





from scipy.stats import pearsonr

import datasets


_UpperCAmelCase							:			Tuple        =							"""
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
"""


_UpperCAmelCase							:			Optional[int]        =							"""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


_UpperCAmelCase							:			Union[str, Any]        =							"""
@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, Ilhan 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, Antonio 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   lowerCAmelCase				(   datasets.Metric     ):
 def 	A_      ( self    : Union[str, Any]      )     ->      Tuple:
     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 	A_      ( self    : Any     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Tuple     ,	UpperCAmelCase    : Tuple=False      )     ->      str:
     if return_pvalue:
         lowerCamelCase__       : List[Any]   					=						pearsonr(UpperCAmelCase     ,	UpperCAmelCase      )
         return {"pearsonr": results[0], "p-value": results[1]}
     else:
         return {"pearsonr": float(pearsonr(UpperCAmelCase     ,	UpperCAmelCase      )[0]      )}












import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
    import torch

class   lowerCAmelCase				:
 def __init__( self    : List[str]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : str=14     ,	UpperCAmelCase    : Any=7     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Any=False     ,	UpperCAmelCase    : Tuple=True     ,	UpperCAmelCase    : int=99     ,	UpperCAmelCase    : str=32     ,	UpperCAmelCase    : int=4     ,	UpperCAmelCase    : Optional[Any]=4     ,	UpperCAmelCase    : Any=4     ,	UpperCAmelCase    : Optional[int]=37     ,	UpperCAmelCase    : Union[str, Any]="gelu"     ,	UpperCAmelCase    : List[Any]=0.1     ,	UpperCAmelCase    : Dict=0.1     ,	UpperCAmelCase    : Optional[int]=512     ,	UpperCAmelCase    : List[Any]=0.0_2     ,	)     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						parent
     lowerCamelCase__       : Tuple   					=						batch_size
     lowerCamelCase__       : Optional[Any]   					=						seq_length
     lowerCamelCase__       : str   					=						is_training
     lowerCamelCase__       : Any   					=						use_input_mask
     lowerCamelCase__       : Optional[Any]   					=						use_token_type_ids
     lowerCamelCase__       : Optional[Any]   					=						use_labels
     lowerCamelCase__       : Optional[Any]   					=						vocab_size
     lowerCamelCase__       : Optional[int]   					=						hidden_size
     lowerCamelCase__       : Optional[Any]   					=						rotary_dim
     lowerCamelCase__       : Optional[Any]   					=						num_hidden_layers
     lowerCamelCase__       : Optional[Any]   					=						num_attention_heads
     lowerCamelCase__       : Tuple   					=						intermediate_size
     lowerCamelCase__       : Union[str, Any]   					=						hidden_act
     lowerCamelCase__       : List[Any]   					=						hidden_dropout_prob
     lowerCamelCase__       : int   					=						attention_probs_dropout_prob
     lowerCamelCase__       : Dict   					=						max_position_embeddings
     lowerCamelCase__       : Tuple   					=						initializer_range
     lowerCamelCase__       : Optional[int]   					=						None
     lowerCamelCase__       : int   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
 def 	A_      ( self    : str      )     ->      int:
     lowerCamelCase__       : Optional[int]   					=						ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

     lowerCamelCase__       : Optional[Any]   					=						None
     if self.use_input_mask:
         lowerCamelCase__       : List[str]   					=						random_attention_mask([self.batch_size, self.seq_length]      )

     lowerCamelCase__       : Tuple   					=						GPTJConfig(
         vocab_size=self.vocab_size     ,	n_embd=self.hidden_size     ,	n_layer=self.num_hidden_layers     ,	n_head=self.num_attention_heads     ,	n_positions=self.max_position_embeddings     ,	use_cache=UpperCAmelCase     ,	bos_token_id=self.bos_token_id     ,	eos_token_id=self.eos_token_id     ,	pad_token_id=self.pad_token_id     ,	rotary_dim=self.rotary_dim     ,	)

     return (config, input_ids, input_mask)
 def 	A_      ( self    : Optional[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : Tuple   					=						self.prepare_config_and_inputs()
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						config_and_inputs
     lowerCamelCase__       : List[Any]   					=						{'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict
 def 	A_      ( self    : int     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Tuple   					=						20
     lowerCamelCase__       : Dict   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : int   					=						jnp.ones((input_ids.shape[0], max_decoder_length)     ,	dtype='i4'      )

     lowerCamelCase__       : List[str]   					=						jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]     ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )
     lowerCamelCase__       : Optional[int]   					=						model(
         input_ids[:, :-1]     ,	attention_mask=UpperCAmelCase     ,	past_key_values=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,	dtype='i4'      )
     lowerCamelCase__       : List[str]   					=						model(
         input_ids[:, -1:]     ,	attention_mask=UpperCAmelCase     ,	past_key_values=outputs_cache.past_key_values     ,	position_ids=UpperCAmelCase     ,	)

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
     self.parent.assertTrue(diff < 1e-3     ,	msg=F"""Max diff is {diff}"""      )







 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : List[str]      )     ->      Optional[Any]:
     lowerCamelCase__       : Any   					=						20
     lowerCamelCase__       : Any   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						jnp.concatenate(
         [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]     ,	axis=-1     ,	)

     lowerCamelCase__       : str   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]     ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )

     lowerCamelCase__       : List[Any]   					=						model(
         input_ids[:, :-1]     ,	attention_mask=UpperCAmelCase     ,	past_key_values=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	)
     lowerCamelCase__       : Optional[Any]   					=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,	dtype='i4'      )
     lowerCamelCase__       : List[Any]   					=						model(
         input_ids[:, -1:]     ,	past_key_values=outputs_cache.past_key_values     ,	attention_mask=UpperCAmelCase     ,	position_ids=UpperCAmelCase     ,	)

     lowerCamelCase__       : List[Any]   					=						model(UpperCAmelCase     ,	attention_mask=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
     self.parent.assertTrue(diff < 1e-3     ,	msg=F"""Max diff is {diff}"""      )


@require_flax
class   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCAmelCase__						=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
 def 	A_      ( self    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Optional[Any]   					=						FlaxGPTJModelTester(self      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]      )     ->      Tuple:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : str   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 @tooslow
 def 	A_      ( self    : Dict      )     ->      Tuple:
     lowerCamelCase__       : str   					=						GPTaTokenizer.from_pretrained('gpt2'     ,	pad_token='<|endoftext|>'     ,	padding_side='left'      )
     lowerCamelCase__       : Dict   					=						tokenizer(['Hello this is a long string', 'Hey']     ,	return_tensors='np'     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
     lowerCamelCase__       : Optional[int]   					=						False
     lowerCamelCase__       : Optional[int]   					=						model.config.eos_token_id

     lowerCamelCase__       : Union[str, Any]   					=						jax.jit(model.generate      )

     lowerCamelCase__       : Optional[Any]   					=						jit_generate(
         inputs['input_ids']     ,	attention_mask=inputs['attention_mask']     ,	pad_token_id=tokenizer.pad_token_id      ).sequences

     lowerCamelCase__       : Optional[int]   					=						tokenizer.batch_decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase      )

     lowerCamelCase__       : int   					=						[
         'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
         'Hey, I\'m a little late to the party. I\'m going to',
     ]

     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )
 @is_pt_flax_cross_test
 def 	A_      ( self    : List[str]      )     ->      Union[str, Any]:
     lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__      ):
             # prepare inputs
             lowerCamelCase__       : Dict   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Optional[int]   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Any   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : Union[str, Any]   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

             lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						pt_inputs['input_ids'].shape
             lowerCamelCase__       : Tuple   					=						np.random.randint(0     ,	seq_length - 1     ,	size=(batch_size,)      )
             for batch_idx, start_index in enumerate(UpperCAmelCase      ):
                 lowerCamelCase__       : List[str]   					=						0
                 lowerCamelCase__       : Optional[int]   					=						1
                 lowerCamelCase__       : Any   					=						0
                 lowerCamelCase__       : Any   					=						1
             lowerCamelCase__       : Dict   					=						pt_model_class(UpperCAmelCase      ).eval()
             lowerCamelCase__       : str   					=						model_class(UpperCAmelCase     ,	dtype=jnp.floataa      )

             lowerCamelCase__       : str   					=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						fx_state

             with torch.no_grad():
                 lowerCamelCase__       : List[str]   					=						pt_model(**UpperCAmelCase      ).to_tuple()

             lowerCamelCase__       : Dict   					=						fx_model(**UpperCAmelCase      ).to_tuple()
             self.assertEqual(len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(UpperCAmelCase      )
                 lowerCamelCase__       : Tuple   					=						model_class.from_pretrained(UpperCAmelCase     ,	from_pt=UpperCAmelCase      )

             lowerCamelCase__       : int   					=						fx_model_loaded(**UpperCAmelCase      ).to_tuple()
             self.assertEqual(
                 len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output_loaded, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output_loaded[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )
 @is_pt_flax_cross_test
 def 	A_      ( self    : Union[str, Any]      )     ->      int:
     lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__      ):
             # prepare inputs
             lowerCamelCase__       : Tuple   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Union[str, Any]   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : int   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

             lowerCamelCase__       : List[Any]   					=						pt_model_class(UpperCAmelCase      ).eval()
             lowerCamelCase__       : str   					=						model_class(UpperCAmelCase     ,	dtype=jnp.floataa      )

             lowerCamelCase__       : List[str]   					=						load_flax_weights_in_pytorch_model(UpperCAmelCase     ,	fx_model.params      )
             lowerCamelCase__					,				lowerCamelCase__       : Union[str, Any]   					=						pt_inputs['input_ids'].shape
             lowerCamelCase__       : Dict   					=						np.random.randint(0     ,	seq_length - 1     ,	size=(batch_size,)      )
             for batch_idx, start_index in enumerate(UpperCAmelCase      ):
                 lowerCamelCase__       : List[Any]   					=						0
                 lowerCamelCase__       : Tuple   					=						1
                 lowerCamelCase__       : Optional[Any]   					=						0
                 lowerCamelCase__       : List[str]   					=						1

             # make sure weights are tied in PyTorch
             pt_model.tie_weights()

             with torch.no_grad():
                 lowerCamelCase__       : Any   					=						pt_model(**UpperCAmelCase      ).to_tuple()

             lowerCamelCase__       : List[Any]   					=						fx_model(**UpperCAmelCase      ).to_tuple()
             self.assertEqual(len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(UpperCAmelCase      )
                 lowerCamelCase__       : Tuple   					=						pt_model_class.from_pretrained(UpperCAmelCase     ,	from_flax=UpperCAmelCase      )

             with torch.no_grad():
                 lowerCamelCase__       : Optional[int]   					=						pt_model_loaded(**UpperCAmelCase      ).to_tuple()

             self.assertEqual(
                 len(UpperCAmelCase      )     ,	len(UpperCAmelCase      )     ,	'Output lengths differ between Flax and PyTorch'      )
             for fx_output, pt_output in zip(UpperCAmelCase     ,	UpperCAmelCase      ):
                 self.assert_almost_equals(fx_output[:, -1]     ,	pt_output[:, -1].numpy()     ,	4e-2      )







 @tooslow
 def 	A_      ( self    : List[str]      )     ->      List[Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__       : Union[str, Any]   					=						model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
         lowerCamelCase__       : Any   					=						model(np.ones((1, 1)      )      )
         self.assertIsNotNone(UpperCAmelCase      )





