





"""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      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ["image_processor", "tokenizer"]
							UpperCAmelCase_      						=   "LayoutLMv3ImageProcessor"
							UpperCAmelCase_      						=   ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																					    " instead.",			_a,			)
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						kwargs.pop("feature_extractor"						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						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__(_a,			_a						)






							def __call__( self     :					Optional[Any],			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[int] = None,			_UpperCAmelCase     :					List[str] = None,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					List[str] = None,			_UpperCAmelCase     :					Optional[Any] = True,			_UpperCAmelCase     :					Dict = False,			_UpperCAmelCase     :					str = None,			_UpperCAmelCase     :					Optional[int] = None,			_UpperCAmelCase     :					int = 0,			_UpperCAmelCase     :					Optional[Any] = None,			_UpperCAmelCase     :					List[str] = None,			_UpperCAmelCase     :					Optional[Any] = None,			_UpperCAmelCase     :					int = False,			_UpperCAmelCase     :					Optional[Any] = False,			_UpperCAmelCase     :					Optional[int] = False,			_UpperCAmelCase     :					int = False,			_UpperCAmelCase     :					List[Any] = True,			_UpperCAmelCase     :					str = None,			**_UpperCAmelCase     :					List[Any],			)				->			BatchEncoding:





														"""simple docstring"""
														# 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
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.image_processor(images=_a,			return_tensors=_a						)

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

														SCREAMING_SNAKE_CASE__       :      List[str]     =						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=_a,			add_special_tokens=_a,			padding=_a,			truncation=_a,			max_length=_a,			stride=_a,			pad_to_multiple_of=_a,			return_token_type_ids=_a,			return_attention_mask=_a,			return_overflowing_tokens=_a,			return_special_tokens_mask=_a,			return_offsets_mapping=_a,			return_length=_a,			verbose=_a,			return_tensors=_a,			**_a,			)

														# add pixel values
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						features.pop("pixel_values"						)
														if return_overflowing_tokens is True:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_overflowing_images(_a,			encoded_inputs["overflow_to_sample_mapping"]						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						images

														return encoded_inputs






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Tuple						)				->			int:





														"""simple docstring"""
														# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[]
														for sample_idx in overflow_to_sample_mapping:
																					images_with_overflow.append(images[sample_idx]						)

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

														return images_with_overflow






							def  A_     ( self     :					str,			*_UpperCAmelCase     :					List[str],			**_UpperCAmelCase     :					Dict						)				->			Optional[Any]:





														"""simple docstring"""
														return self.tokenizer.batch_decode(*_a,			**_a						)






							def  A_     ( self     :					Optional[Any],			*_UpperCAmelCase     :					Tuple,			**_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														return self.tokenizer.decode(*_a,			**_a						)






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





														"""simple docstring"""
														return ["input_ids", "bbox", "attention_mask", "pixel_values"]






							@property
							def  A_     ( self     :					int						)				->			List[Any]:





														"""simple docstring"""
														warnings.warn(
														    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",			_a,			)
														return self.image_processor_class






							@property
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														warnings.warn(
														    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",			_a,			)
														return self.image_processor













from __future__ import annotations




def _a (  SCREAMING_SNAKE_CASE__      :  list[float]			,				SCREAMING_SNAKE_CASE__      :  list[float]       )	->    float:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						sorted(numsa + numsa       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						divmod(len(SCREAMING_SNAKE_CASE__       )			,				2       )
							if mod == 1:
														return all_numbers[div]
							else:
														return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_lowerCamelCase :    List[str]  							= [float(x) for x in input('''Enter the elements of first array: ''').split()]
		_lowerCamelCase :    Any  							= [float(x) for x in input('''Enter the elements of second array: ''').split()]
		print(f"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}")






