


"""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_  ( a_						):
      __magic_name__     :							Dict          =				["""image_processor""", """tokenizer"""]
      __magic_name__     :							Tuple          =				"""OneFormerImageProcessor"""
      __magic_name__     :							Optional[int]          =				("""CLIPTokenizer""", """CLIPTokenizerFast""")







      def __init__(      self :							int		,  _lowercase :							str=None		,  _lowercase :							Any=None		,  _lowercase :							int = 7_7		,  _lowercase :							int = 7_7		,  **_lowercase :							List[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__    :    Dict								=					max_seq_length
        lowerCAmelCase__    :    Optional[int]								=					task_seq_length

        super().__init__(_lowercase		,  _lowercase    )







      def 				_lowerCAmelCase       (      self :							Dict		,  _lowercase :							Union[str, Any]=None		,  _lowercase :							Tuple=7_7    ):
        if text_list is None:
          raise ValueError("tokens cannot be None."    )

        lowerCAmelCase__    :    str								=					self.tokenizer(_lowercase		,  padding="max_length"		,  max_length=_lowercase		,  truncation=_lowercase    )

        lowerCAmelCase__				,		lowerCAmelCase__    :    Optional[Any]								=					tokens["attention_mask"], tokens["input_ids"]

        lowerCAmelCase__    :    int								=					[]
        for attn_mask, input_id in zip(_lowercase		,  _lowercase    ):
          lowerCAmelCase__    :    Union[str, Any]								=					torch.tensor(_lowercase    ) * torch.tensor(_lowercase    )
          token_inputs.append(token.unsqueeze(0    )    )

        lowerCAmelCase__    :    List[str]								=					torch.cat(_lowercase		,  dim=0    )
        return token_inputs







      def __call__(      self :							List[Any]		,  _lowercase :							int=None		,  _lowercase :							Optional[int]=None		,  _lowercase :							Union[str, Any]=None		,  **_lowercase :							Optional[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__    :    Optional[int]								=					self.image_processor(_lowercase		,  _lowercase		,  _lowercase		,  **_lowercase    )

        if isinstance(_lowercase		,  _lowercase    ):
          lowerCAmelCase__    :    Optional[int]								=					[task_inputs]

        if isinstance(_lowercase		,  _lowercase    ) and all(isinstance(_lowercase		,  _lowercase    ) for task_input in task_inputs    ):
          lowerCAmelCase__    :    str								=					[]
          for task in task_inputs:
            lowerCAmelCase__    :    Dict								=					f"the task is {task}"
            task_token_inputs.append(_lowercase    )
          lowerCAmelCase__    :    List[Any]								=					self._preprocess_text(_lowercase		,  max_length=self.task_seq_length    )
        else:
          raise TypeError("Task Inputs should be a string or a list of strings."    )

        if hasattr(_lowercase		,  "text_inputs"    ):
          lowerCAmelCase__    :    Any								=					encoded_inputs.text_inputs

          lowerCAmelCase__    :    Dict								=					[]
          for texts in texts_list:
            lowerCAmelCase__    :    Optional[int]								=					self._preprocess_text(_lowercase		,  max_length=self.max_seq_length    )
            text_inputs.append(text_input_list.unsqueeze(0    )    )

          lowerCAmelCase__    :    str								=					torch.cat(_lowercase		,  dim=0    )

        return encoded_inputs







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

        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.encode_inputs(_lowercase		,  _lowercase		,  _lowercase		,  **_lowercase    )

        if isinstance(_lowercase		,  _lowercase    ):
          lowerCAmelCase__    :    List[str]								=					[task_inputs]

        if isinstance(_lowercase		,  _lowercase    ) and all(isinstance(_lowercase		,  _lowercase    ) for task_input in task_inputs    ):
          lowerCAmelCase__    :    Tuple								=					[]
          for task in task_inputs:
            lowerCAmelCase__    :    Optional[int]								=					f"the task is {task}"
            task_token_inputs.append(_lowercase    )
          lowerCAmelCase__    :    str								=					self._preprocess_text(_lowercase		,  max_length=self.task_seq_length    )
        else:
          raise TypeError("Task Inputs should be a string or a list of strings."    )

        if hasattr(_lowercase		,  "text_inputs"    ):
          lowerCAmelCase__    :    Tuple								=					encoded_inputs.text_inputs

          lowerCAmelCase__    :    Tuple								=					[]
          for texts in texts_list:
            lowerCAmelCase__    :    Optional[int]								=					self._preprocess_text(_lowercase		,  max_length=self.max_seq_length    )
            text_inputs.append(text_input_list.unsqueeze(0    )    )

          lowerCAmelCase__    :    Optional[int]								=					torch.cat(_lowercase		,  dim=0    )

        return encoded_inputs







      def 				_lowerCAmelCase       (      self :							Any		,  *_lowercase :							List[Any]		,  **_lowercase :							Any    ):
        return self.image_processor.post_process_semantic_segmentation(*_lowercase		,  **_lowercase    )







      def 				_lowerCAmelCase       (      self :							Any		,  *_lowercase :							List[str]		,  **_lowercase :							Any    ):
        return self.image_processor.post_process_instance_segmentation(*_lowercase		,  **_lowercase    )







      def 				_lowerCAmelCase       (      self :							Dict		,  *_lowercase :							int		,  **_lowercase :							Optional[Any]    ):
        return self.image_processor.post_process_panoptic_segmentation(*_lowercase		,  **_lowercase    )




"""simple docstring"""





def     lowercase__	(  lowerCamelCase     :  int				)					->     int:
  if not isinstance(lowerCamelCase				,	lowerCamelCase				):
    raise TypeError("recursive_lucas_number accepts only integer arguments."				)
  if n_th_number == 0:
    return 2
  if n_th_number == 1:
    return 1

  return recursive_lucas_number(n_th_number - 1				) + recursive_lucas_number(
      n_th_number - 2				)





def     lowercase__	(  lowerCamelCase     :  int				)					->     int:
  if not isinstance(lowerCamelCase				,	lowerCamelCase				):
    raise TypeError("dynamic_lucas_number accepts only integer arguments."				)
  lowerCAmelCase__				,		lowerCAmelCase__    :    str								=					2, 1
  for _ in range(lowerCamelCase				):
    lowerCAmelCase__				,		lowerCAmelCase__    :    str								=					b, a + b
  return a


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   __UpperCAmelCase    				=      int(input("Enter the number of terms in lucas series:\n").strip())
   print("Using recursive function to calculate lucas series:")
   print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
   print("\nUsing dynamic function to calculate lucas series:")
   print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))
