





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


__lowerCAmelCase				:      Union[str, Any]						      =Dict[str, Any]
__lowerCAmelCase				:      Optional[int]						      =List[Prediction]






@add_end_docstrings(UpperCamelCase__							)
class   UpperCAmelCase      (			UpperCamelCase__							):
 def __init__(							self :str		,			*lowercase_ :Dict		,			**lowercase_ :Any				)->				int:
     super().__init__(*lowercase_		,			**lowercase_				)

     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 :int		,			**lowercase_ :int				)->				Dict:
     A__							=     {}
     if "threshold" in kwargs:
         A__							=     kwargs["threshold"]
     return {}, {}, postprocess_kwargs
 def __call__(							self :Dict		,			*lowercase_ :Union[str, Any]		,			**lowercase_ :List[Any]				)->				Union[Predictions, List[Prediction]]:
     return super().__call__(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :Tuple				)->				Dict:
     A__							=     load_image(lowercase_				)
     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 	UpperCAmelCase_				(							self :int		,			lowercase_ :Tuple				)->				str:
     A__							=     model_inputs.pop("target_size"				)
     A__							=     self.model(**lowercase_				)
     A__							=     outputs.__class__({"target_size": target_size, **outputs}				)
     if self.tokenizer is not None:
         A__							=     model_inputs["bbox"]
     return model_outputs
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Optional[int]		,			lowercase_ :int=0.9				)->				str:
     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(lowercase_ :Union[str, Any]				):
             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),
                     ]				)				)

         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(lowercase_				) for bbox in model_outputs["bbox"].squeeze(0				)]
         A__							=     ["score", "label", "box"]
         A__							=     [dict(zip(lowercase_		,			lowercase_				)				) for vals in zip(scores.tolist()		,			lowercase_		,			lowercase_				) if vals[0] > threshold]
     else:
         # This is a regular ForObjectDetectionModel
         A__							=     self.image_processor.post_process_object_detection(lowercase_		,			lowercase_		,			lowercase_				)
         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(lowercase_				) for box in boxes]

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

     return annotation



 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :"torch.Tensor"				)->				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'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        elif i == ")":
            # RULE 4
            A__							=     operator_stack.peek()
            operator_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      ="(5 + ((4 * 2) * (2 + 3)))"
  # answer = 45
  print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



