



from typing import List

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


if is_torch_available():
			import torch


class   lowerCamelCase__  (					snake_case_):



	"""simple docstring"""

	a__				:   Optional[int]   =     ["image_processor", "tokenizer"]
	a__				:   Dict   =     "OneFormerImageProcessor"
	a__				:   List[Any]   =     ("CLIPTokenizer", "CLIPTokenizerFast")

	def __init__(						self				:				str ,	__lowerCAmelCase				:				int=None ,	__lowerCAmelCase				:				str=None ,	__lowerCAmelCase				:				Union[str, Any] = 77 ,	__lowerCAmelCase				:				str = 77 ,	**__lowerCAmelCase				:				Tuple			)      ->  int:
				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`.'''			)

				_A						     =					max_seq_length
				_A						     =					task_seq_length

				super().__init__(__lowerCAmelCase ,	__lowerCAmelCase			)

	def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				int=None ,	__lowerCAmelCase				:				Union[str, Any]=77			)      ->  List[Any]:
				if text_list is None:
							raise ValueError('''tokens cannot be None.'''			)

				_A						     =					self.tokenizer(__lowerCAmelCase ,	padding='''max_length''' ,	max_length=__lowerCAmelCase ,	truncation=__lowerCAmelCase			)

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

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

				_A						     =					torch.cat(__lowerCAmelCase ,	dim=0			)
				return token_inputs

	def __call__(						self				:				Optional[Any] ,	__lowerCAmelCase				:				Optional[Any]=None ,	__lowerCAmelCase				:				Any=None ,	__lowerCAmelCase				:				Optional[int]=None ,	**__lowerCAmelCase				:				List[str]			)      ->  str:

				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.'''			)

				_A						     =					self.image_processor(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	**__lowerCAmelCase			)

				if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
							_A						     =					[task_inputs]

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

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

							_A						     =					torch.cat(__lowerCAmelCase ,	dim=0			)

				return encoded_inputs

	def 						snake_case_			(						self				:				Optional[Any] ,	__lowerCAmelCase				:				int=None ,	__lowerCAmelCase				:				Optional[Any]=None ,	__lowerCAmelCase				:				int=None ,	**__lowerCAmelCase				:				List[Any]			)      ->  str:

				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.'''			)

				_A						     =					self.image_processor.encode_inputs(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	**__lowerCAmelCase			)

				if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
							_A						     =					[task_inputs]

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

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

							_A						     =					torch.cat(__lowerCAmelCase ,	dim=0			)

				return encoded_inputs

	def 						snake_case_			(						self				:				str ,	*__lowerCAmelCase				:				List[Any] ,	**__lowerCAmelCase				:				Optional[Any]			)      ->  List[Any]:
				return self.image_processor.post_process_semantic_segmentation(*__lowerCAmelCase ,	**__lowerCAmelCase			)

	def 						snake_case_			(						self				:				Any ,	*__lowerCAmelCase				:				Dict ,	**__lowerCAmelCase				:				Optional[int]			)      ->  Tuple:
				return self.image_processor.post_process_instance_segmentation(*__lowerCAmelCase ,	**__lowerCAmelCase			)

	def 						snake_case_			(						self				:				int ,	*__lowerCAmelCase				:				Any ,	**__lowerCAmelCase				:				Optional[Any]			)      ->  str:
				return self.image_processor.post_process_panoptic_segmentation(*__lowerCAmelCase ,	**__lowerCAmelCase			)








import math
from decimal import Decimal, getcontext

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int = 4_000_000							)     ->    int:

   try:
      _A						     =					int(_snake_case							)
   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.'''							)
   _A						     =					100
   _A						     =					(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)

   _A						     =					(math.floor(math.log(n * (phi + 2)  ,      _snake_case							) - 1							) // 3) * 3 + 2
   _A						     =					Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
   _A						     =					num // 2
   return int(_snake_case							)


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



