






"""simple docstring"""
from typing import List

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


if is_torch_available():
	import torch



class 					UpperCamelCase__   (							snake_case_):







				"""simple docstring"""
				__UpperCAmelCase											=   ["""image_processor""", """tokenizer"""]
				__UpperCAmelCase											=   """OneFormerImageProcessor"""
				__UpperCAmelCase											=   ("""CLIPTokenizer""", """CLIPTokenizerFast""")







				def __init__(		self   :  Any							,						UpperCamelCase_   :  int=None							,						UpperCamelCase_   :  List[str]=None							,						UpperCamelCase_   :  Dict = 7_7							,						UpperCamelCase_   :  Optional[int] = 7_7							,						**UpperCamelCase_   :  Optional[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`.'   )

											__magic_name__										=						max_seq_length
											__magic_name__										=						task_seq_length

											super().__init__(UpperCamelCase_							,						UpperCamelCase_   )







				def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  str=None							,						UpperCamelCase_   :  Any=7_7   ):





											'''simple docstring'''

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

											__magic_name__										=						self.tokenizer(UpperCamelCase_							,						padding='max_length'							,						max_length=UpperCamelCase_							,						truncation=UpperCamelCase_   )

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

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

											__magic_name__										=						torch.cat(UpperCamelCase_							,						dim=0   )
											return token_inputs







				def __call__(		self   :  str							,						UpperCamelCase_   :  Any=None							,						UpperCamelCase_   :  Tuple=None							,						UpperCamelCase_   :  Dict=None							,						**UpperCamelCase_   :  List[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.'   )

											__magic_name__										=						self.image_processor(UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						**UpperCamelCase_   )

											if isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
																		__magic_name__										=						[task_inputs]

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

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

																		__magic_name__										=						torch.cat(UpperCamelCase_							,						dim=0   )

											return encoded_inputs







				def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  str=None							,						UpperCamelCase_   :  Optional[Any]=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.'   )

											__magic_name__										=						self.image_processor.encode_inputs(UpperCamelCase_							,						UpperCamelCase_							,						UpperCamelCase_							,						**UpperCamelCase_   )

											if isinstance(UpperCamelCase_							,						UpperCamelCase_   ):
																		__magic_name__										=						[task_inputs]

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

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

																		__magic_name__										=						torch.cat(UpperCamelCase_							,						dim=0   )

											return encoded_inputs







				def 							a__ (		self   :  Union[str, Any]							,						*UpperCamelCase_   :  str							,						**UpperCamelCase_   :  List[Any]   ):





											'''simple docstring'''

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







				def 							a__ (		self   :  List[Any]							,						*UpperCamelCase_   :  int							,						**UpperCamelCase_   :  List[Any]   ):





											'''simple docstring'''

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







				def 							a__ (		self   :  Optional[Any]							,						*UpperCamelCase_   :  Dict							,						**UpperCamelCase_   :  int   ):





											'''simple docstring'''

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







"""simple docstring"""
def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:

       """simple docstring"""





       sequence.append(__snake_case     )

       __magic_name__										=						0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__snake_case     ):
              return None

       return index


if __name__ == "__main__":
 snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
 snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]

 snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
 snake_case   :   str    		=		int(target_input)
 snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print("""Not found""")