


"""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  (snake_case__):





							'''simple docstring'''

							__lowercase:		List[str]				   =     ["""image_processor""", """tokenizer"""]
							__lowercase:		List[str]				   =     """OneFormerImageProcessor"""
							__lowercase:		Dict				   =     ("""CLIPTokenizer""", """CLIPTokenizerFast""")



							def __init__(					self				:   int		,							UpperCAmelCase_				:   Optional[Any]=None		,							UpperCAmelCase_				:   Optional[int]=None		,							UpperCAmelCase_				:   int = 77		,							UpperCAmelCase_				:   int = 77		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->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`."""							)

														snake_case_     							=		max_seq_length
														snake_case_     							=		task_seq_length

														super().__init__(UpperCAmelCase_		,							UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   str=None		,							UpperCAmelCase_				:   Dict=77							)       ->List[str]:



														"""simple docstring"""



														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				:   int		,							UpperCAmelCase_				:   str=None		,							UpperCAmelCase_				:   str=None		,							UpperCAmelCase_				:   int=None		,							**UpperCAmelCase_				:   Optional[Any]							)       ->Dict:



														"""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."""							)

														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 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   Tuple=None		,							UpperCAmelCase_				:   int=None		,							UpperCAmelCase_				:   List[str]=None		,							**UpperCAmelCase_				:   Union[str, Any]							)       ->Tuple:



														"""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."""							)

														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 		lowerCAmelCase							(					self				:   Optional[int]		,							*UpperCAmelCase_				:   List[str]		,							**UpperCAmelCase_				:   Any							)       ->Dict:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[Any]		,							*UpperCAmelCase_				:   Any		,							**UpperCAmelCase_				:   List[Any]							)       ->Union[str, Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]		,							*UpperCAmelCase_				:   Dict		,							**UpperCAmelCase_				:   List[Any]							)       ->Union[str, Any]:



														"""simple docstring"""



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







"""simple docstring"""
import math







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[int]:
							if not isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_SCREAMING_SNAKE_CASE					)
							if number < 1:
														snake_case_     							=		f"""Input value of [number={number}] must be a positive integer"""
														raise ValueError(_SCREAMING_SNAKE_CASE					)
							snake_case_     							=		[number]
							while number != 1:
														if number % 2 == 0:
																					snake_case_     							=		math.floor(math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														else:
																					snake_case_     							=		math.floor(
																					    math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					) * math.sqrt(_SCREAMING_SNAKE_CASE					)					)
														sequence.append(_SCREAMING_SNAKE_CASE					)
							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



