


"""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

__snake_case   			=    logging.get_logger(__name__)


__snake_case   			=    Dict[str, Any]
__snake_case   			=    List[Prediction]







@add_end_docstrings(snake_case_	)
class _lowerCAmelCase				(      snake_case_	):







   def __init__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       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__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    {}
       if "threshold" in kwargs:
           snake_case    :      int      					=    kwargs["threshold"]
       return {}, {}, postprocess_kwargs







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

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    load_image(UpperCamelCase__						)
       snake_case    :      Any      					=    torch.IntTensor([[image.height, image.width]]						)
       snake_case    :      List[str]      					=    self.image_processor(images=[image]						,	return_tensors="pt"						)
       if self.tokenizer is not None:
           snake_case    :      Union[str, Any]      					=    self.tokenizer(text=inputs["words"]						,	boxes=inputs["boxes"]						,	return_tensors="pt"						)
       snake_case    :      Optional[Any]      					=    target_size
       return inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[str]      					=    model_inputs.pop("target_size"						)
       snake_case    :      List[Any]      					=    self.model(**UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    outputs.__class__({"target_size": target_size, **outputs}						)
       if self.tokenizer is not None:
           snake_case    :      List[str]      					=    model_inputs["bbox"]
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=0.9						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      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    :      List[Any]      					=    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),
                       ]						)						)

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

           snake_case    :      str      					=    scores.tolist()
           snake_case    :      Dict      					=    [self.model.config.idalabel[label.item()] for label in labels]
           snake_case    :      Optional[int]      					=    [self._get_bounding_box(UpperCamelCase__						) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           snake_case    :      Union[str, Any]      					=    ["score", "label", "box"]
           snake_case    :      Tuple      					=    [
               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]:

       '''simple docstring'''




       if self.framework != "pt":
           raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."						)
       snake_case					,snake_case					,snake_case					,snake_case    :      Optional[Any]      					=    box.int().tolist()
       snake_case    :      List[str]      					=    {
           "xmin": xmin,
           "ymin": ymin,
           "xmax": xmax,
           "ymax": ymax,
       }
       return bbox









"""simple docstring"""



def   __lowerCAmelCase						(						)	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      Dict      					=    []
    snake_case    :      List[Any]      					=    1

    while len(lowercase ) < 1e6:
        constant.append(str(lowercase ) )
        i += 1

    snake_case    :      Tuple      					=    "".join(lowercase )

    return (
        int(constant[0] )
        * int(constant[9] )
        * int(constant[99] )
        * int(constant[999] )
        * int(constant[9999] )
        * int(constant[9_9999] )
        * int(constant[99_9999] )
    )


if __name__ == "__main__":
  print(solution())





