
from typing import List

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


if is_torch_available():
					import torch







class 	A__				(	__magic_name__      ):
				lowercase            =    ['image_processor', 'tokenizer']
				lowercase            =    'OneFormerImageProcessor'
				lowercase            =    ('CLIPTokenizer', 'CLIPTokenizerFast')






				def __init__(		self   : Optional[Any]       ,	a   : Optional[int]=None       ,	a   : Dict=None       ,	a   : Optional[Any] = 77       ,	a   : Optional[Any] = 77       ,	**a   : Union[str, Any]  ):






							'''simple docstring'''







							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`.'  )

							lowerCAmelCase__ : int   =						max_seq_length
							lowerCAmelCase__ : Union[str, Any]   =						task_seq_length

							super().__init__(UpperCAmelCase_       ,	UpperCAmelCase_  )






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Dict=None       ,	a   : Any=77  ):






							'''simple docstring'''







							if text_list is None:
										raise ValueError('tokens cannot be None.'  )

							lowerCAmelCase__ : Optional[Any]   =						self.tokenizer(UpperCAmelCase_       ,	padding='max_length'       ,	max_length=UpperCAmelCase_       ,	truncation=UpperCAmelCase_  )

							lowerCAmelCase__ ,							lowerCAmelCase__ : int   =						tokens['attention_mask'], tokens['input_ids']

							lowerCAmelCase__ : Union[str, Any]   =						[]
							for attn_mask, input_id in zip(UpperCAmelCase_       ,	UpperCAmelCase_  ):
										lowerCAmelCase__ : Any   =						torch.tensor(UpperCAmelCase_  ) * torch.tensor(UpperCAmelCase_  )
										token_inputs.append(token.unsqueeze(0  )  )

							lowerCAmelCase__ : Any   =						torch.cat(UpperCAmelCase_       ,	dim=0  )
							return token_inputs






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






							'''simple docstring'''







							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.'  )

							lowerCAmelCase__ : Tuple   =						self.image_processor(UpperCAmelCase_       ,	UpperCAmelCase_       ,	UpperCAmelCase_       ,	**UpperCAmelCase_  )

							if isinstance(UpperCAmelCase_       ,	UpperCAmelCase_  ):
										lowerCAmelCase__ : Any   =						[task_inputs]

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

							if hasattr(UpperCAmelCase_       ,	'text_inputs'  ):
										lowerCAmelCase__ : Tuple   =						encoded_inputs.text_inputs

										lowerCAmelCase__ : List[Any]   =						[]
										for texts in texts_list:
													lowerCAmelCase__ : Tuple   =						self._preprocess_text(UpperCAmelCase_       ,	max_length=self.max_seq_length  )
													text_inputs.append(text_input_list.unsqueeze(0  )  )

										lowerCAmelCase__ : str   =						torch.cat(UpperCAmelCase_       ,	dim=0  )

							return encoded_inputs






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Any=None       ,	a   : List[str]=None       ,	a   : Tuple=None       ,	**a   : Any  ):






							'''simple docstring'''







							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.'  )

							lowerCAmelCase__ : int   =						self.image_processor.encode_inputs(UpperCAmelCase_       ,	UpperCAmelCase_       ,	UpperCAmelCase_       ,	**UpperCAmelCase_  )

							if isinstance(UpperCAmelCase_       ,	UpperCAmelCase_  ):
										lowerCAmelCase__ : List[Any]   =						[task_inputs]

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

							if hasattr(UpperCAmelCase_       ,	'text_inputs'  ):
										lowerCAmelCase__ : List[Any]   =						encoded_inputs.text_inputs

										lowerCAmelCase__ : Any   =						[]
										for texts in texts_list:
													lowerCAmelCase__ : List[Any]   =						self._preprocess_text(UpperCAmelCase_       ,	max_length=self.max_seq_length  )
													text_inputs.append(text_input_list.unsqueeze(0  )  )

										lowerCAmelCase__ : List[Any]   =						torch.cat(UpperCAmelCase_       ,	dim=0  )

							return encoded_inputs






				def 				_lowerCamelCase						(		self   : Optional[Any]       ,	*a   : List[str]       ,	**a   : List[Any]  ):






							'''simple docstring'''







							return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase_       ,	**UpperCAmelCase_  )






				def 				_lowerCamelCase						(		self   : str       ,	*a   : List[str]       ,	**a   : Dict  ):






							'''simple docstring'''







							return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase_       ,	**UpperCAmelCase_  )






				def 				_lowerCamelCase						(		self   : Tuple       ,	*a   : Any       ,	**a   : Tuple  ):






							'''simple docstring'''







							return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase_       ,	**UpperCAmelCase_  )






def   _lowercase       (    _UpperCAmelCase					)    ->	bool:
				if not isinstance(_UpperCAmelCase      ,   _UpperCAmelCase					):
								lowerCamelCase									=F"""Input value of [number={number}] must be an integer"""
								raise TypeError(_UpperCAmelCase					)
				if number < 0 or number % 2 == 1:
								return False
				lowerCamelCase									=int(number**0.5					)
				return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





