



"""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__  (      lowercase_    ):




      """simple docstring"""




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



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

        lowerCAmelCase_  :							Union[str, Any]								=					max_seq_length
        lowerCAmelCase_  :							List[Any]								=					task_seq_length

        super().__init__(UpperCAmelCase__       ,     UpperCAmelCase__			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=None       ,     SCREAMING_SNAKE_CASE_							: int=7_7			):
        if text_list is None:
          raise ValueError('tokens cannot be None.'			)

        lowerCAmelCase_  :							Union[str, Any]								=					self.tokenizer(UpperCAmelCase__       ,     padding='max_length'       ,     max_length=UpperCAmelCase__       ,     truncation=UpperCAmelCase__			)

        lowerCAmelCase_		,lowerCAmelCase_  :							Optional[Any]								=					tokens['attention_mask'], tokens['input_ids']

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

        lowerCAmelCase_  :							Any								=					torch.cat(UpperCAmelCase__       ,     dim=0			)
        return token_inputs



      def __call__( self							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Any=None       ,     SCREAMING_SNAKE_CASE_							: List[Any]=None       ,     SCREAMING_SNAKE_CASE_							: int=None       ,     **SCREAMING_SNAKE_CASE_							: 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.'			)

        lowerCAmelCase_  :							List[Any]								=					self.image_processor(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__       ,     **UpperCAmelCase__			)

        if isinstance(UpperCAmelCase__       ,     UpperCAmelCase__			):
          lowerCAmelCase_  :							List[Any]								=					[task_inputs]

        if isinstance(UpperCAmelCase__       ,     UpperCAmelCase__			) and all(isinstance(UpperCAmelCase__       ,     UpperCAmelCase__			) for task_input in task_inputs			):
          lowerCAmelCase_  :							Any								=					[]
          for task in task_inputs:
            lowerCAmelCase_  :							Any								=					F"the task is {task}"
            task_token_inputs.append(UpperCAmelCase__			)
          lowerCAmelCase_  :							List[Any]								=					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'			):
          lowerCAmelCase_  :							Optional[int]								=					encoded_inputs.text_inputs

          lowerCAmelCase_  :							List[str]								=					[]
          for texts in texts_list:
            lowerCAmelCase_  :							Optional[int]								=					self._preprocess_text(UpperCAmelCase__       ,     max_length=self.max_seq_length			)
            text_inputs.append(text_input_list.unsqueeze(0			)			)

          lowerCAmelCase_  :							Dict								=					torch.cat(UpperCAmelCase__       ,     dim=0			)

        return encoded_inputs



      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Tuple=None       ,     SCREAMING_SNAKE_CASE_							: Optional[Any]=None       ,     SCREAMING_SNAKE_CASE_							: str=None       ,     **SCREAMING_SNAKE_CASE_							: Union[str, Any]			):

        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.'			)

        lowerCAmelCase_  :							Union[str, Any]								=					self.image_processor.encode_inputs(UpperCAmelCase__       ,     UpperCAmelCase__       ,     UpperCAmelCase__       ,     **UpperCAmelCase__			)

        if isinstance(UpperCAmelCase__       ,     UpperCAmelCase__			):
          lowerCAmelCase_  :							Dict								=					[task_inputs]

        if isinstance(UpperCAmelCase__       ,     UpperCAmelCase__			) and all(isinstance(UpperCAmelCase__       ,     UpperCAmelCase__			) for task_input in task_inputs			):
          lowerCAmelCase_  :							Union[str, Any]								=					[]
          for task in task_inputs:
            lowerCAmelCase_  :							Any								=					F"the task is {task}"
            task_token_inputs.append(UpperCAmelCase__			)
          lowerCAmelCase_  :							int								=					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'			):
          lowerCAmelCase_  :							Tuple								=					encoded_inputs.text_inputs

          lowerCAmelCase_  :							Union[str, Any]								=					[]
          for texts in texts_list:
            lowerCAmelCase_  :							int								=					self._preprocess_text(UpperCAmelCase__       ,     max_length=self.max_seq_length			)
            text_inputs.append(text_input_list.unsqueeze(0			)			)

          lowerCAmelCase_  :							int								=					torch.cat(UpperCAmelCase__       ,     dim=0			)

        return encoded_inputs



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]       ,     *SCREAMING_SNAKE_CASE_							: List[str]       ,     **SCREAMING_SNAKE_CASE_							: Union[str, Any]			):
        return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase__       ,     **UpperCAmelCase__			)



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     *SCREAMING_SNAKE_CASE_							: Tuple       ,     **SCREAMING_SNAKE_CASE_							: str			):
        return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase__       ,     **UpperCAmelCase__			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]       ,     *SCREAMING_SNAKE_CASE_							: Tuple       ,     **SCREAMING_SNAKE_CASE_							: Any			):
        return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase__       ,     **UpperCAmelCase__			)








"""simple docstring"""





import math
from decimal import Decimal, getcontext




def     _A    (    UpperCamelCase_	:			int = 4000000)      ->		int:
   '''simple docstring'''


   try:
      __lowercase								=		int(UpperCamelCase_)
   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.")
   __lowercase								=		100
   __lowercase								=		(Decimal(5) ** Decimal(0.5) + 1) / Decimal(2)

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


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




