



'''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        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  str   =				["""image_processor""", """tokenizer"""]
					a   :  Any   =				"""LayoutLMv2ImageProcessor"""
					a   :  Any   =				("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")



					def __init__(     self      ,					UpperCamelCase=None      ,					UpperCamelCase=None      ,					**UpperCamelCase  )  ->      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      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = True      ,					UpperCamelCase = False      ,					UpperCamelCase = False      ,					UpperCamelCase = None      ,					UpperCamelCase = 0      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = False      ,					UpperCamelCase = False      ,					UpperCamelCase = False      ,					UpperCamelCase = False      ,					UpperCamelCase = True      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      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       				=	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 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      Optional[int]:
						# 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 							UpperCAmelCase_			(     self      ,					*UpperCamelCase      ,					**UpperCamelCase  )  ->      Dict:
						return self.tokenizer.batch_decode(*UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					*UpperCamelCase      ,					**UpperCamelCase  )  ->      Any:
						return self.tokenizer.decode(*UpperCamelCase      ,					**UpperCamelCase  )



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



					@property
					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						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 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						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 random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	return [*quick_sort(lowerCamelCase       ), pivot, *quick_sort(lowerCamelCase       )]


if __name__ == "__main__":
	lowerCAmelCase						:		List[Any]						      = input('''Enter numbers separated by a comma:\n''').strip()
	lowerCAmelCase						:		Any						      = [int(item) for item in user_input.split(''',''')]
	print(quick_sort(unsorted))