


"""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

lowerCamelCase_       :       Any      						=  logging.get_logger(__name__)


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






@add_end_docstrings(_SCREAMING_SNAKE_CASE							)
class 				__A    (	_SCREAMING_SNAKE_CASE							):






 """simple docstring"""




 def __init__(		self			,   *__A			,   **__A						) ->					Any:
     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 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     a					={}
     if "threshold" in kwargs:
         a					=kwargs['''threshold''']
     return {}, {}, postprocess_kwargs


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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     a					=load_image(__a						)
     a					=torch.IntTensor([[image.height, image.width]]						)
     a					=self.image_processor(images=[image]			,   return_tensors='''pt'''						)
     if self.tokenizer is not None:
         a					=self.tokenizer(text=inputs['''words''']			,   boxes=inputs['''boxes''']			,   return_tensors='''pt'''						)
     a					=target_size
     return inputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
     a					=model_inputs.pop('''target_size'''						)
     a					=self.model(**__a						)
     a					=outputs.__class__({'''target_size''': target_size, **outputs}						)
     if self.tokenizer is not None:
         a					=model_inputs['''bbox''']
     return model_outputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0.9						) ->					Dict:
     a					=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.
         a		,				a					=target_size[0].tolist()

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

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

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

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

     return annotation





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



"""simple docstring"""







def _A						(						lowercase = 2_00_00_00		):







    """simple docstring"""
    a					=[0 for i in range(n + 1		)]
    a					=1
    a					=1

    for i in range(2	,   int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i	,   n + 1	,   lowercase		):
                a					=1
    a					=0
    for i in range(lowercase		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
       print(F'{solution() = }')