


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 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Optional[Any]=None    ,      _lowercase      :			Dict=None						):





     '''simple docstring'''






     if attention_mask is None:
          __UpperCAmelCase			:				Dict													=    tf.cast(tf.math.not_equal(_lowercase    ,      config.pad_token_id						)    ,      tf.inta						)
     return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 			a       :


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     OPTConfig
     SCREAMING_SNAKE_CASE :				str							     =     {}
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     "gelu"

     def __init__(       self  :	List[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	List[str]=13							,				snake_case  :	Optional[int]=7							,				snake_case  :	str=True							,				snake_case  :	Optional[Any]=False							,				snake_case  :	Dict=99							,				snake_case  :	Optional[int]=16							,				snake_case  :	Any=2							,				snake_case  :	Dict=4							,				snake_case  :	str=4							,				snake_case  :	List[Any]="gelu"							,				snake_case  :	Dict=0.1							,				snake_case  :	List[Any]=0.1							,				snake_case  :	List[str]=20							,				snake_case  :	List[str]=2							,				snake_case  :	Optional[int]=1							,				snake_case  :	str=0							,				snake_case  :	List[str]=16							,				snake_case  :	Optional[int]=16							,				)		->							int:
          __UpperCAmelCase			:				Dict													=    parent
          __UpperCAmelCase			:				Optional[Any]													=    batch_size
          __UpperCAmelCase			:				Union[str, Any]													=    seq_length
          __UpperCAmelCase			:				str													=    is_training
          __UpperCAmelCase			:				Optional[int]													=    use_labels
          __UpperCAmelCase			:				Tuple													=    vocab_size
          __UpperCAmelCase			:				Any													=    hidden_size
          __UpperCAmelCase			:				int													=    num_hidden_layers
          __UpperCAmelCase			:				Union[str, Any]													=    num_attention_heads
          __UpperCAmelCase			:				Optional[Any]													=    intermediate_size
          __UpperCAmelCase			:				Tuple													=    hidden_act
          __UpperCAmelCase			:				Dict													=    hidden_dropout_prob
          __UpperCAmelCase			:				Any													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				Any													=    max_position_embeddings
          __UpperCAmelCase			:				Optional[int]													=    eos_token_id
          __UpperCAmelCase			:				Optional[int]													=    pad_token_id
          __UpperCAmelCase			:				str													=    bos_token_id
          __UpperCAmelCase			:				List[str]													=    embed_dim
          __UpperCAmelCase			:				Dict													=    word_embed_proj_dim
          __UpperCAmelCase			:				Any													=    False

     def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
          __UpperCAmelCase			:				List[Any]													=    ids_tensor([self.batch_size, self.seq_length - 1]							,				self.vocab_size    )
          __UpperCAmelCase			:				str													=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,				1    )
          __UpperCAmelCase			:				List[str]													=    tf.concat([input_ids, eos_tensor]							,				axis=1    )

          __UpperCAmelCase			:				str													=    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=snake_case							,				**self.config_updates							,				)
          __UpperCAmelCase			:				Any													=    prepare_opt_inputs_dict(snake_case							,				snake_case    )
          return config, inputs_dict






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	List[str]							,				snake_case  :	Optional[Any]    )		->							Dict:
          __UpperCAmelCase			:				Optional[Any]													=    TFOPTModel(config=snake_case    )
          __UpperCAmelCase			:				str													=    inputs_dict['''input_ids''']

          __UpperCAmelCase			:				int													=    input_ids[:1, :]
          __UpperCAmelCase			:				Union[str, Any]													=    inputs_dict['''attention_mask'''][:1, :]
          __UpperCAmelCase			:				Optional[int]													=    1

          # first forward pass
          __UpperCAmelCase			:				Any													=    model(snake_case							,				attention_mask=snake_case							,				use_cache=snake_case    )

          __UpperCAmelCase			:				str													=    outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          __UpperCAmelCase			:				Union[str, Any]													=    ids_tensor((self.batch_size, 3)							,				config.vocab_size    )
          __UpperCAmelCase			:				Union[str, Any]													=    tf.cast(ids_tensor((self.batch_size, 3)							,				2    )							,				tf.inta    )

          # append to next input_ids and
          __UpperCAmelCase			:				Optional[int]													=    tf.concat([input_ids, next_tokens]							,				axis=-1    )
          __UpperCAmelCase			:				Optional[Any]													=    tf.concat([attention_mask, next_attn_mask]							,				axis=-1    )

          __UpperCAmelCase			:				int													=    model(snake_case							,				attention_mask=snake_case    )[0]
          __UpperCAmelCase			:				Optional[Any]													=    model(snake_case							,				attention_mask=snake_case							,				past_key_values=snake_case    )[0]

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

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

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



@require_tf
class 			a       (	_a			,						_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[int]							     =     (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE :				Dict							     =     (TFOPTForCausalLM,) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE :				Any							     =     (
         {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
     )
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     False
     SCREAMING_SNAKE_CASE :				str							     =     False
     SCREAMING_SNAKE_CASE :				Tuple							     =     False
     SCREAMING_SNAKE_CASE :				str							     =     1_0

     def 				lowerCamelCase__				(       self  :	int    )		->							str:
          __UpperCAmelCase			:				Dict													=    TFOPTModelTester(self    )
          __UpperCAmelCase			:				int													=    ConfigTester(self							,				config_class=snake_case    )

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Any:
          self.config_tester.run_common_tests()

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[str]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*snake_case    )






     def 				lowerCamelCase__				(       self  :	Any    )		->							Tuple:
          __UpperCAmelCase			:				Any													=    self.model_tester.prepare_config_and_inputs_for_common()

          def _get_word_embedding_weight(snake_case  :	Optional[int]							,				snake_case  :	int    ):
               if hasattr(snake_case							,				'''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(snake_case							,				'''weight'''    ):
                         return embedding_layer.weight
                    else:
                         return None

          for model_class in self.all_model_classes:
               for size in [config.vocab_size - 10, config.vocab_size + 10]:
                    # build the embeddings
                    __UpperCAmelCase			:				str													=    model_class(config=snake_case    )
                    __UpperCAmelCase			:				Tuple													=    _get_word_embedding_weight(snake_case							,				model.get_input_embeddings()    )
                    __UpperCAmelCase			:				int													=    _get_word_embedding_weight(snake_case							,				model.get_output_embeddings()    )

                    # reshape the embeddings
                    model.resize_token_embeddings(snake_case    )
                    __UpperCAmelCase			:				str													=    _get_word_embedding_weight(snake_case							,				model.get_input_embeddings()    )
                    __UpperCAmelCase			:				List[str]													=    _get_word_embedding_weight(snake_case							,				model.get_output_embeddings()    )

                    # check that the resized embeddings size matches the desired size.
                    __UpperCAmelCase			:				List[Any]													=    size if size is not None else config.vocab_size

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

                    # check that weights remain the same after resizing
                    __UpperCAmelCase			:				str													=    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:
                              __UpperCAmelCase			:				Optional[Any]													=    False
                    self.assertTrue(snake_case    )

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

                         __UpperCAmelCase			:				Union[str, Any]													=    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:
                                   __UpperCAmelCase			:				Dict													=    False
                         self.assertTrue(snake_case    )




def 			_a			(		_lowercase      :			Union[str, Any]						):





     '''simple docstring'''






     return tf.constant(_lowercase    ,      dtype=tf.intaa						)



@require_tf
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Any							     =     9_9

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Dict:
          __UpperCAmelCase			:				str													=    tf.ones((4, 1)							,				dtype=tf.intaa    ) * 2
          __UpperCAmelCase			:				Union[str, Any]													=    tf.concat([ids_tensor((4, 6)							,				self.vocab_size - 3    ) + 3, eos_column_vector]							,				axis=1    )
          __UpperCAmelCase			:				Optional[Any]													=    input_ids.shape[0]
          __UpperCAmelCase			:				Optional[int]													=    OPTConfig(
              vocab_size=self.vocab_size							,				hidden_size=24							,				num_hidden_layers=2							,				num_attention_heads=2							,				ffn_dim=32							,				max_position_embeddings=48							,				eos_token_id=2							,				pad_token_id=1							,				bos_token_id=0							,				)
          return config, input_ids, batch_size



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


     """simple docstring"""






     @slow
     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							str:
          __UpperCAmelCase			:				Union[str, Any]													=    TFOPTModel.from_pretrained('''facebook/opt-350m'''    )
          __UpperCAmelCase			:				Any													=    _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    )
          __UpperCAmelCase			:				int													=    tf.not_equal(snake_case							,				model.config.pad_token_id    )
          with tf.GradientTape():
               __UpperCAmelCase			:				Union[str, Any]													=    model(input_ids=snake_case							,				attention_mask=snake_case    ).last_hidden_state
          __UpperCAmelCase			:				Dict													=    (1, 11, 512)
          self.assertEqual(output.shape							,				snake_case    )
          __UpperCAmelCase			:				Union[str, Any]													=    tf.constant(
              [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]    )
          self.assertTrue(np.allclose(output[:, :3, :3]							,				snake_case							,				atol=4E-3    )    )

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



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


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[Any]:
          super().setUp()
          __UpperCAmelCase			:				List[Any]													=    '''facebook/opt-350m'''






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Union[str, Any]													=    TFOPTForCausalLM.from_pretrained(self.path_model    )
          __UpperCAmelCase			:				List[str]													=    GPTaTokenizer.from_pretrained(self.path_model    )

          __UpperCAmelCase			:				Optional[Any]													=    [
              '''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
          __UpperCAmelCase			:				Optional[Any]													=    tokenizer(snake_case							,				return_tensors='''tf'''							,				padding=snake_case							,				add_special_tokens=snake_case    )
          __UpperCAmelCase			:				Any													=    tf.math.reduce_mean(model(inputs.input_ids							,				attention_mask=inputs.attention_mask    )[0]							,				axis=-1    )
          __UpperCAmelCase			:				Any													=    tf.constant(
              [
                  [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
                  [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
                  [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
                  [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
              ]    )
          self.assertTrue(np.allclose(snake_case							,				snake_case							,				atol=1E-4    )    )

          __UpperCAmelCase			:				str													=    tf.function(snake_case							,				jit_compile=snake_case    )
          __UpperCAmelCase			:				Optional[int]													=    tf.math.reduce_mean(xla_generate(inputs.input_ids							,				attention_mask=inputs.attention_mask    )[0]							,				axis=-1    )
          self.assertTrue(np.allclose(snake_case							,				snake_case							,				atol=1E-4    )    )



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


     """simple docstring"""






     @property
     def 				lowerCamelCase__				(       self  :	int    )		->							Dict:
          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 				lowerCamelCase__				(       self  :	Any    )		->							Tuple:
          __UpperCAmelCase			:				List[Any]													=    '''facebook/opt-125m'''

          __UpperCAmelCase			:				str													=    [
              '''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''',
          ]

          __UpperCAmelCase			:				Dict													=    []
          __UpperCAmelCase			:				List[str]													=    GPTaTokenizer.from_pretrained(snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    TFOPTForCausalLM.from_pretrained(snake_case    )

          for prompt in self.prompts:
               __UpperCAmelCase			:				List[Any]													=    tokenizer(snake_case							,				return_tensors='''tf'''    ).input_ids

               __UpperCAmelCase			:				str													=    model.generate(snake_case							,				max_length=10    )

               __UpperCAmelCase			:				int													=    tokenizer.batch_decode(snake_case							,				skip_special_tokens=snake_case    )
               predicted_outputs += generated_string

          self.assertListEqual(snake_case							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Any:
          __UpperCAmelCase			:				Union[str, Any]													=    '''facebook/opt-350m'''

          __UpperCAmelCase			:				List[Any]													=    GPTaTokenizer.from_pretrained(snake_case    )
          __UpperCAmelCase			:				List[str]													=    TFOPTForCausalLM.from_pretrained(snake_case    )

          __UpperCAmelCase			:				Dict													=    '''left'''

          # use different length sentences to test batching
          __UpperCAmelCase			:				Tuple													=    [
              '''Hello, my dog is a little''',
              '''Today, I''',
          ]

          __UpperCAmelCase			:				Any													=    tokenizer(snake_case							,				return_tensors='''tf'''							,				padding=snake_case    )
          __UpperCAmelCase			:				List[Any]													=    inputs['''input_ids''']

          __UpperCAmelCase			:				Dict													=    model.generate(input_ids=snake_case							,				attention_mask=inputs['''attention_mask''']    )

          __UpperCAmelCase			:				int													=    tokenizer(sentences[0]							,				return_tensors='''tf'''    ).input_ids
          __UpperCAmelCase			:				str													=    model.generate(input_ids=snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    inputs_non_padded.shape[-1] - tf.math.reduce_sum(
              tf.cast(inputs['''attention_mask'''][-1]							,				tf.intaa    )    )
          __UpperCAmelCase			:				List[str]													=    tokenizer(sentences[1]							,				return_tensors='''tf'''    ).input_ids
          __UpperCAmelCase			:				List[str]													=    model.generate(input_ids=snake_case							,				max_length=model.config.max_length - num_paddings    )

          __UpperCAmelCase			:				List[str]													=    tokenizer.batch_decode(snake_case							,				skip_special_tokens=snake_case    )
          __UpperCAmelCase			:				Tuple													=    tokenizer.decode(output_non_padded[0]							,				skip_special_tokens=snake_case    )
          __UpperCAmelCase			:				List[Any]													=    tokenizer.decode(output_padded[0]							,				skip_special_tokens=snake_case    )

          __UpperCAmelCase			:				str													=    [
              '''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(snake_case							,				snake_case    )
          self.assertListEqual(snake_case							,				[non_padded_sentence, padded_sentence]    )






     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							List[str]:
          __UpperCAmelCase			:				Union[str, Any]													=    '''facebook/opt-350m'''

          __UpperCAmelCase			:				Union[str, Any]													=    [
              '''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''',
          ]

          __UpperCAmelCase			:				Optional[Any]													=    []
          __UpperCAmelCase			:				List[Any]													=    GPTaTokenizer.from_pretrained(snake_case    )
          __UpperCAmelCase			:				List[str]													=    TFOPTForCausalLM.from_pretrained(snake_case    )

          for prompt in self.prompts:
               __UpperCAmelCase			:				int													=    tokenizer(snake_case							,				return_tensors='''tf'''    ).input_ids

               __UpperCAmelCase			:				Any													=    model.generate(snake_case							,				max_length=10    )

               __UpperCAmelCase			:				str													=    tokenizer.batch_decode(snake_case							,				skip_special_tokens=snake_case    )
               predicted_outputs += generated_string

          self.assertListEqual(snake_case							,				snake_case    )



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\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    :List[str]				       =     "\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    :List[Any]				       =     "\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 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          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 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}