




'''simple docstring'''







from typing import List

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


if is_torch_available():
						import torch





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = ["""image_processor""", """tokenizer"""]
					lowercase__    = """OneFormerImageProcessor"""
					lowercase__    = ("""CLIPTokenizer""", """CLIPTokenizerFast""")






					def __init__(    self,  __UpperCamelCase=None,  __UpperCamelCase=None,  __UpperCamelCase = 77,  __UpperCamelCase = 77,  **__UpperCamelCase ):
							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`." )

							_snake_case	       =       max_seq_length
							_snake_case	       =       task_seq_length

							super().__init__(__UpperCamelCase,  __UpperCamelCase )






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

							_snake_case	       =       self.tokenizer(__UpperCamelCase,  padding="max_length",  max_length=__UpperCamelCase,  truncation=__UpperCamelCase )

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

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

							_snake_case	       =       torch.cat(__UpperCamelCase,  dim=0 )
							return token_inputs






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

							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." )

							_snake_case	       =       self.image_processor(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase )

							if isinstance(__UpperCamelCase,  __UpperCamelCase ):
									_snake_case	       =       [task_inputs]

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

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

									_snake_case	       =       torch.cat(__UpperCamelCase,  dim=0 )

							return encoded_inputs






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

							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." )

							_snake_case	       =       self.image_processor.encode_inputs(__UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  **__UpperCamelCase )

							if isinstance(__UpperCamelCase,  __UpperCamelCase ):
									_snake_case	       =       [task_inputs]

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

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

									_snake_case	       =       torch.cat(__UpperCamelCase,  dim=0 )

							return encoded_inputs






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






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






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







def 				A__    (  lowerCamelCase = 20  )     ->		int:

				try:
								UpperCamelCase_:							int		=  int(lowerCamelCase  )
				except (TypeError, ValueError):
								raise TypeError("""Parameter n must be int or castable to int."""  )
				if n <= 0:
								raise ValueError("""Parameter n must be greater than or equal to one."""  )
				UpperCamelCase_:							Any		=  0
				while 1:
								i += n * (n - 1)
								UpperCamelCase_:							List[Any]		=  0
								for j in range(2   ,			lowerCamelCase  ):
												if i % j != 0:
																UpperCamelCase_:							Union[str, Any]		=  1
																break
								if nfound == 0:
												if i == 0:
																UpperCamelCase_:							List[str]		=  1
												return i
				return None


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






