



'''simple docstring'''




from __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int] ):
		if not list_of_ints:
				return []

		if min(_lowerCamelCase ) < 0:
				raise ValueError("""All numbers must be positive""" )

		__SCREAMING_SNAKE_CASE	:		Any   	=		max(len(bin(_lowerCamelCase )[2:] ) for x in list_of_ints )
		return _msd_radix_sort(_lowerCamelCase	,				_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int ):
		if bit_position == 0 or len(_lowerCamelCase ) in [0, 1]:
				return list_of_ints

		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		[]
		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		# Split numbers based on bit at bit_position from the right
		for number in list_of_ints:
				if (number >> (bit_position - 1)) & 1:
						# number has a one at bit bit_position
						ones.append(_lowerCamelCase )
				else:
						# number has a zero at bit bit_position
						zeros.append(_lowerCamelCase )

    # recursively split both lists further
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		_msd_radix_sort(_lowerCamelCase	,				bit_position - 1 )
		__SCREAMING_SNAKE_CASE	:		str   	=		_msd_radix_sort(_lowerCamelCase	,				bit_position - 1 )

		# recombine lists
		__SCREAMING_SNAKE_CASE	:		str   	=		zeros
		res.extend(_lowerCamelCase )

		return res


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int] ):
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		len(_lowerCamelCase )
		if not list_of_ints or length == 1:
				return

		if min(_lowerCamelCase ) < 0:
				raise ValueError("""All numbers must be positive""" )

		__SCREAMING_SNAKE_CASE	:		int   	=		max(len(bin(_lowerCamelCase )[2:] ) for x in list_of_ints )
		_msd_radix_sort_inplace(_lowerCamelCase	,				_lowerCamelCase	,				0	,				_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       int	,				_lowerCamelCase:       int ):
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		begin_index
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		end_index - 1
		while i <= j:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						__SCREAMING_SNAKE_CASE	:		List[str]   	=		True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		True

				if changed:
						continue

				__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		list_of_ints[j], list_of_ints[i]
				j -= 1
				if j != i:
						i += 1

		_msd_radix_sort_inplace(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )
		_msd_radix_sort_inplace(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		['''image_processor''', '''tokenizer''']
			_A				:		Union[str, Any]         =		'''AutoImageProcessor'''
			_A				:		Optional[Any]         =		'''AutoTokenizer'''

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""							,					lowerCAmelCase__							,					)
							__SCREAMING_SNAKE_CASE	:		str   	=		kwargs.pop("""feature_extractor"""						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		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__(lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		Dict   	=		False

			def __call__(							self					:			List[str]							,					*lowerCAmelCase__					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					if self._in_target_context_manager:
							return self.current_processor(*lowerCAmelCase__							,					**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		kwargs.pop("""images"""							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		kwargs.pop("""text"""							,					lowerCAmelCase__						)
					if len(lowerCAmelCase__						) > 0:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		args[0]
							__SCREAMING_SNAKE_CASE	:		int   	=		args[1:]

					if images is None and text is None:
							raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

					if images is not None:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.image_processor(lowerCAmelCase__							,					*lowerCAmelCase__							,					**lowerCAmelCase__						)
					if text is not None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.tokenizer(lowerCAmelCase__							,					**lowerCAmelCase__						)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							__SCREAMING_SNAKE_CASE	:		int   	=		encodings["""input_ids"""]
							return inputs

			def UpperCamelCase__ (							self					:			Any							,					*lowerCAmelCase__					:			Any							,					**lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






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

			def UpperCamelCase__ (							self					:			int							,					*lowerCAmelCase__					:			str							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






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

			@contextmanager
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					warnings.warn(
					    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
					    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
					    """your images inputs, or in a separate call."""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		True
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.tokenizer
					yield
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.image_processor
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		False

			@property
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






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



			@property
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






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