




from typing import List

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


if is_torch_available():
   import torch





class  lowerCamelCase_							(   __lowerCamelCase      ):


 '''simple docstring'''







 lowercase_       					=					['image_processor', 'tokenizer']
 lowercase_       					=					'OneFormerImageProcessor'
 lowercase_       					=					('CLIPTokenizer', 'CLIPTokenizerFast')



 def __init__(							self						:	int       ,       _lowerCAmelCase						:	Optional[int]=None       ,       _lowerCAmelCase						:	List[str]=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`.'		)

       SCREAMING_SNAKE_CASE_					=    max_seq_length
       SCREAMING_SNAKE_CASE_					=    task_seq_length

       super().__init__(_lowerCAmelCase       ,       _lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	List[Any]       ,       _lowerCAmelCase						:	Union[str, Any]=None       ,       _lowerCAmelCase						:	Any=77		):






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

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

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

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

       SCREAMING_SNAKE_CASE_					=    torch.cat(_lowerCAmelCase       ,       dim=0		)
       return token_inputs



 def __call__(							self						:	int       ,       _lowerCAmelCase						:	Optional[int]=None       ,       _lowerCAmelCase						:	Optional[int]=None       ,       _lowerCAmelCase						:	Tuple=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.'		)

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

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

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

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

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

       return encoded_inputs



 def    lowerCAmelCase_						(							self						:	List[str]       ,       _lowerCAmelCase						:	Optional[int]=None       ,       _lowerCAmelCase						:	Tuple=None       ,       _lowerCAmelCase						:	Any=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.'		)

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

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

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

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

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

       return encoded_inputs



 def    lowerCAmelCase_						(							self						:	List[str]       ,       *_lowerCAmelCase						:	Any       ,       **_lowerCAmelCase						:	Optional[Any]		):
       return self.image_processor.post_process_semantic_segmentation(*_lowerCAmelCase       ,       **_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Optional[Any]       ,       *_lowerCAmelCase						:	Union[str, Any]       ,       **_lowerCAmelCase						:	List[str]		):
       return self.image_processor.post_process_instance_segmentation(*_lowerCAmelCase       ,       **_lowerCAmelCase		)




 def    lowerCAmelCase_						(							self						:	List[str]       ,       *_lowerCAmelCase						:	int       ,       **_lowerCAmelCase						:	List[str]		):
       return self.image_processor.post_process_panoptic_segmentation(*_lowerCAmelCase       ,       **_lowerCAmelCase		)





from collections import defaultdict

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int = 5							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    defaultdict(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    0

      while True:
            SCREAMING_SNAKE_CASE_					=    get_digits(__UpperCAmelCase							)
            freqs[digits].append(__UpperCAmelCase							)

            if len(freqs[digits]							) == max_base:
                  SCREAMING_SNAKE_CASE_					=    freqs[digits][0] ** 3
                  return base

            num += 1

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int							)      ->				str:
      return "".join(sorted(str(num**3							)							)							)


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