
"""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

A__      :	List[Any]          =    logging.get_logger(__name__)


A__      :	Any          =    Dict[str, Any]
A__      :	Optional[Any]          =    List[Prediction]







@add_end_docstrings(lowerCAmelCase_				)
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Optional[Any]:
          super().__init__(*__UpperCamelCase ,			**__UpperCamelCase  )

          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  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Any 							=						{}
          if "threshold" in kwargs:
               UpperCAmelCase__							: Any 							=						kwargs["threshold"]
          return {}, {}, postprocess_kwargs



     def __call__(      self ,			*__UpperCamelCase ,			**__UpperCamelCase  )->					Union[Predictions, List[Prediction]]:
          return super().__call__(*__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: Any 							=						load_image(__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						torch.IntTensor([[image.height, image.width]]  )
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processor(images=[image] ,			return_tensors="pt"  )
          if self.tokenizer is not None:
               UpperCAmelCase__							: Dict 							=						self.tokenizer(text=inputs["words"] ,			boxes=inputs["boxes"] ,			return_tensors="pt"  )
          UpperCAmelCase__							: int 							=						target_size
          return inputs



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: List[str] 							=						model_inputs.pop("target_size"  )
          UpperCAmelCase__							: Union[str, Any] 							=						self.model(**__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						outputs.__class__({"target_size": target_size, **outputs}  )
          if self.tokenizer is not None:
               UpperCAmelCase__							: Optional[int] 							=						model_inputs["bbox"]
          return model_outputs



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=0.9  )->					str:
          UpperCAmelCase__							: Optional[Any] 							=						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__							: Any 							=						target_size[0].tolist()

               def unnormalize(__UpperCamelCase  ):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 10_00),
                                (height * bbox[1] / 10_00),
                                (width * bbox[2] / 10_00),
                                (height * bbox[3] / 10_00),
                            ]  )  )

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

               UpperCAmelCase__							: Union[str, Any] 							=						scores.tolist()
               UpperCAmelCase__							: Dict 							=						[self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__							: Optional[Any] 							=						[self._get_bounding_box(__UpperCamelCase  ) for box in boxes]

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

          return annotation



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict[str, int]:
          if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."  )
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						box.int().tolist()
          UpperCAmelCase__							: str 							=						{
              "xmin": xmin,
              "ymin": ymin,
              "xmax": xmax,
              "ymax": ymax,
          }
          return bbox





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(a - b		) for a, b in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)



def 							a__ (				lowerCAmelCase	:       list[float]		):






     '''simple docstring'''
     if point:
          if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
               for item in point:
                    if not isinstance(lowerCAmelCase       ,    (int, float)		):
                         UpperCAmelCase__							: Tuple 							=						(
                             "Expected a list of numbers as input, found "
                             F"{type(lowerCAmelCase		).__name__}"
                         )
                         raise TypeError(lowerCAmelCase		)
          else:
               UpperCAmelCase__							: Dict 							=						F"Expected a list of numbers as input, found {type(lowerCAmelCase		).__name__}"
               raise TypeError(lowerCAmelCase		)
     else:
          raise ValueError("Missing an input"		)



def 							a__ (				lowerCAmelCase	:       list       ,    lowerCAmelCase	:       list		):






     '''simple docstring'''
     _validate_point(lowerCAmelCase		)
     _validate_point(lowerCAmelCase		)
     if len(lowerCAmelCase		) != len(lowerCAmelCase		):
          raise ValueError("Both points must be in the same n-dimensional space"		)

     return float(sum(abs(x - y		) for x, y in zip(lowerCAmelCase       ,    lowerCAmelCase		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



