


from typing import List

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


if is_torch_available():
					import torch

class      __snake_case				(   A_):
					SCREAMING_SNAKE_CASE_		:       str								=			['image_processor', 'tokenizer']
					SCREAMING_SNAKE_CASE_		:       Any								=			'OneFormerImageProcessor'
					SCREAMING_SNAKE_CASE_		:       str								=			('CLIPTokenizer', 'CLIPTokenizerFast')
					def __init__(						self       :						List[str]	,      _lowerCAmelCase       :						int=None	,      _lowerCAmelCase       :						Optional[int]=None	,      _lowerCAmelCase       :						List[str] = 7_7	,      _lowerCAmelCase       :						int = 7_7	,      **_lowerCAmelCase       :						Optional[int]  )							->					int:



											'''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`.'  )

											__magic_name__					=      max_seq_length
											__magic_name__					=      task_seq_length

											super().__init__(_lowerCAmelCase	,      _lowerCAmelCase  )
					def 	a			(						self       :						str	,      _lowerCAmelCase       :						Dict=None	,      _lowerCAmelCase       :						Union[str, Any]=7_7  )							->					List[str]:



											'''simple docstring'''





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

											__magic_name__					=      self.tokenizer(_lowerCAmelCase	,      padding='max_length'	,      max_length=_lowerCAmelCase	,      truncation=_lowerCAmelCase  )

											__magic_name__		,      __magic_name__					=      tokens['attention_mask'], tokens['input_ids']

											__magic_name__					=      []
											for attn_mask, input_id in zip(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      torch.tensor(_lowerCAmelCase  ) * torch.tensor(_lowerCAmelCase  )
																	token_inputs.append(token.unsqueeze(0  )  )

											__magic_name__					=      torch.cat(_lowerCAmelCase	,      dim=0  )
											return token_inputs
					def __call__(						self       :						Tuple	,      _lowerCAmelCase       :						Dict=None	,      _lowerCAmelCase       :						Optional[int]=None	,      _lowerCAmelCase       :						List[str]=None	,      **_lowerCAmelCase       :						Tuple  )							->					str:



											'''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.'  )

											__magic_name__					=      self.image_processor(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      **_lowerCAmelCase  )

											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      [task_inputs]

											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) and all(isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) for task_input in task_inputs  ):
																	__magic_name__					=      []
																	for task in task_inputs:
																							__magic_name__					=      F'''the task is {task}'''
																							task_token_inputs.append(_lowerCAmelCase  )
																	__magic_name__					=      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'  ):
																	__magic_name__					=      encoded_inputs.text_inputs

																	__magic_name__					=      []
																	for texts in texts_list:
																							__magic_name__					=      self._preprocess_text(_lowerCAmelCase	,      max_length=self.max_seq_length  )
																							text_inputs.append(text_input_list.unsqueeze(0  )  )

																	__magic_name__					=      torch.cat(_lowerCAmelCase	,      dim=0  )

											return encoded_inputs
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						Any=None	,      _lowerCAmelCase       :						Optional[Any]=None	,      _lowerCAmelCase       :						Union[str, Any]=None	,      **_lowerCAmelCase       :						int  )							->					Union[str, 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.'  )

											__magic_name__					=      self.image_processor.encode_inputs(_lowerCAmelCase	,      _lowerCAmelCase	,      _lowerCAmelCase	,      **_lowerCAmelCase  )

											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      [task_inputs]

											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) and all(isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ) for task_input in task_inputs  ):
																	__magic_name__					=      []
																	for task in task_inputs:
																							__magic_name__					=      F'''the task is {task}'''
																							task_token_inputs.append(_lowerCAmelCase  )
																	__magic_name__					=      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'  ):
																	__magic_name__					=      encoded_inputs.text_inputs

																	__magic_name__					=      []
																	for texts in texts_list:
																							__magic_name__					=      self._preprocess_text(_lowerCAmelCase	,      max_length=self.max_seq_length  )
																							text_inputs.append(text_input_list.unsqueeze(0  )  )

																	__magic_name__					=      torch.cat(_lowerCAmelCase	,      dim=0  )

											return encoded_inputs
					def 	a			(						self       :						Optional[Any]	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						Dict  )							->					List[Any]:



											'''simple docstring'''





											return self.image_processor.post_process_semantic_segmentation(*_lowerCAmelCase	,      **_lowerCAmelCase  )
					def 	a			(						self       :						List[str]	,      *_lowerCAmelCase       :						Tuple	,      **_lowerCAmelCase       :						Tuple  )							->					Union[str, Any]:



											'''simple docstring'''





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







					def 	a			(						self       :						List[Any]	,      *_lowerCAmelCase       :						int	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					int:



											'''simple docstring'''





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






def    UpperCAmelCase					(  lowercase__						:					int = 998001	):



			'''simple docstring'''





			for number in range(n - 1				,					9999				,					-1	):
						a__					=  str(lowercase__	)

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									a__					=  999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor	)	) == 3.0):
															return number
												divisor -= 1
			raise ValueError("""That number is larger than our acceptable range."""	)


if __name__ == "__main__":
	print(f'''{solution() = }''')

