

from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    				=     '\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'


__UpperCAmelCase    				=     '\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'


__UpperCAmelCase    				=     '\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 A__    ( datasets.Metric ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[int]       ):






      '''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    __magic_name__       (		self :	str     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Dict=False       ):






      '''simple docstring'''




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









from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
  import PIL

if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = True     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else {"shortest_edge": 2_2_4}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Any        =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					str        =    resample
      _lowerCAmelCase   :					Optional[Any]        =    do_rescale
      _lowerCAmelCase   :					Dict        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_flip_channel_order

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Union[str, Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Any     ,						):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Optional[Union[str, ChannelDimension]] = None       ):






      '''simple docstring'''




      return flip_channel_order(A_     ,						data_format=A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Dict        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					Tuple        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Optional[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Dict        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."       )

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Optional[int]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					Any        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Tuple        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					Any        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           _lowerCAmelCase   :					Dict        =    [self.flip_channel_order(image=A_       ) for image in images]

      _lowerCAmelCase   :					Optional[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    outputs.logits

      # Resize logits and compute semantic segmentation maps
      if target_sizes is not None:
           if len(A_       ) != len(A_       ):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"       )

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					Dict        =    target_sizes.numpy()

           _lowerCAmelCase   :					List[Any]        =    []

           for idx in range(len(A_       )       ):
                _lowerCAmelCase   :					Tuple        =    torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0       )     ,						size=target_sizes[idx]     ,						mode="bilinear"     ,						align_corners=A_       )
                _lowerCAmelCase   :					Any        =    resized_logits[0].argmax(dim=0       )
                semantic_segmentation.append(A_       )
      else:
           _lowerCAmelCase   :					Tuple        =    logits.argmax(dim=1       )
           _lowerCAmelCase   :					Tuple        =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

      return semantic_segmentation






