




from scipy.stats import spearmanr

import datasets


_lowerCamelCase      :						List[Any]							=  """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

_lowerCamelCase      :						Any							=  """
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

_lowerCamelCase      :						List[Any]							=  r"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@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, {\.I}lhan 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, Ant{\^o}nio 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 							UpperCamelCase_       (					datasets.Metric	):

    '''simple docstring'''






    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Optional[Any]:





           '''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.spearmanr.html''']   ,					)





    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      List[str]=False)		->int:





           '''simple docstring'''

           A__ 					=       spearmanr(UpperCAmelCase__   ,					UpperCAmelCase__)
           if return_pvalue:
                  return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
           else:
                  return {"spearmanr": results[0]}






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Dict       			=  ["""pixel_values"""]






		def __init__(						self				:     Tuple   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Union[int, float] = 1 / 2_55   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Any   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						size if size is not None else {"height": 3_84, "width": 3_84}
				SCREAMING_SNAKE_CASE_				:str				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						do_resize
				SCREAMING_SNAKE_CASE_				:int				     =						size
				SCREAMING_SNAKE_CASE_				:int				     =						resample
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				SCREAMING_SNAKE_CASE_				:Dict				     =						image_std if image_std is not None else OPENAI_CLIP_STD
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_convert_rgb






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Any   ,  ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)
				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()}")
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(size["height"], size["width"])
				return resize(UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Union[int, float]   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     List[str]   ,  ):
				return rescale(UpperCAmelCase   ,  scale=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Union[float, List[float]]   ,  UpperCAmelCase				:     Union[float, List[float]]   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Optional[Any]   ,  ):
				return normalize(UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Dict[str, int]] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[float] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     ChannelDimension = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Dict   ,  ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image_mean if image_mean is not None else self.image_mean
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image_std if image_std is not None else self.image_std
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Tuple				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						make_list_of_images(UpperCAmelCase)

				if not valid_images(UpperCAmelCase):
						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 or resample is None:
						raise ValueError("Size and resample 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_normalize and (image_mean is None or image_std is None):
						raise ValueError("Image mean and std must be specified if do_normalize is True.")

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[convert_to_rgb(UpperCAmelCase) for image in images]

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

				if do_resize:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						[self.resize(image=UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase) for image in images]

				if do_rescale:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.rescale(image=UpperCAmelCase   ,  scale=UpperCAmelCase) for image in images]

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.normalize(image=UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						BatchFeature(data={"pixel_values": images}   ,  tensor_type=UpperCAmelCase)

				return encoded_outputs






