
from sklearn.metrics import recall_score

import datasets


lowerCamelCase_						     =		'''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''


lowerCamelCase_						     =		'''
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `\'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. Note that it can result in an F-score that is not between precision and recall.
    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {\'recall\': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {\'recall\': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {\'recall\': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric(\'recall\')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')
        >>> print(results)
        {\'recall\': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'recall\': array([1., 0., 0.])}
'''


lowerCamelCase_						     =		'''
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,				_KWARGS_DESCRIPTION			)
class      __A(    datasets.Metric			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       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.recall_score.html"""]	,      )







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=1	,      SCREAMING_SNAKE_CASE_="binary"	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_="warn"	,      ):
       UpperCamelCase__				=    recall_score(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_	,      pos_label=SCREAMING_SNAKE_CASE_	,      average=SCREAMING_SNAKE_CASE_	,      sample_weight=SCREAMING_SNAKE_CASE_	,      zero_division=SCREAMING_SNAKE_CASE_	,      )
       return {"recall": float(SCREAMING_SNAKE_CASE_						) if score.size == 1 else score}







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCamelCase_						     =					logging.get_logger(__name__)




@add_end_docstrings(__lowerCamelCase			)
class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      **SCREAMING_SNAKE_CASE_						):
       super().__init__(**SCREAMING_SNAKE_CASE_						)
       requires_backends(self	,      """vision"""						)
       requires_backends(self	,      """torch"""						)

       if self.framework != "pt":
            raise ValueError(F"The {self.__class__} is only available in PyTorch."						)

       self.check_model_type(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}
       UpperCamelCase__				=    {}
       UpperCamelCase__				=    {}
       # preprocess args
       if "points_per_batch" in kwargs:
            UpperCamelCase__				=    kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
            UpperCamelCase__				=    kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
            UpperCamelCase__				=    kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
            UpperCamelCase__				=    kwargs["""crop_overlap_ratio"""]
       if "crop_n_points_downscale_factor" in kwargs:
            UpperCamelCase__				=    kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
            UpperCamelCase__				=    kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
            UpperCamelCase__				=    kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
            UpperCamelCase__				=    kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
            UpperCamelCase__				=    kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
            UpperCamelCase__				=    kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_						):
       return super().__call__(SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      num_workers=SCREAMING_SNAKE_CASE_	,      batch_size=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=64	,      SCREAMING_SNAKE_CASE_ = 0	,      SCREAMING_SNAKE_CASE_ = 5_12 / 15_00	,      SCREAMING_SNAKE_CASE_ = 32	,      SCREAMING_SNAKE_CASE_ = 1	,      ):
       UpperCamelCase__				=    load_image(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor.size["""longest_edge"""]
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.generate_crop_boxes(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)

       with self.device_placement():
            if self.framework == "pt":
                 UpperCamelCase__				=    self.get_inference_context()
                 with inference_context():
                      UpperCamelCase__				=    self._ensure_tensor_on_device(SCREAMING_SNAKE_CASE_	,      device=self.device						)
                      UpperCamelCase__				=    self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""						)						)
                      UpperCamelCase__				=    image_embeddings

       UpperCamelCase__				=    grid_points.shape[1]
       UpperCamelCase__				=    points_per_batch if points_per_batch is not None else n_points

       if points_per_batch <= 0:
            raise ValueError(
                """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
                """To return all points at once, set points_per_batch to None"""						)

       for i in range(0	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    grid_points[:, i : i + points_per_batch, :, :]
            UpperCamelCase__				=    input_labels[:, i : i + points_per_batch]
            UpperCamelCase__				=    i == n_points - points_per_batch
            yield {
                "input_points": batched_points,
                "input_labels": labels,
                "input_boxes": crop_boxes,
                "is_last": is_last,
                **model_inputs,
            }







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=0.88	,      SCREAMING_SNAKE_CASE_=0.95	,      SCREAMING_SNAKE_CASE_=0	,      SCREAMING_SNAKE_CASE_=1	,      ):
       UpperCamelCase__				=    model_inputs.pop("""input_boxes"""						)
       UpperCamelCase__				=    model_inputs.pop("""is_last"""						)
       UpperCamelCase__				=    model_inputs.pop("""original_sizes"""						).tolist()
       UpperCamelCase__				=    model_inputs.pop("""reshaped_input_sizes"""						).tolist()

       UpperCamelCase__				=    self.model(**SCREAMING_SNAKE_CASE_						)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       UpperCamelCase__				=    model_outputs["""pred_masks"""]
       UpperCamelCase__				=    self.image_processor.post_process_masks(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      binarize=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model_outputs["""iou_scores"""]
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.filter_masks(
           masks[0]	,      iou_scores[0]	,      original_sizes[0]	,      input_boxes[0]	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      )
       return {
           "masks": masks,
           "is_last": is_last,
           "boxes": boxes,
           "iou_scores": iou_scores,
       }







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=0.7	,      ):
       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       UpperCamelCase__				=    []
       for model_output in model_outputs:
            all_scores.append(model_output.pop("""iou_scores"""						)						)
            all_masks.extend(model_output.pop("""masks"""						)						)
            all_boxes.append(model_output.pop("""boxes"""						)						)

       UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    torch.cat(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    self.image_processor.post_process_for_mask_generation(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    defaultdict(SCREAMING_SNAKE_CASE_						)
       for output in model_outputs:
            for k, v in output.items():
                 extra[k].append(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    {}
       if output_rle_mask:
            UpperCamelCase__				=    rle_mask

       if output_bboxes_mask:
            UpperCamelCase__				=    bounding_boxes

       return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





