





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
			import torch


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							["image_processor", "tokenizer"]
	_A       	=							"OneFormerImageProcessor"
	_A       	=							("CLIPTokenizer", "CLIPTokenizerFast")

	def __init__(	self:	Tuple,				lowerCamelCase_:	int=None,				lowerCamelCase_:	Tuple=None,				lowerCamelCase_:	int = 77,				lowerCamelCase_:	int = 77,				**lowerCamelCase_:	Optional[int] ):
			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`.' )

			lowercase__				:			Any            =		max_seq_length
			lowercase__				:			Tuple            =		task_seq_length

			super().__init__(lowerCamelCase_,				lowerCamelCase_ )

	def       snake_case__(	self:	List[str],				lowerCamelCase_:	int=None,				lowerCamelCase_:	Optional[int]=77 ):
			if text_list is None:
					raise ValueError('tokens cannot be None.' )

			lowercase__				:			Any            =		self.tokenizer(lowerCamelCase_,				padding='max_length',				max_length=lowerCamelCase_,				truncation=lowerCamelCase_ )

			lowercase__      ,							lowercase__				:			List[Any]            =		tokens['attention_mask'], tokens['input_ids']

			lowercase__				:			List[str]            =		[]
			for attn_mask, input_id in zip(lowerCamelCase_,				lowerCamelCase_ ):
					lowercase__				:			Tuple            =		torch.tensor(lowerCamelCase_ ) * torch.tensor(lowerCamelCase_ )
					token_inputs.append(token.unsqueeze(0 ) )

			lowercase__				:			List[Any]            =		torch.cat(lowerCamelCase_,				dim=0 )
			return token_inputs

	def __call__(	self:	List[Any],				lowerCamelCase_:	Optional[Any]=None,				lowerCamelCase_:	Any=None,				lowerCamelCase_:	str=None,				**lowerCamelCase_:	int ):

			if task_inputs is None:
					raise ValueError('You have to specify the task_input. Found None.' )
			elif images is None:
					raise ValueError('You have to specify the image. Found None.' )

			if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs ):
					raise ValueError('task_inputs must be semantic, instance, or panoptic.' )

			lowercase__				:			Tuple            =		self.image_processor(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ )

			if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
					lowercase__				:			List[Any]            =		[task_inputs]

			if isinstance(lowerCamelCase_,				lowerCamelCase_ ) and all(isinstance(lowerCamelCase_,				lowerCamelCase_ ) for task_input in task_inputs ):
					lowercase__				:			List[str]            =		[]
					for task in task_inputs:
							lowercase__				:			str            =		F"""the task is {task}"""
							task_token_inputs.append(lowerCamelCase_ )
					lowercase__				:			Union[str, Any]            =		self._preprocess_text(lowerCamelCase_,				max_length=self.task_seq_length )
			else:
					raise TypeError('Task Inputs should be a string or a list of strings.' )

			if hasattr(lowerCamelCase_,				'text_inputs' ):
					lowercase__				:			Dict            =		encoded_inputs.text_inputs

					lowercase__				:			str            =		[]
					for texts in texts_list:
							lowercase__				:			Optional[int]            =		self._preprocess_text(lowerCamelCase_,				max_length=self.max_seq_length )
							text_inputs.append(text_input_list.unsqueeze(0 ) )

					lowercase__				:			str            =		torch.cat(lowerCamelCase_,				dim=0 )

			return encoded_inputs

	def       snake_case__(	self:	Tuple,				lowerCamelCase_:	Optional[int]=None,				lowerCamelCase_:	List[Any]=None,				lowerCamelCase_:	str=None,				**lowerCamelCase_:	str ):

			if task_inputs is None:
					raise ValueError('You have to specify the task_input. Found None.' )
			elif images is None:
					raise ValueError('You have to specify the image. Found None.' )

			if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs ):
					raise ValueError('task_inputs must be semantic, instance, or panoptic.' )

			lowercase__				:			str            =		self.image_processor.encode_inputs(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ )

			if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
					lowercase__				:			Any            =		[task_inputs]

			if isinstance(lowerCamelCase_,				lowerCamelCase_ ) and all(isinstance(lowerCamelCase_,				lowerCamelCase_ ) for task_input in task_inputs ):
					lowercase__				:			Optional[int]            =		[]
					for task in task_inputs:
							lowercase__				:			int            =		F"""the task is {task}"""
							task_token_inputs.append(lowerCamelCase_ )
					lowercase__				:			Optional[Any]            =		self._preprocess_text(lowerCamelCase_,				max_length=self.task_seq_length )
			else:
					raise TypeError('Task Inputs should be a string or a list of strings.' )

			if hasattr(lowerCamelCase_,				'text_inputs' ):
					lowercase__				:			Optional[Any]            =		encoded_inputs.text_inputs

					lowercase__				:			Tuple            =		[]
					for texts in texts_list:
							lowercase__				:			Optional[int]            =		self._preprocess_text(lowerCamelCase_,				max_length=self.max_seq_length )
							text_inputs.append(text_input_list.unsqueeze(0 ) )

					lowercase__				:			Optional[int]            =		torch.cat(lowerCamelCase_,				dim=0 )

			return encoded_inputs

	def       snake_case__(	self:	Dict,				*lowerCamelCase_:	Tuple,				**lowerCamelCase_:	str ):
			return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	List[str],				*lowerCamelCase_:	List[str],				**lowerCamelCase_:	Optional[int] ):
			return self.image_processor.post_process_instance_segmentation(*lowerCamelCase_,				**lowerCamelCase_ )





	def       snake_case__(	self:	List[str],				*lowerCamelCase_:	Tuple,				**lowerCamelCase_:	Dict ):
			return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase_,				**lowerCamelCase_ )









def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   list )      ->       list:




		'''simple docstring'''
		lowercase__				:			Any            =		len(_lowercase )
		for cycle_start in range(0      ,						array_len - 1 ):
				lowercase__				:			List[str]            =		array[cycle_start]

				lowercase__				:			str            =		cycle_start
				for i in range(cycle_start + 1      ,						_lowercase ):
						if array[i] < item:
								pos += 1

				if pos == cycle_start:
						continue

				while item == array[pos]:
						pos += 1

				lowercase__      ,							lowercase__				:			Optional[Any]            =		item, array[pos]
				while pos != cycle_start:
						lowercase__				:			str            =		cycle_start
						for i in range(cycle_start + 1      ,						_lowercase ):
								if array[i] < item:
										pos += 1

						while item == array[pos]:
								pos += 1

						lowercase__      ,							lowercase__				:			Tuple            =		item, array[pos]

		return array


if __name__ == "__main__":
			assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


