] = None,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Optional[Union[str, TensorType]] = None,    **lowerCamelCase				:    Optional[Any],    )->   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
								lowerCamelCase__						:						Dict						      =self.image_processor(images=lowerCamelCase,    return_tensors=lowerCamelCase	)

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

								lowerCamelCase__						:						Tuple						      =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=lowerCamelCase,    add_special_tokens=lowerCamelCase,    padding=lowerCamelCase,    truncation=lowerCamelCase,    max_length=lowerCamelCase,    stride=lowerCamelCase,    pad_to_multiple_of=lowerCamelCase,    return_token_type_ids=lowerCamelCase,    return_attention_mask=lowerCamelCase,    return_overflowing_tokens=lowerCamelCase,    return_special_tokens_mask=lowerCamelCase,    return_offsets_mapping=lowerCamelCase,    return_length=lowerCamelCase,    verbose=lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase,    )

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

								return encoded_inputs



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Dict	)->   Optional[int]:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								lowerCamelCase__						:						Optional[int]						      =[]
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]	)

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

								return images_with_overflow



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    List[str],    **lowerCamelCase				:    str	)->   List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    Tuple,    *lowerCamelCase				:    Dict,    **lowerCamelCase				:    Tuple	)->   Any:
								return self.tokenizer.decode(*lowerCamelCase,    **lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    List[str]	)->   Tuple:
								return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



					@property
					def 						snake_case			(			self				:    List[Any]	)->   List[str]:
								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 						snake_case			(			self				:    Optional[Any]	)->   Optional[Any]:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',    lowerCamelCase,    )
								return self.image_processor





'''simple docstring'''


from __future__ import annotations

snake_case__	:     str      =    [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  int  = -1
       UpperCAmelCase_				:  list[int]  = [-1] * length
       UpperCAmelCase_				:  Node | None  = None
       UpperCAmelCase_				:  Node | None  = None






  def __repr__(					self		):




       '''simple docstring'''

       return F'''Node(min_value={self.minn} max_value={self.maxx})'''







def    _lowerCamelCase       ( lowerCamelCase_   :				list[int]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[str]  = Node(len(lowerCamelCase_  )  )
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = min(lowerCamelCase_  ), max(lowerCamelCase_  )
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     UpperCAmelCase_				:  str  = (root.minn + root.maxx) // 2

     UpperCAmelCase_				:  list[int]  = []
     UpperCAmelCase_				:  list[int]  = []

     for index, num in enumerate(lowerCamelCase_  ):
          if num <= pivot:
               left_arr.append(lowerCamelCase_  )
          else:
               right_arr.append(lowerCamelCase_  )
          UpperCAmelCase_				:  Tuple  = len(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[Any]  = build_tree(lowerCamelCase_  )
     return root


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index < 0 or node is None:
          return 0
     # Leaf node cases
     if node.minn == node.maxx:
          return index + 1 if node.minn == num else 0
     UpperCAmelCase_				:  Any  = (node.minn + node.maxx) // 2
     if num <= pivot:
          # go the left subtree and map index to the left subtree
          return rank_till_index(node.left      ,	lowerCamelCase_      ,	node.map_left[index] - 1  )
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right      ,	lowerCamelCase_      ,	index - node.map_left[index]  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if start > end:
          return 0
     UpperCAmelCase_				:  int  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_  )
     UpperCAmelCase_				:  List[Any]  = rank_till_index(lowerCamelCase_      ,	lowerCamelCase_      ,	start - 1  )
     return rank_till_end - rank_before_start


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if index > (end - start) or start > end or node is None:
          return -1
     # Leaf node case
     if node.minn == node.maxx:
          return node.minn
     # Number of elements in the left subtree in interval [start, end]
     UpperCAmelCase_				:  Tuple  = node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left      ,	lowerCamelCase_      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	)
     else:
          return quantile(
              node.right      ,	index - num_elements_in_left_tree      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	)


def    _lowerCamelCase       ( lowerCamelCase_   :				Node | None      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if (
         start > end
         or node is None
         or start_num > end_num
         or node.minn > end_num
         or node.maxx < start_num
     ):
          return 0
     if start_num <= node.minn and node.maxx <= end_num:
          return end - start + 1
     UpperCAmelCase_				:  Any  = range_counting(
         node.left      ,	(node.map_left[start - 1] if start else 0)      ,	node.map_left[end] - 1      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     UpperCAmelCase_				:  List[str]  = range_counting(
         node.right      ,	start - (node.map_left[start - 1] if start else 0)      ,	end - node.map_left[end]      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	)
     return left + right


if __name__ == "__main__":
 import doctest

 doctest.testmod()






