





"""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 			_a						(				_a    ):
				UpperCamelCase								=   ['''image_processor''', '''tokenizer''']
				UpperCamelCase								=   '''LayoutLMv2ImageProcessor'''
				UpperCamelCase								=   ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






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




										'''simple docstring'''



										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.''',			snake_case_,			)
																_UpperCamelCase      : Union[str, Any]         =   kwargs.pop('''feature_extractor'''							)

										_UpperCamelCase      : Optional[int]         =   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__(snake_case_,			snake_case_							)






				def __call__(			self		:   int,			lowerCAmelCase__		:   Optional[Any],			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] = None,			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],			)     ->				Dict:




										'''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.'''							)

										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      : List[Any]         =   self.image_processor(images=snake_case_,			return_tensors=snake_case_							)

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

										_UpperCamelCase      : Optional[int]         =   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=snake_case_,			add_special_tokens=snake_case_,			padding=snake_case_,			truncation=snake_case_,			max_length=snake_case_,			stride=snake_case_,			pad_to_multiple_of=snake_case_,			return_token_type_ids=snake_case_,			return_attention_mask=snake_case_,			return_overflowing_tokens=snake_case_,			return_special_tokens_mask=snake_case_,			return_offsets_mapping=snake_case_,			return_length=snake_case_,			verbose=snake_case_,			return_tensors=snake_case_,			**snake_case_,			)

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

										return encoded_inputs






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   List[str]							)     ->				str:




										'''simple docstring'''



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

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

										return images_with_overflow






				def     snake_case     (			self		:   List[str],			*lowerCAmelCase__		:   Any,			**lowerCAmelCase__		:   Optional[Any]							)     ->				List[Any]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   Union[str, Any],			*lowerCAmelCase__		:   Any,			**lowerCAmelCase__		:   Optional[Any]							)     ->				Any:




										'''simple docstring'''



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






				@property
				def     snake_case     (			self		:   int							)     ->				List[str]:




										'''simple docstring'''



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






				@property
				def     snake_case     (			self		:   Dict							)     ->				Optional[Any]:




										'''simple docstring'''



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






				@property
				def     snake_case     (			self		:   Optional[int]							)     ->				int:




										'''simple docstring'''



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










"""simple docstring"""




from __future__ import annotations

from random import choice





def 	a_    (							_lowercase       ):
						return choice(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   random_pivot(_lowercase       )

						# partition based on pivot
						# linear time
						_UpperCamelCase      : Union[str, Any]         =   [e for e in lst if e < pivot]
						_UpperCamelCase      : int         =   [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(_lowercase       ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(_lowercase       ) < k - 1:
												return kth_number(_lowercase	,						k - len(_lowercase       ) - 1       )
						# pivot is in elements smaller than k
						else:
												return kth_number(_lowercase	,						_lowercase       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



