
"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=None  ,				UpperCamelCase_=None				):
		if attention_mask is None:
				__SCREAMING_SNAKE_CASE            = tf.cast(tf.math.not_equal(UpperCamelCase_  ,				config.pad_token_id				)  ,				tf.inta				)
		return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		OPTConfig
		__lowercase							:  Union[str, Any]						   =		{}
		__lowercase							:  List[Any]						   =		'''gelu'''




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=1_3							,  lowerCAmelCase__=7							,  lowerCAmelCase__=True							,  lowerCAmelCase__=False							,  lowerCAmelCase__=9_9							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2							,  lowerCAmelCase__=4							,  lowerCAmelCase__=4							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=2_0							,  lowerCAmelCase__=2							,  lowerCAmelCase__=1							,  lowerCAmelCase__=0							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=1_6							,  ):
				__SCREAMING_SNAKE_CASE            = parent
				__SCREAMING_SNAKE_CASE            = batch_size
				__SCREAMING_SNAKE_CASE            = seq_length
				__SCREAMING_SNAKE_CASE            = is_training
				__SCREAMING_SNAKE_CASE            = use_labels
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = eos_token_id
				__SCREAMING_SNAKE_CASE            = pad_token_id
				__SCREAMING_SNAKE_CASE            = bos_token_id
				__SCREAMING_SNAKE_CASE            = embed_dim
				__SCREAMING_SNAKE_CASE            = word_embed_proj_dim
				__SCREAMING_SNAKE_CASE            = False




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = ids_tensor([self.batch_size, self.seq_length - 1]							,  self.vocab_size)
				__SCREAMING_SNAKE_CASE            = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)							,  1)
				__SCREAMING_SNAKE_CASE            = tf.concat([input_ids, eos_tensor]							,  axis=1)

				__SCREAMING_SNAKE_CASE            = self.config_cls(
				    vocab_size=self.vocab_size							,  hidden_size=self.hidden_size							,  num_hidden_layers=self.num_hidden_layers							,  num_attention_heads=self.num_attention_heads							,  ffn_dim=self.intermediate_size							,  dropout=self.hidden_dropout_prob							,  attention_dropout=self.attention_probs_dropout_prob							,  max_position_embeddings=self.max_position_embeddings							,  eos_token_id=self.eos_token_id							,  bos_token_id=self.bos_token_id							,  pad_token_id=self.pad_token_id							,  embed_dim=self.embed_dim							,  word_embed_proj_dim=self.word_embed_proj_dim							,  is_encoder_decoder=lowerCAmelCase__							,  **self.config_updates							,  )
				__SCREAMING_SNAKE_CASE            = prepare_opt_inputs_dict(lowerCAmelCase__							,  lowerCAmelCase__)
				return config, inputs_dict




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = TFOPTModel(config=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = inputs_dict["""input_ids"""]

				__SCREAMING_SNAKE_CASE            = input_ids[:1, :]
				__SCREAMING_SNAKE_CASE            = inputs_dict["""attention_mask"""][:1, :]
				__SCREAMING_SNAKE_CASE            = 1

				# first forward pass
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  use_cache=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				__SCREAMING_SNAKE_CASE            = ids_tensor((self.batch_size, 3)							,  config.vocab_size)
				__SCREAMING_SNAKE_CASE            = tf.cast(ids_tensor((self.batch_size, 3)							,  2)							,  tf.inta)

				# append to next input_ids and
				__SCREAMING_SNAKE_CASE            = tf.concat([input_ids, next_tokens]							,  axis=-1)
				__SCREAMING_SNAKE_CASE            = tf.concat([attention_mask, next_attn_mask]							,  axis=-1)

				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__)[0]
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  past_key_values=lowerCAmelCase__)[0]

				self.parent.assertEqual(next_tokens.shape[1]							,  output_from_past.shape[1])

				# select random slice
				__SCREAMING_SNAKE_CASE            = int(ids_tensor((1,)							,  output_from_past.shape[-1]))
				__SCREAMING_SNAKE_CASE            = output_from_no_past[:, -3:, random_slice_idx]
				__SCREAMING_SNAKE_CASE            = output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(lowerCAmelCase__							,  lowerCAmelCase__							,  rtol=1E-3)



@require_tf
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  List[str]						   =		(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
		__lowercase							:  Any						   =		(TFOPTForCausalLM,) if is_tf_available() else ()
		__lowercase							:  int						   =		(
		    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
		)
		__lowercase							:  List[str]						   =		False
		__lowercase							:  Union[str, Any]						   =		False
		__lowercase							:  Tuple						   =		False
		__lowercase							:  Union[str, Any]						   =		10




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFOPTModelTester(self)
				__SCREAMING_SNAKE_CASE            = ConfigTester(self							,  config_class=lowerCAmelCase__)




		def 	snake_case_     (     self):
				self.config_tester.run_common_tests()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs_for_common()

				def _get_word_embedding_weight(lowerCAmelCase__							,  lowerCAmelCase__):
						if hasattr(lowerCAmelCase__							,  """weight"""):
								return embedding_layer.weight
						else:
								# Here we build the word embeddings weights if not exists.
								# And then we retry to get the attribute once built.
								model.build()
								if hasattr(lowerCAmelCase__							,  """weight"""):
										return embedding_layer.weight
								else:
										return None

				for model_class in self.all_model_classes:
						for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
								# build the embeddings
								__SCREAMING_SNAKE_CASE            = model_class(config=lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_input_embeddings())
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_output_embeddings())

								# reshape the embeddings
								model.resize_token_embeddings(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_input_embeddings())
								__SCREAMING_SNAKE_CASE            = _get_word_embedding_weight(lowerCAmelCase__							,  model.get_output_embeddings())

								# check that the resized embeddings size matches the desired size.
								__SCREAMING_SNAKE_CASE            = size if size is not None else config.vocab_size

								self.assertEqual(new_input_embeddings.shape[0]							,  lowerCAmelCase__)

								# check that weights remain the same after resizing
								__SCREAMING_SNAKE_CASE            = True
								for pa, pa in zip(old_input_embeddings.value()							,  new_input_embeddings.value()):
										if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
												__SCREAMING_SNAKE_CASE            = False
								self.assertTrue(lowerCAmelCase__)

								if old_output_embeddings is not None and new_output_embeddings is not None:
										self.assertEqual(new_output_embeddings.shape[0]							,  lowerCAmelCase__)

										__SCREAMING_SNAKE_CASE            = True
										for pa, pa in zip(old_output_embeddings.value()							,  new_output_embeddings.value()):
												if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
														__SCREAMING_SNAKE_CASE            = False
										self.assertTrue(lowerCAmelCase__)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		return tf.constant(UpperCamelCase_  ,				dtype=tf.intaa				)
@require_tf
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  Union[str, Any]						   =		99




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = tf.ones((4, 1)							,  dtype=tf.intaa) * 2
				__SCREAMING_SNAKE_CASE            = tf.concat([ids_tensor((4, 6)							,  self.vocab_size - 3) + 3, eos_column_vector]							,  axis=1)
				__SCREAMING_SNAKE_CASE            = input_ids.shape[0]
				__SCREAMING_SNAKE_CASE            = OPTConfig(
				    vocab_size=self.vocab_size							,  hidden_size=2_4							,  num_hidden_layers=2							,  num_attention_heads=2							,  ffn_dim=3_2							,  max_position_embeddings=4_8							,  eos_token_id=2							,  pad_token_id=1							,  bos_token_id=0							,  )
				return config, input_ids, batch_size



@require_sentencepiece
@require_tf
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFOPTModel.from_pretrained("""facebook/opt-350m""")
				__SCREAMING_SNAKE_CASE            = _long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
				__SCREAMING_SNAKE_CASE            = tf.not_equal(lowerCAmelCase__							,  model.config.pad_token_id)
				with tf.GradientTape():
						__SCREAMING_SNAKE_CASE            = model(input_ids=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__).last_hidden_state
				__SCREAMING_SNAKE_CASE            = (1, 1_1, 5_1_2)
				self.assertEqual(output.shape							,  lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tf.constant(
				    [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]])
				self.assertTrue(np.allclose(output[:, :3, :3]							,  lowerCAmelCase__							,  atol=4E-3))

				__SCREAMING_SNAKE_CASE            = tf.function(lowerCAmelCase__							,  jit_compile=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = xla_generate(lowerCAmelCase__							,  lowerCAmelCase__)[0]
				self.assertTrue(np.allclose(output[:, :3, :3]							,  lowerCAmelCase__							,  atol=4E-2))



@require_tf
@slow
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		def 	snake_case_     (     self):
				super().setUp()
				__SCREAMING_SNAKE_CASE            = """facebook/opt-350m"""




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(self.path_model)
				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(self.path_model)

				__SCREAMING_SNAKE_CASE            = [
				    """Today is a beautiful day and I want to""",
				    """In the city of""",
				    """Paris is the capital of France and""",
				    """Computers and mobile phones have taken""",
				]
				# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
				__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf"""							,  padding=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tf.math.reduce_mean(model(inputs.input_ids							,  attention_mask=inputs.attention_mask)[0]							,  axis=-1)
				__SCREAMING_SNAKE_CASE            = tf.constant(
				    [
				        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
				        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
				        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
				        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
				    ])
				self.assertTrue(np.allclose(lowerCAmelCase__							,  lowerCAmelCase__							,  atol=1E-4))

				__SCREAMING_SNAKE_CASE            = tf.function(lowerCAmelCase__							,  jit_compile=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tf.math.reduce_mean(xla_generate(inputs.input_ids							,  attention_mask=inputs.attention_mask)[0]							,  axis=-1)
				self.assertTrue(np.allclose(lowerCAmelCase__							,  lowerCAmelCase__							,  atol=1E-4))



@require_tf
@slow
class 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@property
		def 	snake_case_     (     self):
				return [
				    "Today is a beautiful day and I want",
				    "In the city of",
				    "Paris is the capital of France and",
				    "Computers and mobile phones have taken",
				]




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """facebook/opt-125m"""

				__SCREAMING_SNAKE_CASE            = [
				    """Today is a beautiful day and I want to""",
				    """In the city of New York, the city""",
				    """Paris is the capital of France and the capital""",
				    """Computers and mobile phones have taken over the""",
				]

				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(lowerCAmelCase__)

				for prompt in self.prompts:
						__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf""").input_ids

						__SCREAMING_SNAKE_CASE            = model.generate(lowerCAmelCase__							,  max_length=1_0)

						__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
						predicted_outputs += generated_string

				self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """facebook/opt-350m"""

				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = """left"""

				# use different length sentences to test batching
				__SCREAMING_SNAKE_CASE            = [
				    """Hello, my dog is a little""",
				    """Today, I""",
				]

				__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf"""							,  padding=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = inputs["""input_ids"""]

				__SCREAMING_SNAKE_CASE            = model.generate(input_ids=lowerCAmelCase__							,  attention_mask=inputs["""attention_mask"""])

				__SCREAMING_SNAKE_CASE            = tokenizer(sentences[0]							,  return_tensors="""tf""").input_ids
				__SCREAMING_SNAKE_CASE            = model.generate(input_ids=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
				    tf.cast(inputs["""attention_mask"""][-1]							,  tf.intaa))
				__SCREAMING_SNAKE_CASE            = tokenizer(sentences[1]							,  return_tensors="""tf""").input_ids
				__SCREAMING_SNAKE_CASE            = model.generate(input_ids=lowerCAmelCase__							,  max_length=model.config.max_length - num_paddings)

				__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.decode(output_non_padded[0]							,  skip_special_tokens=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tokenizer.decode(output_padded[0]							,  skip_special_tokens=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = [
				    """Hello, my dog is a little bit of a dork.\nI'm a little bit""",
				    """Today, I was in the middle of a conversation with a friend about the""",
				]
				self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__)
				self.assertListEqual(lowerCAmelCase__							,  [non_padded_sentence, padded_sentence])




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = """facebook/opt-350m"""

				__SCREAMING_SNAKE_CASE            = [
				    """Today is a beautiful day and I want to""",
				    """In the city of San Francisco, the city""",
				    """Paris is the capital of France and the capital""",
				    """Computers and mobile phones have taken over the""",
				]

				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = GPTaTokenizer.from_pretrained(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = TFOPTForCausalLM.from_pretrained(lowerCAmelCase__)

				for prompt in self.prompts:
						__SCREAMING_SNAKE_CASE            = tokenizer(lowerCAmelCase__							,  return_tensors="""tf""").input_ids

						__SCREAMING_SNAKE_CASE            = model.generate(lowerCAmelCase__							,  max_length=1_0)

						__SCREAMING_SNAKE_CASE            = tokenizer.batch_decode(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)
						predicted_outputs += generated_string

				self.assertListEqual(lowerCAmelCase__							,  lowerCAmelCase__)







"""simple docstring"""





from scipy.stats import spearmanr

import datasets


_a       =							'\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

_a       =							'\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

_a       =							r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {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, {\.I}lhan 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, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'




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







 """simple docstring"""







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




