





'''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 				lowerCAmelCase_						(						lowercase_						):
	__lowerCamelCase  :							Optional[Any]    					=   ["image_processor", "tokenizer"]
	__lowerCamelCase  :							List[str]    					=   "LayoutLMv2ImageProcessor"
	__lowerCamelCase  :							Optional[Any]    					=   ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")




	def __init__(		self     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       **_lowerCAmelCase						)      ->							Optional[int]:
					_lowerCAmelCase		     = 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     ,       )
									_lowerCAmelCase		     = kwargs.pop("feature_extractor"						)

					_lowerCAmelCase		     = 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     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 0     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							str:
					# 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		     = 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						):
													_lowerCAmelCase		     = [text]  # add batch dimension (as the image processor always adds a batch dimension)
									_lowerCAmelCase		     = features["words"]

					_lowerCAmelCase		     = 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
					_lowerCAmelCase		     = features.pop("pixel_values"						)
					if return_overflowing_tokens is True:
									_lowerCAmelCase		     = self.get_overflowing_images(__UpperCamelCase     ,       encoded_inputs["overflow_to_sample_mapping"]						)
					_lowerCAmelCase		     = images

					return encoded_inputs




	def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Any:
					# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
					_lowerCAmelCase		     = []
					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 							_snake_case				(		self     ,       *_lowerCAmelCase     ,       **_lowerCAmelCase						)      ->							int:
					return self.tokenizer.batch_decode(*__UpperCamelCase     ,       **__UpperCamelCase						)




	def 							_snake_case				(		self     ,       *_lowerCAmelCase     ,       **_lowerCAmelCase						)      ->							Any:
					return self.tokenizer.decode(*__UpperCamelCase     ,       **__UpperCamelCase						)




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




	@property
	def 							_snake_case				(		self						)      ->							int:
					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 							_snake_case				(		self						)      ->							List[str]:
					warnings.warn(
					    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,       __UpperCamelCase     ,       )
					return self.image_processor









'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE			=							TypeVar("T", bound=bool)

class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = value
     _lowerCAmelCase		     = None
     _lowerCAmelCase		     = None




 @property
 def 							_snake_case				(		self						)      ->							T:
     return self._value





 @staticmethod
 def 							_snake_case				(		_lowerCAmelCase     ,       _lowerCAmelCase						)      ->							SkewNode[T] | None:
     if not roota:
         return roota

     if not roota:
         return roota

     if roota.value > roota.value:
         _lowerCAmelCase     ,     _lowerCAmelCase		     = roota, roota

     _lowerCAmelCase		     = roota
     _lowerCAmelCase		     = roota.right
     _lowerCAmelCase		     = roota.left
     _lowerCAmelCase		     = SkewNode.merge(_lowerCAmelCase     ,       _lowerCAmelCase						)

     return result







class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase = ()						)      ->							None:
     _lowerCAmelCase		     = None
     if data:
         for item in data:
             self.insert(_lowerCAmelCase						)




 def __bool__(		self						)      ->							bool:
     return self._root is not None




 def __iter__(		self						)      ->							Iterator[T]:
     _lowerCAmelCase		     = []
     while self:
         result.append(self.pop()						)

     # Pushing items back to the heap not to clear it.
     for item in result:
         self.insert(_lowerCAmelCase						)

     return iter(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = SkewNode.merge(self._root     ,       SkewNode(_lowerCAmelCase						)						)




 def 							_snake_case				(		self						)      ->							T | None:
     _lowerCAmelCase		     = self.top()
     _lowerCAmelCase		     = (
         SkewNode.merge(self._root.left     ,       self._root.right						) if self._root else None
     )

     return result




 def 							_snake_case				(		self						)      ->							T:
     if not self._root:
         raise IndexError("Can't get top element for the empty heap."						)
     return self._root.value





 def 							_snake_case				(		self						)      ->							None:
     _lowerCAmelCase		     = None


if __name__ == "__main__":
       import doctest

       doctest.testmod()


