


'''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__  :					Optional[int]        =       logging.get_logger(__name__)


a__  :					Any        =       Dict[str, Any]
a__  :					List[Any]        =       List[Prediction]
@add_end_docstrings(a__   )
class 					lowercase_     (    a__   ):


    def __init__(     self  ,					*a  ,					**a    ):
          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    __a							(     self  ,					**a    ):
          UpperCamelCase__										=				{}
          if "threshold" in kwargs:
                UpperCamelCase__										=				kwargs["threshold"]
          return {}, {}, postprocess_kwargs


    def __call__(     self  ,					*a  ,					**a    ):
          return super().__call__(*a  ,					**a    )


    def    __a							(     self  ,					a    ):
          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    __a							(     self  ,					a    ):
          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    __a							(     self  ,					a  ,					a=0.9    ):
          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(a    ):
                      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__										=				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    __a							(     self  ,					a    ):
          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 __lowercase			(							_a		,			_a		,			_a=False    ):

    if isinstance(_a		,			_a    ) and isinstance(_a		,			_a    ):
        snake_case_ :     Union[str, Any]				=	len(set_a.intersection(_a    )    )

        if alternative_union:
            snake_case_ :     Any				=	len(_a    ) + len(_a    )
        else:
            snake_case_ :     str				=	len(set_a.union(_a    )    )

        return intersection / union

    if isinstance(_a		,			(list, tuple)    ) and isinstance(_a		,			(list, tuple)    ):
        snake_case_ :     str				=	[element for element in set_a if element in set_b]

        if alternative_union:
            snake_case_ :     Tuple				=	len(_a    ) + len(_a    )
            return len(_a    ) / union
        else:
            snake_case_ :     List[Any]				=	set_a + [element for element in set_b if element not in set_a]
            return len(_a    ) / len(_a    )

        return len(_a    ) / len(_a    )
    return None


if __name__ == "__main__":
     lowercase__    :  Any							    =	{'''a''', '''b''', '''c''', '''d''', '''e'''}
     lowercase__    :  Optional[Any]							    =	{'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
     print(jaccard_similarity(set_a, set_b))





