



'''simple docstring'''





from scipy.stats import pearsonr

import datasets


A					:       Tuple									=     "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe 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.\nThe 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.\n"


A					:       str									=     "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    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.\n    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.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


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





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





    def 	__snake_case  (							self							:							Tuple      ):



       '''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 	__snake_case  (							self							:							Optional[int],							_snake_case							:							int,							_snake_case							:							Any,							_snake_case							:							Optional[int]=False      ):



       '''simple docstring'''







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







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





@add_end_docstrings(__a		)
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    **lowerCAmelCase__							:Dict     )  ->int:
										super().__init__(**lowerCAmelCase__     )
										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(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    **lowerCAmelCase__							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      {}
										lowercase         =      {}
										lowercase         =      {}
										# preprocess args
										if "points_per_batch" in kwargs:
														lowercase         =      kwargs["points_per_batch"]
										if "points_per_crop" in kwargs:
														lowercase         =      kwargs["points_per_crop"]
										if "crops_n_layers" in kwargs:
														lowercase         =      kwargs["crops_n_layers"]
										if "crop_overlap_ratio" in kwargs:
														lowercase         =      kwargs["crop_overlap_ratio"]
										if "crop_n_points_downscale_factor" in kwargs:
														lowercase         =      kwargs["crop_n_points_downscale_factor"]
										# postprocess args
										if "pred_iou_thresh" in kwargs:
														lowercase         =      kwargs["pred_iou_thresh"]
										if "stability_score_offset" in kwargs:
														lowercase         =      kwargs["stability_score_offset"]
										if "mask_threshold" in kwargs:
														lowercase         =      kwargs["mask_threshold"]
										if "stability_score_thresh" in kwargs:
														lowercase         =      kwargs["stability_score_thresh"]
										if "crops_nms_thresh" in kwargs:
														lowercase         =      kwargs["crops_nms_thresh"]
										if "output_rle_mask" in kwargs:
														lowercase         =      kwargs["output_rle_mask"]
										if "output_bboxes_mask" in kwargs:
														lowercase         =      kwargs["output_bboxes_mask"]
										return preprocess_kwargs, forward_params, postprocess_kwargs





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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int = 0		,    lowerCAmelCase__							:float = 512 / 1500		,    lowerCAmelCase__							:Optional[int] = 32		,    lowerCAmelCase__							:Optional[int] = 1		,    )  ->Any:
										lowercase         =      load_image(lowerCAmelCase__     )
										lowercase         =      self.image_processor.size["longest_edge"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.generate_crop_boxes(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      self.image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     )

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

										lowercase         =      grid_points.shape[1]
										lowercase         =      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		,    lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      grid_points[:, i : i + points_per_batch, :, :]
														lowercase         =      input_labels[:, i : i + points_per_batch]
														lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict=0.88		,    lowerCAmelCase__							:Dict=0.95		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:int=1		,    )  ->str:
										lowercase         =      model_inputs.pop("input_boxes"     )
										lowercase         =      model_inputs.pop("is_last"     )
										lowercase         =      model_inputs.pop("original_sizes"     ).tolist()
										lowercase         =      model_inputs.pop("reshaped_input_sizes"     ).tolist()

										lowercase         =      self.model(**lowerCAmelCase__     )

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





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:int=0.7		,    )  ->List[Any]:
										lowercase         =      []
										lowercase         =      []
										lowercase         =      []
										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"     )     )

										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.post_process_for_mask_generation(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

										lowercase         =      {}
										if output_rle_mask:
														lowercase         =      rle_mask

										if output_bboxes_mask:
														lowercase         =      bounding_boxes

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