
"""simple docstring"""

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     :    List[str]       =			logging.get_logger(__name__)


UpperCamelCase     :    Union[str, Any]       =			Dict[str, Any]
UpperCamelCase     :    Tuple       =			List[Prediction]

@add_end_docstrings(__lowercase      )
class __lowerCAmelCase		(				__lowercase      ):






     """simple docstring"""




     def __init__(     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''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     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	{}
      if "threshold" in kwargs:
       __UpperCamelCase      =	kwargs['threshold']
      return {}, {}, postprocess_kwargs





     def __call__(     self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




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





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	load_image(_a				)
      __UpperCamelCase      =	torch.IntTensor([[image.height, image.width]]				)
      __UpperCamelCase      =	self.image_processor(images=[image]     ,					return_tensors='pt'				)
      if self.tokenizer is not None:
       __UpperCamelCase      =	self.tokenizer(text=inputs['words']     ,					boxes=inputs['boxes']     ,					return_tensors='pt'				)
      __UpperCamelCase      =	target_size
      return inputs





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	model_inputs.pop('target_size'				)
      __UpperCamelCase      =	self.model(**_a				)
      __UpperCamelCase      =	outputs.__class__({'target_size': target_size, **outputs}				)
      if self.tokenizer is not None:
       __UpperCamelCase      =	model_inputs['bbox']
      return model_outputs





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0.9				):

      '''simple docstring'''




      __UpperCamelCase      =	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.
       __UpperCamelCase  ,     __UpperCamelCase      =	target_size[0].tolist()

       def unnormalize(__UpperCAmelCase				):
        return self._get_bounding_box(
            torch.Tensor(
                [
                    (width * bbox[0] / 1000),
                    (height * bbox[1] / 1000),
                    (width * bbox[2] / 1000),
                    (height * bbox[3] / 1000),
                ]				)				)

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

       __UpperCamelCase      =	scores.tolist()
       __UpperCamelCase      =	[self.model.config.idalabel[label.item()] for label in labels]
       __UpperCamelCase      =	[self._get_bounding_box(_a				) for box in boxes]

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

      return annotation







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




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





"""simple docstring"""

def 							A						(    snake_case				:int		)				->     bool:
 return sum(i for i in range(1    , number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("Program to check whether a number is a Perfect number or not...")
      UpperCamelCase     :    Union[str, Any]       =			int(input("Enter number: ").strip())
      print(f'''{number} is {"" if perfect(number) else "not "}a Perfect Number.''')



