





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	:					Optional[Any]			  =					logging.get_logger(__name__)


__lowerCamelCase	:					Optional[Any]			  =					Dict[str, Any]
__lowerCamelCase	:					Tuple			  =					List[Prediction]



@add_end_docstrings(__snake_case      )
class  A__       (	__snake_case      ):





      def __init__(			self	,							*A_	,							**A_       ):
       '''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	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       {}
       if "threshold" in kwargs:
        UpperCamelCase     : Any	     =       kwargs["threshold"]
       return {}, {}, postprocess_kwargs





      def __call__(			self	,							*A_	,							**A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_	,							A_=0.9       ):
       '''simple docstring'''




       UpperCamelCase     : List[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     : List[str]	     =       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),
                 ]       )       )

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

        UpperCamelCase     : Union[str, Any]	     =       scores.tolist()
        UpperCamelCase     : int	     =       [self.model.config.idalabel[label.item()] for label in labels]
        UpperCamelCase     : List[Any]	     =       [self._get_bounding_box(A_       ) for box in boxes]

        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
        UpperCamelCase     : str	     =       ["score", "label", "box"]
        UpperCamelCase     : Optional[int]	     =       [
            dict(zip(A_	,							A_       )       )
            for vals in zip(raw_annotation["scores"]	,							raw_annotation["labels"]	,							raw_annotation["boxes"]       )
        ]

       return annotation





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




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







import os



def 		A_    (					)						->			int:
 UpperCamelCase     : List[str]	     =       os.path.dirname(os.path.realpath(_lowerCAmelCase					)					)
 UpperCamelCase     : Any	     =       os.path.join(_lowerCAmelCase							,       "triangle.txt"					)

 with open(_lowerCAmelCase					) as f:
  UpperCamelCase     : Optional[Any]	     =       f.readlines()

 UpperCamelCase     : Tuple	     =       []
 for line in triangle:
  UpperCamelCase     : List[str]	     =       []
  for number in line.strip().split(" "					):
   numbers_from_line.append(int(_lowerCAmelCase					)					)
  a.append(_lowerCAmelCase					)

 for i in range(1							,       len(_lowerCAmelCase					)					):
  for j in range(len(a[i]					)					):
   UpperCamelCase     : List[Any]	     =       a[i - 1][j] if j != len(a[i - 1]					) else 0
   UpperCamelCase     : Dict	     =       a[i - 1][j - 1] if j > 0 else 0
   a[i][j] += max(_lowerCAmelCase							,       _lowerCAmelCase					)
 return max(a[-1]					)


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