
'''simple docstring'''

from typing import List

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


if is_torch_available():
   import torch


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




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






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE = 77    ,__SCREAMING_SNAKE_CASE = 77    ,**__SCREAMING_SNAKE_CASE      ):
    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  :  Optional[Any]								=		max_seq_length
    SCREAMING_SNAKE_CASE  :  Any								=		task_seq_length

    super().__init__(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=77      ):
    if text_list is None:
     raise ValueError('tokens cannot be None.'      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		self.tokenizer(__SCREAMING_SNAKE_CASE    ,padding='max_length'    ,max_length=__SCREAMING_SNAKE_CASE    ,truncation=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  str								=		tokens['attention_mask'], tokens['input_ids']

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[]
    for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Dict								=		torch.tensor(__SCREAMING_SNAKE_CASE      ) * torch.tensor(__SCREAMING_SNAKE_CASE      )
     token_inputs.append(token.unsqueeze(0      )      )

    SCREAMING_SNAKE_CASE  :  int								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )
    return token_inputs






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

    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  :  Dict								=		self.image_processor(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )

    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		[task_inputs]

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

    if hasattr(__SCREAMING_SNAKE_CASE    ,'text_inputs'      ):
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		encoded_inputs.text_inputs

     SCREAMING_SNAKE_CASE  :  Tuple								=		[]
     for texts in texts_list:
      SCREAMING_SNAKE_CASE  :  Optional[int]								=		self._preprocess_text(__SCREAMING_SNAKE_CASE    ,max_length=self.max_seq_length      )
      text_inputs.append(text_input_list.unsqueeze(0      )      )

     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )

    return encoded_inputs






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,**__SCREAMING_SNAKE_CASE      ):

    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  :  Optional[int]								=		self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )

    if isinstance(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  List[Any]								=		[task_inputs]

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

    if hasattr(__SCREAMING_SNAKE_CASE    ,'text_inputs'      ):
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encoded_inputs.text_inputs

     SCREAMING_SNAKE_CASE  :  List[str]								=		[]
     for texts in texts_list:
      SCREAMING_SNAKE_CASE  :  Dict								=		self._preprocess_text(__SCREAMING_SNAKE_CASE    ,max_length=self.max_seq_length      )
      text_inputs.append(text_input_list.unsqueeze(0      )      )

     SCREAMING_SNAKE_CASE  :  List[Any]								=		torch.cat(__SCREAMING_SNAKE_CASE    ,dim=0      )

    return encoded_inputs






   def 					__a			(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )




'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	int:
 if number != int(snake_case_							):
  raise ValueError('factorial() only accepts integral values'							)
 if number < 0:
  raise ValueError('factorial() not defined for negative values'							)
 SCREAMING_SNAKE_CASE  :  str								=		1
 for i in range(1   ,     number + 1							):
  value *= i
 return value



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int							)       ->	int:
 if not isinstance(snake_case_   ,     snake_case_							):
  raise ValueError('factorial() only accepts integral values'							)
 if n < 0:
  raise ValueError('factorial() not defined for negative values'							)
 return 1 if n == 0 or n == 1 else n * factorial(n - 1							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __UpperCAmelCase												=   int(input('Enter a positive integer: ').strip() or 0)
   print(f"""factorial{n} is {factorial(n)}""")


