



from typing import List

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


if is_torch_available():
	import torch
class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     str									=		['image_processor', 'tokenizer']
	A_       :     Any									=		'OneFormerImageProcessor'
	A_       :     str									=		('CLIPTokenizer', 'CLIPTokenizerFast')

	def __init__(	self   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase = 77   ,       lowerCamelCase = 77   ,       **lowerCamelCase       ):



				'''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`."""       )

				a__					=  max_seq_length
				a__					=  task_seq_length

				super().__init__(lowerCamelCase   ,       lowerCamelCase       )

	def 				_A			(	self   ,       lowerCamelCase=None   ,       lowerCamelCase=77       ):



				'''simple docstring'''


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

				a__					=  self.tokenizer(lowerCamelCase   ,       padding="""max_length"""   ,       max_length=lowerCamelCase   ,       truncation=lowerCamelCase       )

				a__				,       a__					=  tokens["""attention_mask"""], tokens["""input_ids"""]

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

				a__					=  torch.cat(lowerCamelCase   ,       dim=0       )
				return token_inputs

	def __call__(	self   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       **lowerCamelCase       ):



				'''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."""       )

				a__					=  self.image_processor(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       **lowerCamelCase       )

				if isinstance(lowerCamelCase   ,       lowerCamelCase       ):
							a__					=  [task_inputs]

				if isinstance(lowerCamelCase   ,       lowerCamelCase       ) and all(isinstance(lowerCamelCase   ,       lowerCamelCase       ) for task_input in task_inputs       ):
							a__					=  []
							for task in task_inputs:
										a__					=  f'the task is {task}'
										task_token_inputs.append(lowerCamelCase       )
							a__					=  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"""       ):
							a__					=  encoded_inputs.text_inputs

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

							a__					=  torch.cat(lowerCamelCase   ,       dim=0       )

				return encoded_inputs

	def 				_A			(	self   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       **lowerCamelCase       ):



				'''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."""       )

				a__					=  self.image_processor.encode_inputs(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       **lowerCamelCase       )

				if isinstance(lowerCamelCase   ,       lowerCamelCase       ):
							a__					=  [task_inputs]

				if isinstance(lowerCamelCase   ,       lowerCamelCase       ) and all(isinstance(lowerCamelCase   ,       lowerCamelCase       ) for task_input in task_inputs       ):
							a__					=  []
							for task in task_inputs:
										a__					=  f'the task is {task}'
										task_token_inputs.append(lowerCamelCase       )
							a__					=  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"""       ):
							a__					=  encoded_inputs.text_inputs

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

							a__					=  torch.cat(lowerCamelCase   ,       dim=0       )

				return encoded_inputs

	def 				_A			(	self   ,       *lowerCamelCase   ,       **lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       *lowerCamelCase   ,       **lowerCamelCase       ):



				'''simple docstring'''


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







	def 				_A			(	self   ,       *lowerCamelCase   ,       **lowerCamelCase       ):



				'''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() = }''')

