

'''simple docstring'''






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

 if is_vision_available():
  from transformers import MaskFormerImageProcessor

if is_vision_available():
 from PIL import Image






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  List[Any]      ,lowercase__     :  Any      ,lowercase__     :  Tuple=2      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Any=False      ,lowercase__     :  List[str]=1_0      ,lowercase__     :  Dict=3      ,lowercase__     :  Optional[Any]=3_2 * 4      ,lowercase__     :  Union[str, Any]=3_2 * 6      ,lowercase__     :  Union[str, Any]=4      ,lowercase__     :  List[Any]=3_2      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				is_training
   __lowercase													=				use_auxiliary_loss
   __lowercase													=				num_queries
   __lowercase													=				num_channels
   __lowercase													=				min_size
   __lowercase													=				max_size
   __lowercase													=				num_labels
   __lowercase													=				mask_feature_size






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
       lowercase__    )

   __lowercase													=				torch.ones([self.batch_size, self.min_size, self.max_size]      ,device=lowercase__    )

   __lowercase													=				(
       torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]      ,device=lowercase__    ) > 0.5
   ).float()
   __lowercase													=				(torch.rand((self.batch_size, self.num_labels)      ,device=lowercase__    ) > 0.5).long()

   __lowercase													=				self.get_config()
   return config, pixel_values, pixel_mask, mask_labels, class_labels






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   return MaskFormerConfig.from_backbone_and_decoder_configs(
       backbone_config=SwinConfig(
           depths=[1, 1, 1, 1]      ,)      ,decoder_config=DetrConfig(
           decoder_ffn_dim=1_2_8      ,num_queries=self.num_queries      ,decoder_attention_heads=2      ,d_model=self.mask_feature_size      ,)      ,mask_feature_size=self.mask_feature_size      ,fpn_feature_size=self.mask_feature_size      ,num_channels=self.num_channels      ,num_labels=self.num_labels      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.prepare_config_and_inputs()
   __lowercase													=				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
   return config, inputs_dict






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  Any    ):
   __lowercase													=				output.encoder_hidden_states
   __lowercase													=				output.pixel_decoder_hidden_states
   __lowercase													=				output.transformer_decoder_hidden_states

   self.parent.assertTrue(len(lowercase__    )      ,len(config.backbone_config.depths    )    )
   self.parent.assertTrue(len(lowercase__    )      ,len(config.backbone_config.depths    )    )
   self.parent.assertTrue(len(lowercase__    )      ,config.decoder_config.decoder_layers    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]      ,lowercase__     :  Any      ,lowercase__     :  Tuple=False    ):
   with torch.no_grad():
    __lowercase													=				MaskFormerModel(config=lowercase__    )
    model.to(lowercase__    )
    model.eval()

    __lowercase													=				model(pixel_values=lowercase__      ,pixel_mask=lowercase__    )
    __lowercase													=				model(lowercase__      ,output_hidden_states=lowercase__    )
   # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
   # encoder and pixel decoder
   self.parent.assertEqual(
       output.transformer_decoder_last_hidden_state.shape      ,(self.batch_size, self.num_queries, self.mask_feature_size)      ,)
   # let's ensure the other two hidden state exists
   self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None    )
   self.parent.assertTrue(output.encoder_last_hidden_state is not None    )

   if output_hidden_states:
    self.check_output_hidden_state(lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]    ):
   __lowercase													=				MaskFormerForInstanceSegmentation(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()

   def comm_check_on_output(lowercase__     :  str    ):
    # let's still check that all the required stuff is there
    self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None    )
    self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None    )
    self.parent.assertTrue(result.encoder_last_hidden_state is not None    )
    # okay, now we need to check the logits shape
    # due to the encoder compression, masks have a //4 spatial size
    self.parent.assertEqual(
        result.masks_queries_logits.shape      ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)      ,)
    # + 1 for null class
    self.parent.assertEqual(
        result.class_queries_logits.shape      ,(self.batch_size, self.num_queries, self.num_labels + 1)    )

   with torch.no_grad():
    __lowercase													=				model(pixel_values=lowercase__      ,pixel_mask=lowercase__    )
    __lowercase													=				model(lowercase__    )

    comm_check_on_output(lowercase__    )

    __lowercase													=				model(
        pixel_values=lowercase__      ,pixel_mask=lowercase__      ,mask_labels=lowercase__      ,class_labels=lowercase__    )

   comm_check_on_output(lowercase__    )

   self.parent.assertTrue(result.loss is not None    )
   self.parent.assertEqual(result.loss.shape      ,torch.Size([1]    )    )






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Any           =    (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    (
      {'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE					:						str           =    False
  SCREAMING_SNAKE_CASE					:						Dict           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				MaskFormerModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,has_text_modality=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.create_and_check_maskformer_model(lowercase__      ,**lowercase__      ,output_hidden_states=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*lowercase__    )






  @unittest.skip(reason='''MaskFormer does not use inputs_embeds'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   pass






  @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   pass






  @unittest.skip(reason='''MaskFormer is not a generative model'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   pass






  @unittest.skip(reason='''MaskFormer does not use token embeddings'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   pass






  @require_torch_multi_gpu
  @unittest.skip(
      reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   pass






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				model_class(lowercase__    )
    __lowercase													=				inspect.signature(model.forward    )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase													=				[*signature.parameters.keys()]

    __lowercase													=				['''pixel_values''']
    self.assertListEqual(arg_names[:1]      ,lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   for model_name in ["facebook/maskformer-swin-small-coco"]:
    __lowercase													=				MaskFormerModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				(self.model_tester.min_size,) * 2
   __lowercase													=				{
       '''pixel_values''': torch.randn((2, 3, *size)      ,device=lowercase__    ),
       '''mask_labels''': torch.randn((2, 1_0, *size)      ,device=lowercase__    ),
       '''class_labels''': torch.zeros(2      ,1_0      ,device=lowercase__    ).long(),
   }

   __lowercase													=				MaskFormerForInstanceSegmentation(MaskFormerConfig()    ).to(lowercase__    )
   __lowercase													=				model(**lowercase__    )
   self.assertTrue(outputs.loss is not None    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.create_and_check_maskformer_model(lowercase__      ,**lowercase__      ,output_hidden_states=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase													=				model_class(lowercase__    ).to(lowercase__    )
    __lowercase													=				model(**lowercase__      ,output_attentions=lowercase__    )
    self.assertTrue(outputs.attentions is not None    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   if not self.model_tester.is_training:
    return
   # only MaskFormerForInstanceSegmentation has the loss
   __lowercase													=				self.all_model_classes[1]
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs()

   __lowercase													=				model_class(lowercase__    )
   model.to(lowercase__    )
   model.train()

   __lowercase													=				model(lowercase__      ,mask_labels=lowercase__      ,class_labels=lowercase__    ).loss
   loss.backward()






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   # only MaskFormerForInstanceSegmentation has the loss
   __lowercase													=				self.all_model_classes[1]
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs()
   __lowercase													=				True
   __lowercase													=				True

   __lowercase													=				model_class(lowercase__    )
   model.to(lowercase__    )
   model.train()

   __lowercase													=				model(lowercase__      ,mask_labels=lowercase__      ,class_labels=lowercase__    )

   __lowercase													=				outputs.encoder_hidden_states[0]
   encoder_hidden_states.retain_grad()

   __lowercase													=				outputs.pixel_decoder_hidden_states[0]
   pixel_decoder_hidden_states.retain_grad()
   # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
   __lowercase													=				outputs.transformer_decoder_hidden_states[0]
   transformer_decoder_hidden_states.retain_grad()

   __lowercase													=				outputs.attentions[0]
   attentions.retain_grad()

   outputs.loss.backward(retain_graph=lowercase__    )

   self.assertIsNotNone(encoder_hidden_states.grad    )
   self.assertIsNotNone(pixel_decoder_hidden_states.grad    )
   self.assertIsNotNone(transformer_decoder_hidden_states.grad    )
   self.assertIsNotNone(attentions.grad    )
lowerCAmelCase__      		=			1e-4


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_vision
@slow
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  @cached_property
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   return (
       MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''    )
       if is_vision_available()
       else None
   )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''    ).to(lowercase__    )
   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )
   __lowercase													=				inputs['''pixel_values'''].shape
   # check size is divisible by 32
   self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
   # check size
   self.assertEqual(lowercase__      ,(1, 3, 8_0_0, 1_0_8_8)    )

   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )

   __lowercase													=				torch.tensor(
       [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]    ).to(lowercase__    )
   self.assertTrue(
       torch.allclose(
           outputs.encoder_last_hidden_state[0, 0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )

   __lowercase													=				torch.tensor(
       [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]    ).to(lowercase__    )
   self.assertTrue(
       torch.allclose(
           outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )

   __lowercase													=				torch.tensor(
       [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]    ).to(lowercase__    )
   self.assertTrue(
       torch.allclose(
           outputs.transformer_decoder_last_hidden_state[0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				(
       MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''    )
       .to(lowercase__    )
       .eval()
   )
   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )
   __lowercase													=				inputs['''pixel_values'''].shape
   # check size is divisible by 32
   self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
   # check size
   self.assertEqual(lowercase__      ,(1, 3, 8_0_0, 1_0_8_8)    )

   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )
   # masks_queries_logits
   __lowercase													=				outputs.masks_queries_logits
   self.assertEqual(
       masks_queries_logits.shape      ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      ,)
   __lowercase													=				[
       [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
       [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
       [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
   ]
   __lowercase													=				torch.tensor(lowercase__    ).to(lowercase__    )
   self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )
   # class_queries_logits
   __lowercase													=				outputs.class_queries_logits
   self.assertEqual(
       class_queries_logits.shape      ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
   __lowercase													=				torch.tensor(
       [
           [1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
           [3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
           [1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
       ]    ).to(lowercase__    )
   self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				(
       MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''    )
       .to(lowercase__    )
       .eval()
   )
   __lowercase													=				self.default_image_processor
   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )
   __lowercase													=				inputs['''pixel_values'''].shape
   # check size is divisible by 32
   self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0    )
   # check size
   self.assertEqual(lowercase__      ,(1, 3, 8_0_0, 1_0_8_8)    )

   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )
   # masks_queries_logits
   __lowercase													=				outputs.masks_queries_logits
   self.assertEqual(
       masks_queries_logits.shape      ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      ,)
   __lowercase													=				[[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
   __lowercase													=				torch.tensor(lowercase__    ).to(lowercase__    )
   self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )
   # class_queries_logits
   __lowercase													=				outputs.class_queries_logits
   self.assertEqual(
       class_queries_logits.shape      ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)    )
   __lowercase													=				torch.tensor(
       [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]    ).to(lowercase__    )
   self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,lowercase__      ,atol=lowercase__    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				(
       MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''    )
       .to(lowercase__    )
       .eval()
   )
   __lowercase													=				self.default_image_processor

   __lowercase													=				image_processor(
       [np.zeros((3, 8_0_0, 1_3_3_3)    ), np.zeros((3, 8_0_0, 1_3_3_3)    )]      ,segmentation_maps=[np.zeros((3_8_4, 3_8_4)    ).astype(np.floataa    ), np.zeros((3_8_4, 3_8_4)    ).astype(np.floataa    )]      ,return_tensors='''pt'''      ,)

   __lowercase													=				inputs['''pixel_values'''].to(lowercase__    )
   __lowercase													=				[el.to(lowercase__    ) for el in inputs['''mask_labels''']]
   __lowercase													=				[el.to(lowercase__    ) for el in inputs['''class_labels''']]

   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )

   self.assertTrue(outputs.loss is not None    )




'''simple docstring'''






from scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
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__      		=			'''
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__      		=			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     lowercase_			(datasets.Metric   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   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     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}

