
'''simple docstring'''




import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


__lowerCamelCase   :					str   							=					1E-4

if is_torch_available():
		import torch

		from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
		from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder







@require_torch
class    A_	:
							"""simple docstring"""




							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=16       ,							lowerCAmelCase__			:Tuple=13       ,							lowerCAmelCase__			:int=7       ,							lowerCAmelCase__			:Dict=14       ,							lowerCAmelCase__			:int=10       ,							lowerCAmelCase__			:str=19       ,							lowerCAmelCase__			:Optional[Any]=5       ,							lowerCAmelCase__			:int=4       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:Any=16       ,							lowerCAmelCase__			:Optional[int]=2       ,							lowerCAmelCase__			:str=4       ,							lowerCAmelCase__			:Union[str, Any]=4       ,							lowerCAmelCase__			:int="gelu"       ,							lowerCAmelCase__			:Dict=0.1       ,							lowerCAmelCase__			:int=0.1       ,							lowerCAmelCase__			:Optional[int]=[1, 2, 3, 4, 5]       ,							lowerCAmelCase__			:Dict=25       ,							lowerCAmelCase__			:Any=5       ,							)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       d_model
										snake_case_				:					Any   =       parent
										snake_case_				:					Tuple   =       batch_size
										snake_case_				:					Tuple   =       prediction_length
										snake_case_				:					List[str]   =       context_length
										snake_case_				:					List[str]   =       cardinality
										snake_case_				:					Optional[Any]   =       num_time_features
										snake_case_				:					Dict   =       lags_sequence
										snake_case_				:					Union[str, Any]   =       embedding_dimension
										snake_case_				:					List[str]   =       is_training
										snake_case_				:					int   =       hidden_size
										snake_case_				:					int   =       num_hidden_layers
										snake_case_				:					Dict   =       num_attention_heads
										snake_case_				:					Optional[Any]   =       intermediate_size
										snake_case_				:					List[str]   =       hidden_act
										snake_case_				:					int   =       hidden_dropout_prob
										snake_case_				:					str   =       attention_probs_dropout_prob

										snake_case_				:					Optional[int]   =       context_length
										snake_case_				:					Dict   =       prediction_length + label_length
										snake_case_				:					Dict   =       label_length

										snake_case_				:					Union[str, Any]   =       moving_average
										snake_case_				:					int   =       autocorrelation_factor

							def 							_A	(		self			:Union[str, Any]				)				->  str:


										'''simple docstring'''




										return AutoformerConfig(
										    d_model=self.d_model       ,							encoder_layers=self.num_hidden_layers       ,							decoder_layers=self.num_hidden_layers       ,							encoder_attention_heads=self.num_attention_heads       ,							decoder_attention_heads=self.num_attention_heads       ,							encoder_ffn_dim=self.intermediate_size       ,							decoder_ffn_dim=self.intermediate_size       ,							dropout=self.hidden_dropout_prob       ,							attention_dropout=self.attention_probs_dropout_prob       ,							prediction_length=self.prediction_length       ,							context_length=self.context_length       ,							label_length=self.label_length       ,							lags_sequence=self.lags_sequence       ,							num_time_features=self.num_time_features       ,							num_static_categorical_features=1       ,							cardinality=[self.cardinality]       ,							embedding_dimension=[self.embedding_dimension]       ,							moving_average=self.moving_average       ,							)

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Union[str, Any]				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       config.context_length + max(config.lags_sequence				)

										snake_case_				:					Union[str, Any]   =       ids_tensor([self.batch_size, 1]       ,							config.cardinality[0]				)
										snake_case_				:					Union[str, Any]   =       floats_tensor([self.batch_size, _past_length, config.num_time_features]				)
										snake_case_				:					Any   =       floats_tensor([self.batch_size, _past_length]				)
										snake_case_				:					List[str]   =       floats_tensor([self.batch_size, _past_length]				) > 0.5

										# decoder inputs
										snake_case_				:					Tuple   =       floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]				)
										snake_case_				:					Optional[Any]   =       floats_tensor([self.batch_size, config.prediction_length]				)

										snake_case_				:					Optional[Any]   =       {
										    "past_values": past_values,
										    "static_categorical_features": static_categorical_features,
										    "past_time_features": past_time_features,
										    "past_observed_mask": past_observed_mask,
										    "future_time_features": future_time_features,
										    "future_values": future_values,
										}
										return inputs_dict

							def 							_A	(		self			:Optional[int]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.get_config()
										snake_case_				:					Any   =       self.prepare_autoformer_inputs_dict(lowerCAmelCase__				)
										return config, inputs_dict

							def 							_A	(		self			:Union[str, Any]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.prepare_config_and_inputs()
										return config, inputs_dict






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:Optional[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       AutoformerModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()
										snake_case_				:					Union[str, Any]   =       model(**lowerCAmelCase__				)

										snake_case_				:					Dict   =       outputs.encoder_last_hidden_state
										snake_case_				:					Optional[Any]   =       outputs.last_hidden_state

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case_				:					str   =       model.get_encoder()
													encoder.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					List[str]   =       AutoformerEncoder.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)

										snake_case_				:					str   =       model.create_network_inputs(**lowerCAmelCase__				)
										snake_case_				:					Any   =       model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]				)

										snake_case_				:					List[str]   =       torch.cat(
										    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])       ,							dim=-1       ,							)
										snake_case_				:					str   =       encoder(inputs_embeds=lowerCAmelCase__				)[0]
										self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3				)

										snake_case_				:					Optional[int]   =       (
										    torch.mean(transformer_inputs[:, : config.context_length, ...]       ,							dim=1				)
										    .unsqueeze(1				)
										    .repeat(1       ,							config.prediction_length       ,							1				)
										)
										snake_case_				:					List[Any]   =       torch.zeros(
										    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]       ,							device=enc_input.device       ,							)

										snake_case_				:					int   =       torch.cat(
										    (
										        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)       ,							dim=1				),
										        feature[:, config.context_length - config.label_length :, ...],
										    )       ,							dim=-1       ,							)
										snake_case_				:					Any   =       torch.cat(
										    (
										        torch.cat((trend_input[:, -config.label_length :, ...], mean)       ,							dim=1				),
										        feature[:, config.context_length - config.label_length :, ...],
										    )       ,							dim=-1       ,							)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case_				:					Optional[int]   =       model.get_decoder()
													decoder.save_pretrained(lowerCAmelCase__				)
													snake_case_				:					int   =       AutoformerDecoder.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)

										snake_case_				:					Union[str, Any]   =       decoder(
										    trend=lowerCAmelCase__       ,							inputs_embeds=lowerCAmelCase__       ,							encoder_hidden_states=lowerCAmelCase__       ,							)[0]

										self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3				)







@require_torch
class    A_	(a_						,					a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
							a__   		=	(AutoformerForPrediction,) if is_torch_available() else ()
							a__   		=	{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
							a__   		=	False
							a__   		=	False
							a__   		=	False
							a__   		=	False
							a__   		=	False
							a__   		=	False

							def 							_A	(		self			:int				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       AutoformerModelTester(self				)
										snake_case_				:					Optional[Any]   =       ConfigTester(self       ,							config_class=lowerCAmelCase__       ,							has_text_modality=lowerCAmelCase__				)

							def 							_A	(		self			:Tuple				)				->  Dict:


										'''simple docstring'''




										self.config_tester.run_common_tests()

							def 							_A	(		self			:Optional[int]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													snake_case_				:					Any   =       model_class(lowerCAmelCase__				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(lowerCAmelCase__				)
																snake_case_				:					int   =       model_class.from_pretrained(lowerCAmelCase__       ,							output_loading_info=lowerCAmelCase__				)
													self.assertEqual(info["missing_keys"]       ,							[]				)

							def 							_A	(		self			:Optional[int]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__				)

							@unittest.skip(reason="Model has no tokens embeddings"				)
							def 							_A	(		self			:Optional[int]				)				->  Optional[Any]:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       inspect.signature(getattr(lowerCAmelCase__       ,							"forward"				)				)
										# The main input is the name of the argument after `self`
										snake_case_				:					Any   =       list(model_signature.parameters.keys()				)[1]
										self.assertEqual(AutoformerModel.main_input_name       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													snake_case_				:					Optional[Any]   =       model_class(lowerCAmelCase__				)
													snake_case_				:					Optional[int]   =       inspect.signature(model.forward				)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													snake_case_				:					Tuple   =       [*signature.parameters.keys()]

													snake_case_				:					Optional[int]   =       [
													    "past_values",
													    "past_time_features",
													    "past_observed_mask",
													    "static_categorical_features",
													    "static_real_features",
													    "future_values",
													    "future_time_features",
													]

													if model.__class__.__name__ in ["AutoformerForPrediction"]:
																expected_arg_names.append("future_observed_mask"				)

													expected_arg_names.extend(
													    [
													        "decoder_attention_mask",
													        "head_mask",
													        "decoder_head_mask",
													        "cross_attn_head_mask",
													        "encoder_outputs",
													        "past_key_values",
													        "output_hidden_states",
													        "output_attentions",
													        "use_cache",
													        "return_dict",
													    ]				)

													self.assertListEqual(arg_names[: len(lowerCAmelCase__				)]       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.model_tester.prepare_config_and_inputs_for_common()
										snake_case_				:					List[str]   =       True

										snake_case_				:					Dict   =       getattr(self.model_tester       ,							"seq_length"       ,							lowerCAmelCase__				)
										snake_case_				:					str   =       getattr(self.model_tester       ,							"decoder_seq_length"       ,							lowerCAmelCase__				)
										snake_case_				:					int   =       getattr(self.model_tester       ,							"encoder_seq_length"       ,							lowerCAmelCase__				)
										snake_case_				:					Tuple   =       getattr(self.model_tester       ,							"d_model"       ,							lowerCAmelCase__				)
										snake_case_				:					int   =       getattr(self.model_tester       ,							"num_attention_heads"       ,							lowerCAmelCase__				)
										snake_case_				:					Optional[int]   =       d_model // num_attention_heads

										for model_class in self.all_model_classes:
													snake_case_				:					int   =       True
													snake_case_				:					Dict   =       False
													snake_case_				:					Optional[Any]   =       True
													snake_case_				:					Optional[Any]   =       model_class(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													with torch.no_grad():
																snake_case_				:					List[str]   =       model(**self._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__				)				)
													snake_case_				:					Optional[int]   =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_hidden_layers				)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													snake_case_				:					str   =       True
													snake_case_				:					Optional[int]   =       model_class(lowerCAmelCase__				)
													model.to(lowerCAmelCase__				)
													model.eval()
													with torch.no_grad():
																snake_case_				:					Any   =       model(**self._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__				)				)
													snake_case_				:					List[Any]   =       outputs.encoder_attentions
													self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_hidden_layers				)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads, encoder_seq_length, dim]       ,							)
													snake_case_				:					int   =       len(lowerCAmelCase__				)

													snake_case_				:					Dict   =       7

													if "last_hidden_state" in outputs:
																correct_outlen += 1

													if "trend" in outputs:
																correct_outlen += 1

													if "past_key_values" in outputs:
																correct_outlen += 1  # past_key_values have been returned

													if "loss" in outputs:
																correct_outlen += 1

													if "params" in outputs:
																correct_outlen += 1

													self.assertEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

													# decoder attentions
													snake_case_				:					Any   =       outputs.decoder_attentions
													self.assertIsInstance(lowerCAmelCase__       ,							(list, tuple)				)
													self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_hidden_layers				)
													self.assertListEqual(
													    list(decoder_attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads, decoder_seq_length, dim]       ,							)

													# cross attentions
													snake_case_				:					List[Any]   =       outputs.cross_attentions
													self.assertIsInstance(lowerCAmelCase__       ,							(list, tuple)				)
													self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_hidden_layers				)
													self.assertListEqual(
													    list(cross_attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads, decoder_seq_length, dim]       ,							)

										# Check attention is always last and order is fine
										snake_case_				:					List[Any]   =       True
										snake_case_				:					Union[str, Any]   =       True
										snake_case_				:					Dict   =       model_class(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										with torch.no_grad():
													snake_case_				:					List[str]   =       model(**self._prepare_for_class(lowerCAmelCase__       ,							lowerCAmelCase__				)				)

										self.assertEqual(out_len + 2       ,							len(lowerCAmelCase__				)				)

										snake_case_				:					Optional[Any]   =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

										self.assertEqual(len(lowerCAmelCase__				)       ,							self.model_tester.num_hidden_layers				)
										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]				)       ,							[self.model_tester.num_attention_heads, encoder_seq_length, dim]       ,							)






							@is_flaky()
							def 							_A	(		self			:List[Any]				)				->  List[str]:


										'''simple docstring'''




										super().test_retain_grad_hidden_states_attentions()

def 	__UpperCAmelCase       ( __magic_name__="train-batch.pt"						)->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch"     ,filename=__magic_name__     ,repo_type="dataset"						)
			snake_case_				:					str   =       torch.load(__magic_name__     ,map_location=__magic_name__						)
			return batch







@require_torch
@slow
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Dict				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly"				).to(lowerCAmelCase__				)
										snake_case_				:					int   =       prepare_batch()

										with torch.no_grad():
													snake_case_				:					Dict   =       model(
													    past_values=batch["past_values"]       ,							past_time_features=batch["past_time_features"]       ,							past_observed_mask=batch["past_observed_mask"]       ,							static_categorical_features=batch["static_categorical_features"]       ,							future_values=batch["future_values"]       ,							future_time_features=batch["future_time_features"]       ,							)[0]

										snake_case_				:					int   =       torch.Size(
										    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)				)
										self.assertEqual(output.shape       ,							lowerCAmelCase__				)

										snake_case_				:					Any   =       torch.tensor(
										    [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]       ,							device=lowerCAmelCase__				)
										self.assertTrue(torch.allclose(output[0, :3, :3]       ,							lowerCAmelCase__       ,							atol=lowerCAmelCase__				)				)

							def 							_A	(		self			:Union[str, Any]				)				->  str:


										'''simple docstring'''




										snake_case_				:					str   =       AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"				).to(lowerCAmelCase__				)
										snake_case_				:					Tuple   =       prepare_batch("val-batch.pt"				)
										with torch.no_grad():
													snake_case_				:					Any   =       model(
													    past_values=batch["past_values"]       ,							past_time_features=batch["past_time_features"]       ,							past_observed_mask=batch["past_observed_mask"]       ,							static_categorical_features=batch["static_categorical_features"]       ,							).encoder_last_hidden_state
										snake_case_				:					Optional[int]   =       torch.Size((64, model.config.context_length, model.config.d_model)				)
										self.assertEqual(output.shape       ,							lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       torch.tensor(
										    [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]       ,							device=lowerCAmelCase__				)
										self.assertTrue(torch.allclose(output[0, :3, :3]       ,							lowerCAmelCase__       ,							atol=lowerCAmelCase__				)				)






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


										'''simple docstring'''




										snake_case_				:					str   =       AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"				).to(lowerCAmelCase__				)
										snake_case_				:					int   =       prepare_batch("val-batch.pt"				)
										with torch.no_grad():
													snake_case_				:					Union[str, Any]   =       model.generate(
													    static_categorical_features=batch["static_categorical_features"]       ,							past_time_features=batch["past_time_features"]       ,							past_values=batch["past_values"]       ,							future_time_features=batch["future_time_features"]       ,							past_observed_mask=batch["past_observed_mask"]       ,							)
										snake_case_				:					Tuple   =       torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)				)
										self.assertEqual(outputs.sequences.shape       ,							lowerCAmelCase__				)

										snake_case_				:					str   =       torch.tensor([3_130.6_763, 4_056.5_293, 7_053.0_786]       ,							device=lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       outputs.sequences.mean(dim=1				)
										self.assertTrue(torch.allclose(mean_prediction[0, -3:]       ,							lowerCAmelCase__       ,							rtol=1E-1				)				)












'''simple docstring'''




from scipy.stats import spearmanr

import datasets


__lowerCamelCase   :					str   							=					'''
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   :					int   							=					'''
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[str]   							=					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    A_	(datasets.Metric							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, 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 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       spearmanr(lowerCAmelCase__       ,							lowerCAmelCase__				)
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}




