code: 
  
  
  
  
  
  '''simple docstring'''
  
  from sklearn.metrics import precision_score
  
  import datasets
  
  
  __lowerCAmelCase							:			Tuple				="\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n"
  
  
  __lowerCAmelCase							:			Optional[Any]				="\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to 'warn'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - 'warn': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'precision': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['precision'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(results)\n        {'precision': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'precision': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'precision': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'precision': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results['precision']])\n        [0.67, 0.0, 0.0]\n"
  
  
  __lowerCAmelCase							:			List[Any]				="\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION       )
  class      UpperCAmelCase			( datasets.Metric       ):
   def   UpperCAmelCase_							(       self   :Any							)->							List[str]:
       return datasets.MetricInfo(
           description=_DESCRIPTION		, citation=_CITATION		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("int32"							)							),
                   "references": datasets.Sequence(datasets.Value("int32"							)							),
               }
               if self.config_name == "multilabel"
               else {
                   "predictions": datasets.Value("int32"							),
                   "references": datasets.Value("int32"							),
               }							)		, reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html"]		, )
  
  
  
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Dict		, lowercase_   :Optional[int]=None		, lowercase_   :str=1		, lowercase_   :Any="binary"		, lowercase_   :List[Any]=None		, lowercase_   :Any="warn"		, )->							Any:
       A__       =    precision_score(
           lowercase_		, lowercase_		, labels=lowercase_		, pos_label=lowercase_		, average=lowercase_		, sample_weight=lowercase_		, zero_division=lowercase_		, )
       return {"precision": float(lowercase_							) if score.size == 1 else score}
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import getopt
  import json
  import os
  
  # import numpy as np
  import sys
  from collections import OrderedDict
  
  import datasets
  import numpy as np
  import torch
  from modeling_frcnn import GeneralizedRCNN
  from processing_image import Preprocess
  
  from utils import Config
  __lowerCAmelCase							:			str				=False
  __lowerCAmelCase							:			Union[str, Any]				=Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  __lowerCAmelCase							:			int				=datasets.Features(
      OrderedDict(
          {
              "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
              "img_id": datasets.Value("int32"),
              "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :List[str]		, lowercase_   :List[Any]=sys.argv[1:]							)->							Dict:
       A__       =    None
       A__       =    None
       A__       =    None
       A__       =    1
       A__,			A__       =    getopt.getopt(lowercase_		, "i:o:b:s"		, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
       for opt, arg in opts:
           if opt in ("-i", "--inputdir"):
               A__       =    arg
           elif opt in ("-o", "--outfile"):
               A__       =    arg
           elif opt in ("-b", "--batch_size"):
               A__       =    int(lowercase_							)
           elif opt in ("-s", "--subset_list"):
               A__       =    arg
  
       assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
       assert outputfile is not None and not os.path.isfile(lowercase_							), F"{outputfile}"
       if subset_list is not None:
           with open(os.path.realpath(lowercase_							)							) as f:
               A__       =    {self._vqa_file_split()[0] for x in tryload(lowercase_							)}
       else:
           A__       =    None
  
       A__       =    CONFIG
       if torch.cuda.is_available():
           A__       =    "cuda"
       A__       =    os.path.realpath(lowercase_							)
       A__       =    os.path.realpath(lowercase_							)
       A__       =    Preprocess(self.config							)
       A__       =    GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"		, config=self.config							)
       A__       =    batch_size if batch_size != 0 else 1
       A__       =    DEFAULT_SCHEMA
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Dict:
       A__       =    int(file.split("."							)[0].split("_"							)[-1]							)
       A__       =    os.path.join(self.inputdir		, lowercase_							)
       return (img_id, filepath)
   @property
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Optional[int]:
       A__       =    []
       for i, file in enumerate(os.listdir(self.inputdir							)							):
           if self.subset_list is not None and i not in self.subset_list:
               continue
           batch.append(self._vqa_file_split(lowercase_							)							)
           if len(lowercase_							) == self.batch:
               A__       =    batch
               A__       =    []
               yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
       for i in range(1							):
           yield list(map(lowercase_		, zip(*lowercase_							)							)							)
  
  
  
   def __call__(       self   :Optional[int]							)->							List[str]:
       # make writer
       if not TEST:
           A__       =    datasets.ArrowWriter(features=self.schema		, path=self.outputfile							)
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator							):
           A__,			A__,			A__       =    self.preprocess(lowercase_							)
           A__       =    self.model(
               lowercase_		, lowercase_		, scales_yx=lowercase_		, padding="max_detections"		, max_detections=self.config.MAX_DETECTIONS		, pad_value=0		, return_tensors="np"		, location="cpu"		, )
           A__       =    output_dict.pop("normalized_boxes"							)
           if not TEST:
               A__       =    np.array(lowercase_							)
               A__       =    self.schema.encode_batch(lowercase_							)
               writer.write_batch(lowercase_							)
           if TEST:
               break
           # finalizer the writer
       if not TEST:
           A__,			A__       =    writer.finalize()
           print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int] ):
      try:
          A__       =    json.load(_lowerCamelCase )
          try:
              A__       =    list(data.keys() )
          except Exception:
              A__       =    [d["img_id"] for d in data]
      except Exception:
          try:
              A__       =    eval(stream.read() )
          except Exception:
              A__       =    stream.read().split("\n" )
      return data
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			int				=Extract(sys.argv[1:])
    extract()
    if not TEST:
      __lowerCAmelCase							:			Optional[Any]				=datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  