


"""simple docstring"""


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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			['image_processor', 'tokenizer']
		__UpperCamelCase           =			'BridgeTowerImageProcessor'
		__UpperCamelCase           =			('RobertaTokenizer', 'RobertaTokenizerFast')







		def __init__(					self      :       str    ,   a__      :       List[Any]    ,   a__      :       str		)       ->						Dict:





						'''simple docstring'''







						super().__init__(a__    ,   a__		)







		def __call__(					self      :       Tuple    ,   a__      :       Optional[int]    ,   a__      :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,   a__      :       bool = True    ,   a__      :       Union[bool, str, PaddingStrategy] = False    ,   a__      :       Union[bool, str, TruncationStrategy] = None    ,   a__      :       Optional[int] = None    ,   a__      :       int = 0    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[bool] = None    ,   a__      :       Optional[bool] = None    ,   a__      :       bool = False    ,   a__      :       bool = False    ,   a__      :       bool = False    ,   a__      :       bool = False    ,   a__      :       bool = True    ,   a__      :       Optional[Union[str, TensorType]] = None    ,   **a__      :       str    ,   )       ->						BatchEncoding:





						'''simple docstring'''







						_A					  =			self.tokenizer(
						    text=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 + pixel_mask
						_A					  =			self.image_processor(
						    a__    ,   return_tensors=a__    ,   do_normalize=a__    ,   do_center_crop=a__    ,   **a__		)
						encoding.update(a__		)

						return encoding







		def  a_ (					self      :       List[Any]    ,   *a__      :       Dict    ,   **a__      :       str		)       ->						Tuple:





						'''simple docstring'''







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







		def  a_ (					self      :       Optional[int]    ,   *a__      :       Dict    ,   **a__      :       List[str]		)       ->						str:





						'''simple docstring'''







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







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





						'''simple docstring'''







						_A					  =			self.tokenizer.model_input_names
						_A					  =			self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)










"""simple docstring"""


import random






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			a[left_index]
				_A					  =			left_index + 1
				for j in range(left_index + 1     ,			__lowercase   ):
								if a[j] < pivot:
												_A  ,		_A					  =			a[i], a[j]
												i += 1
				_A  ,		_A					  =			a[i - 1], a[left_index]
				return i - 1






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				if left < right:
								_A					  =			random.randint(__lowercase     ,			right - 1   )
								_A  ,		_A					  =			(
								    a[left],
								    a[pivot],
								)  # switches the pivot with the left most bound
								_A					  =			partition(__lowercase     ,			__lowercase     ,			__lowercase   )
								quick_sort_random(
								    __lowercase     ,			__lowercase     ,			__lowercase   )  # recursive quicksort to the left of the pivot point
								quick_sort_random(
								    __lowercase     ,			pivot_index + 1     ,			__lowercase   )  # recursive quicksort to the right of the pivot point






def      a__ (				)	->	Dict:
				_A					  =			input("Enter numbers separated by a comma:\n"   ).strip()
				_A					  =			[int(__lowercase   ) for item in user_input.split(","   )]

				quick_sort_random(__lowercase     ,			0     ,			len(__lowercase   )   )

				print(__lowercase   )


if __name__ == "__main__":
		main()