





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from ..image_utils import load_image


if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCAmelCase					:		Tuple		=							logging.get_logger(__name__)


UpperCAmelCase					:		List[str]		=							Dict[str, Any]
UpperCAmelCase					:		int		=							List[Prediction]





@add_end_docstrings(_UpperCAmelCase					)
class lowerCamelCase						(_UpperCAmelCase					):







  def __init__(		self     ,     *lowercase__     ,     **lowercase__		)		->						List[str]:






        """simple docstring"""





        super().__init__(*A_     ,     **A_		)

        if self.framework == "tf":
              raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''		)

        requires_backends(self     ,     '''vision'''		)
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)







  def 				UpperCAmelCase_				(		self     ,     **lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     {}
        if "threshold" in kwargs:
              _snake_case		:	List[Any]					     =     kwargs['''threshold''']
        return {}, {}, postprocess_kwargs







  def __call__(		self     ,     *lowercase__     ,     **lowercase__		)		->						Union[Predictions, List[Prediction]]:






        """simple docstring"""





        return super().__call__(*A_     ,     **A_		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     load_image(A_		)
        _snake_case		:	str					     =     torch.IntTensor([[image.height, image.width]]		)
        _snake_case		:	str					     =     self.image_processor(images=[image]     ,     return_tensors='''pt'''		)
        if self.tokenizer is not None:
              _snake_case		:	Optional[int]					     =     self.tokenizer(text=inputs['''words''']     ,     boxes=inputs['''boxes''']     ,     return_tensors='''pt'''		)
        _snake_case		:	str					     =     target_size
        return inputs







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Tuple					     =     model_inputs.pop('''target_size'''		)
        _snake_case		:	Union[str, Any]					     =     self.model(**A_		)
        _snake_case		:	Optional[int]					     =     outputs.__class__({'''target_size''': target_size, **outputs}		)
        if self.tokenizer is not None:
              _snake_case		:	Tuple					     =     model_inputs['''bbox''']
        return model_outputs







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__=0.9		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     model_outputs['''target_size''']
        if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              _snake_case     ,							_snake_case		:	Optional[int]					     =     target_size[0].tolist()

              def unnormalize(lowercase__		):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1_000),
                                (height * bbox[1] / 1_000),
                                (width * bbox[2] / 1_000),
                                (height * bbox[3] / 1_000),
                            ]		)		)

              _snake_case     ,							_snake_case		:	Union[str, Any]					     =     model_outputs['''logits'''].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
              _snake_case		:	Any					     =     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              _snake_case		:	Optional[Any]					     =     [unnormalize(A_		) for bbox in model_outputs['''bbox'''].squeeze(0		)]
              _snake_case		:	Dict					     =     ['''score''', '''label''', '''box''']
              _snake_case		:	Union[str, Any]					     =     [dict(zip(A_     ,     A_		)		) for vals in zip(scores.tolist()     ,     A_     ,     A_		) if vals[0] > threshold]
        else:
              # This is a regular ForObjectDetectionModel
              _snake_case		:	Union[str, Any]					     =     self.image_processor.post_process_object_detection(A_     ,     A_     ,     A_		)
              _snake_case		:	List[str]					     =     raw_annotations[0]
              _snake_case		:	List[str]					     =     raw_annotation['''scores''']
              _snake_case		:	Optional[int]					     =     raw_annotation['''labels''']
              _snake_case		:	int					     =     raw_annotation['''boxes''']

              _snake_case		:	Tuple					     =     scores.tolist()
              _snake_case		:	List[str]					     =     [self.model.config.idalabel[label.item()] for label in labels]
              _snake_case		:	Union[str, Any]					     =     [self._get_bounding_box(A_		) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              _snake_case		:	List[Any]					     =     ['''score''', '''label''', '''box''']
              _snake_case		:	Dict					     =     [
                  dict(zip(A_     ,     A_		)		)
                  for vals in zip(raw_annotation['''scores''']     ,     raw_annotation['''labels''']     ,     raw_annotation['''boxes''']		)
              ]

        return annotation







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Dict[str, int]:






        """simple docstring"""





        if self.framework != "pt":
              raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''		)
        _snake_case     ,							_snake_case     ,							_snake_case     ,							_snake_case		:	int					     =     box.int().tolist()
        _snake_case		:	int					     =     {
            '''xmin''': xmin,
            '''ymin''': ymin,
            '''xmax''': xmax,
            '''ymax''': ymax,
        }
        return bbox









'''simple docstring'''

from collections.abc import Generator




def    _a    (							):





      """simple docstring"""



      _snake_case     ,							_snake_case		:	Union[str, Any]					     =     0, 1
      while True:
            _snake_case     ,							_snake_case		:	List[str]					     =     b, a + b
            yield b




def    _a    (							lowerCAmelCase_ = 1_000	):





      """simple docstring"""



      _snake_case		:	List[str]					     =     1
      _snake_case		:	Dict					     =     fibonacci_generator()
      while len(str(next(lowerCAmelCase_	)	)	) < n:
            answer += 1
      return answer + 1


if __name__ == "__main__":
   print(solution(int(str(input()).strip())))


