"""simple docstring"""







from typing import List

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


if is_torch_available():
						import torch


class 			_lowercase		(   lowerCAmelCase__  ):
				lowerCamelCase__						=       ['image_processor', 'tokenizer']
				lowerCamelCase__						=       'OneFormerImageProcessor'
				lowerCamelCase__						=       ('CLIPTokenizer', 'CLIPTokenizerFast')

				def __init__(     self       :    int       ,    _lowercase       :    List[str]=None       ,    _lowercase       :    List[str]=None       ,    _lowercase       :    List[Any] = 7_7       ,    _lowercase       :    Tuple = 7_7       ,    **_lowercase       :    Tuple     ):


										'''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`."""     )

										UpperCAmelCase__						=		max_seq_length
										UpperCAmelCase__						=		task_seq_length

										super().__init__(_lowerCamelCase       ,    _lowerCamelCase     )

				def        A (     self       :    List[Any]       ,    _lowercase       :    List[Any]=None       ,    _lowercase       :    Dict=7_7     ):


										'''simple docstring'''

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

										UpperCAmelCase__						=		self.tokenizer(_lowerCamelCase       ,    padding="""max_length"""       ,    max_length=_lowerCamelCase       ,    truncation=_lowerCamelCase     )

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

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

										UpperCAmelCase__						=		torch.cat(_lowerCamelCase       ,    dim=0     )
										return token_inputs

				def __call__(     self       :    List[str]       ,    _lowercase       :    Dict=None       ,    _lowercase       :    Tuple=None       ,    _lowercase       :    Any=None       ,    **_lowercase       :    Optional[int]     ):


										'''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."""     )

										UpperCAmelCase__						=		self.image_processor(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    **_lowerCamelCase     )

										if isinstance(_lowerCamelCase       ,    _lowerCamelCase     ):
																UpperCAmelCase__						=		[task_inputs]

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

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

																UpperCAmelCase__						=		torch.cat(_lowerCamelCase       ,    dim=0     )

										return encoded_inputs

				def        A (     self       :    int       ,    _lowercase       :    Union[str, Any]=None       ,    _lowercase       :    Dict=None       ,    _lowercase       :    Any=None       ,    **_lowercase       :    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."""     )

										UpperCAmelCase__						=		self.image_processor.encode_inputs(_lowerCamelCase       ,    _lowerCamelCase       ,    _lowerCamelCase       ,    **_lowerCamelCase     )

										if isinstance(_lowerCamelCase       ,    _lowerCamelCase     ):
																UpperCAmelCase__						=		[task_inputs]

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

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

																UpperCAmelCase__						=		torch.cat(_lowerCamelCase       ,    dim=0     )

										return encoded_inputs

				def        A (     self       :    List[str]       ,    *_lowercase       :    Any       ,    **_lowercase       :    Dict     ):


										'''simple docstring'''
										return self.image_processor.post_process_semantic_segmentation(*_lowerCamelCase       ,    **_lowerCamelCase     )

				def        A (     self       :    Tuple       ,    *_lowercase       :    str       ,    **_lowercase       :    str     ):


										'''simple docstring'''
										return self.image_processor.post_process_instance_segmentation(*_lowerCamelCase       ,    **_lowerCamelCase     )

				def        A (     self       :    int       ,    *_lowercase       :    Any       ,    **_lowercase       :    List[Any]     ):


										'''simple docstring'''
										return self.image_processor.post_process_panoptic_segmentation(*_lowerCamelCase       ,    **_lowerCamelCase     )





"""simple docstring"""







from __future__ import annotations

import sys


def   a__      (		UpperCAmelCase__       :			int			)->	int:
						if n < 0:
												raise ValueError("""Negative arguments are not supported"""			)
						return _fib(UpperCAmelCase__			)[0]


def   a__      (		UpperCAmelCase__       :			int			)->	tuple[int, int]:
						if n == 0:  # (F(0), F(1))
												return (0, 1)

						# F(2n) = F(n)[2F(n+1) − F(n)]
						# F(2n+1) = F(n+1)^2+F(n)^2
						UpperCAmelCase__	,      UpperCAmelCase__						=		_fib(n // 2			)
						UpperCAmelCase__						=		a * (b * 2 - a)
						UpperCAmelCase__						=		a * a + b * b
						return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						UpperCamelCase_       :       int							   =		int(sys.argv[1])
						print(F"""fibonacci({n}) is {fibonacci(n)}""")

