



"""simple docstring"""


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

snake_case_				     =       logging.get_logger(__name__)







@add_end_docstrings(SCREAMING_SNAKE_CASE_				)
class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __init__(					self :List[Any]   ,  **lowercase_ :int					)				->			Union[str, Any]:
												super().__init__(**lowercase_					)
												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(lowercase_					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]   ,  **lowercase_ :int					)				->			List[str]:
												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 :Tuple   ,  lowercase_ :str   ,  *lowercase_ :Tuple   ,  lowercase_ :Optional[int]=None   ,  lowercase_ :Tuple=None   ,  **lowercase_ :str					)				->			List[Any]:
												return super().__call__(lowercase_   ,  *lowercase_   ,  num_workers=lowercase_   ,  batch_size=lowercase_   ,  **lowercase_					)






						def 	UpperCAmelCase__     (					self :Optional[int]   ,  lowercase_ :Any   ,  lowercase_ :Optional[Any]=64   ,  lowercase_ :int = 0   ,  lowercase_ :float = 5_12 / 15_00   ,  lowercase_ :Optional[int] = 32   ,  lowercase_ :Optional[int] = 1   ,  )				->			Union[str, Any]:
												UpperCAmelCase             = load_image(lowercase_					)
												UpperCAmelCase             = self.image_processor.size['longest_edge']
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = self.image_processor.generate_crop_boxes(
												    lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_					)
												UpperCAmelCase             = self.image_processor(images=lowercase_   ,  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(lowercase_   ,  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   ,  lowercase_   ,  lowercase_					):
																		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 :Optional[Any]   ,  lowercase_ :Any   ,  lowercase_ :Optional[Any]=0.88   ,  lowercase_ :Tuple=0.95   ,  lowercase_ :Optional[Any]=0   ,  lowercase_ :Optional[int]=1   ,  )				->			str:
												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(**lowercase_					)

												# 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(
												    lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_   ,  binarize=lowercase_					)
												UpperCAmelCase             = model_outputs['iou_scores']
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = self.image_processor.filter_masks(
												    masks[0]   ,  iou_scores[0]   ,  original_sizes[0]   ,  input_boxes[0]   ,  lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_   ,  )
												return {
												    "masks": masks,
												    "is_last": is_last,
												    "boxes": boxes,
												    "iou_scores": iou_scores,
												}






						def 	UpperCAmelCase__     (					self :Tuple   ,  lowercase_ :Any   ,  lowercase_ :Optional[Any]=False   ,  lowercase_ :List[str]=False   ,  lowercase_ :Union[str, Any]=0.7   ,  )				->			str:
												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(lowercase_					)
												UpperCAmelCase             = torch.cat(lowercase_					)
												UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = self.image_processor.post_process_for_mask_generation(
												    lowercase_   ,  lowercase_   ,  lowercase_   ,  lowercase_					)

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

												UpperCAmelCase             = {}
												if output_rle_mask:
																		UpperCAmelCase             = rle_mask

												if output_bboxes_mask:
																		UpperCAmelCase             = bounding_boxes

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






"""simple docstring"""






from scipy.stats import pearsonr

import datasets


__lowerCAmelCase    :       List[Any]					     ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


__lowerCAmelCase    :       Optional[int]					     ="""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__lowerCAmelCase    :       str					     ="""
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""





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

      def 				A__       (						self						):







            """simple docstring"""



            return datasets.MetricInfo(
                description=_DESCRIPTION		,       citation=_CITATION		,       inputs_description=_KWARGS_DESCRIPTION		,       features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""float"""						),
                        """references""": datasets.Value("""float"""						),
                    }						)		,       reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]		,       )







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False						):







            """simple docstring"""



            if return_pvalue:
                  lowercase														=	pearsonr(__lowerCAmelCase		,       __lowerCAmelCase						)
                  return {"pearsonr": results[0], "p-value": results[1]}
            else:
                  return {"pearsonr": float(pearsonr(__lowerCAmelCase		,       __lowerCAmelCase						)[0]						)}



