






from typing import List

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


if is_torch_available():
 import torch
class        __snake_case(					__UpperCamelCase      ):
 lowercase :       List[str]		      =		['''image_processor''', '''tokenizer''']
 lowercase :       Any		      =		'''OneFormerImageProcessor'''
 lowercase :       Union[str, Any]		      =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')



 def __init__(	self   :   Dict ,		UpperCAmelCase__   :   Any=None ,		UpperCAmelCase__   :   Union[str, Any]=None ,		UpperCAmelCase__   :   int = 77 ,		UpperCAmelCase__   :   int = 77 ,		**UpperCAmelCase__   :   List[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`."""	)

    lowercase__						:	Optional[Any]         = max_seq_length
    lowercase__						:	Tuple         = task_seq_length

    super().__init__(UpperCAmelCase__ ,		UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   str=None ,		UpperCAmelCase__   :   List[Any]=77	):

    """simple docstring"""






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

    lowercase__						:	List[Any]         = self.tokenizer(UpperCAmelCase__ ,		padding="""max_length""" ,		max_length=UpperCAmelCase__ ,		truncation=UpperCAmelCase__	)

    lowercase__     ,					lowercase__						:	Union[str, Any]         = tokens["""attention_mask"""], tokens["""input_ids"""]

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

    lowercase__						:	Tuple         = torch.cat(UpperCAmelCase__ ,		dim=0	)
    return token_inputs



 def __call__(	self   :   Optional[int] ,		UpperCAmelCase__   :   List[Any]=None ,		UpperCAmelCase__   :   Tuple=None ,		UpperCAmelCase__   :   List[Any]=None ,		**UpperCAmelCase__   :   List[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."""	)

    lowercase__						:	List[Any]         = self.image_processor(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		**UpperCAmelCase__	)

    if isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	):
       lowercase__						:	Optional[int]         = [task_inputs]

    if isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	) and all(isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	) for task_input in task_inputs	):
       lowercase__						:	List[Any]         = []
       for task in task_inputs:
          lowercase__						:	str         = f'''the task is {task}'''
          task_token_inputs.append(UpperCAmelCase__	)
       lowercase__						:	Union[str, 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"""	):
       lowercase__						:	str         = encoded_inputs.text_inputs

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

       lowercase__						:	Any         = torch.cat(UpperCAmelCase__ ,		dim=0	)

    return encoded_inputs



 def 						UpperCAmelCase    (	self   :   Tuple ,		UpperCAmelCase__   :   Optional[int]=None ,		UpperCAmelCase__   :   Optional[Any]=None ,		UpperCAmelCase__   :   Dict=None ,		**UpperCAmelCase__   :   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."""	)

    lowercase__						:	List[Any]         = self.image_processor.encode_inputs(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		**UpperCAmelCase__	)

    if isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	):
       lowercase__						:	Dict         = [task_inputs]

    if isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	) and all(isinstance(UpperCAmelCase__ ,		UpperCAmelCase__	) for task_input in task_inputs	):
       lowercase__						:	Union[str, Any]         = []
       for task in task_inputs:
          lowercase__						:	Optional[int]         = f'''the task is {task}'''
          task_token_inputs.append(UpperCAmelCase__	)
       lowercase__						:	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"""	):
       lowercase__						:	Any         = encoded_inputs.text_inputs

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

       lowercase__						:	Union[str, Any]         = torch.cat(UpperCAmelCase__ ,		dim=0	)

    return encoded_inputs



 def 						UpperCAmelCase    (	self   :   int ,		*UpperCAmelCase__   :   Optional[int] ,		**UpperCAmelCase__   :   Any	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[str] ,		*UpperCAmelCase__   :   Union[str, Any] ,		**UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






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







 def 						UpperCAmelCase    (	self   :   List[str] ,		*UpperCAmelCase__   :   Dict ,		**UpperCAmelCase__   :   Union[str, Any]	):

    """simple docstring"""






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










a    =       10**8
def       a__						(  A					):
   if n % 10 == 0:
      return False
   lowercase__						:	Optional[Any]         = str(A					)
   return s == s[::-1]
def       a__						(  ):
   lowercase__						:	Union[str, Any]         = set()
   lowercase__						:	Dict         = 1
   lowercase__						:	List[str]         = 5
   while sum_squares < LIMIT:
      lowercase__						:	Tuple         = first_square + 1
      while sum_squares < LIMIT:
         if is_palindrome(A					):
            answer.add(A					)
         last_square += 1
         sum_squares += last_square**2
      first_square += 1
      lowercase__						:	Any         = first_square**2 + (first_square + 1) ** 2

   return sum(A					)


if __name__ == "__main__":
 print(solution())


