





from sklearn.metrics import matthews_corrcoef

import datasets


lowerCAmelCase_        =							'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

lowerCAmelCase_        =							'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

lowerCAmelCase_        =							'''\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''



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






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int32'''       ),
							            '''references''': datasets.Value('''int32'''       ),
							        }       )   ,				reference_urls=[
							        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
							    ]   ,				)






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__=None       )	->  int:


							'''simple docstring'''



							return {
							    "matthews_correlation": float(matthews_corrcoef(__magic_name__   ,				__magic_name__   ,				sample_weight=__magic_name__       )       ),
							}





'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


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



@slow
@require_flax
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							int      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return f'''gaussian_noise_s={seed}_shape={"_".join([str(__a  ) for s in shape]  )}.npy'''
 def A_   (   self						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def A_   (   self						:							List[Any]      ,     __a						:							Union[str, Any]=0      ,     __a						:							List[str]=(4, 4, 64, 64)      ,     __a						:							Optional[Any]=False  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   List[Any]													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return image
 def A_   (   self						:							Any      ,     __a						:							Any=False      ,     __a						:							Dict="CompVis/stable-diffusion-v1-4"  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   int													=    'bf16' if fpaa else None

       __snake_case							,     __snake_case :   Union[str, Any]													=    FlaxUNetaDConditionModel.from_pretrained(
           __a      ,     subfolder='unet'      ,     dtype=__a      ,     revision=__a  )
       return model, params
 def A_   (   self						:							Any      ,     __a						:							Dict=0      ,     __a						:							Dict=(4, 77, 768)      ,     __a						:							List[str]=False  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   Any													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return hidden_states
 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
         [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
         [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
         [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
         # fmt: on
     ]  )
 def A_   (   self						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,     fpaa=__a  )
       __snake_case :   Tuple													=    self.get_latents(__a      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_encoder_hidden_states(__a      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   str													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )





 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
         [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
         [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
         [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
         # fmt: on
     ]  )
 def A_   (   self						:							str      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_latents(__a      ,     shape=(4, 4, 96, 96)      ,     fpaa=__a  )
       __snake_case :   Optional[Any]													=    self.get_encoder_hidden_states(__a      ,     shape=(4, 77, 1024)      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   int													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )



