


'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


UpperCamelCase__    					=      '''\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
'''

UpperCamelCase__    					=      '''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

UpperCamelCase__    					=      '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	int:
    return float((preds == labels).mean()						)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	str:
    UpperCAmelCase__			:		str													=				simple_accuracy(lowerCAmelCase__  ,					lowerCAmelCase__						)
    UpperCAmelCase__			:		Union[str, Any]													=				float(fa_score(y_true=lowerCAmelCase__  ,					y_pred=lowerCAmelCase__						)						)
    return {
        "accuracy": acc,
        "f1": fa,
    }





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				float(pearsonr(lowerCAmelCase__  ,					lowerCAmelCase__						)[0]						)
    UpperCAmelCase__			:		Union[str, Any]													=				float(spearmanr(lowerCAmelCase__  ,					lowerCAmelCase__						)[0]						)
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
    }







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






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          if self.config_name not in [
              "sst2",
              "mnli",
              "mnli_mismatched",
              "mnli_matched",
              "cola",
              "stsb",
              "mrpc",
              "qqp",
              "qnli",
              "rte",
              "wnli",
              "hans",
          ]:
              raise KeyError(
                  '''You should supply a configuration name selected in '''
                  '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                  '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]''' )
          return datasets.MetricInfo(
              description=_DESCRIPTION						,						citation=_CITATION						,						inputs_description=_KWARGS_DESCRIPTION						,						features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32''' ),
                      '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32''' ),
                  } )						,						codebase_urls=[]						,						reference_urls=[]						,						format='''numpy'''						,						)






      def lowercase_							(					self				:    List[Any]						,						_A				:    Optional[int]						,						_A				:    List[str] ):


          '''simple docstring'''


          if self.config_name == "cola":
              return {"matthews_correlation": matthews_corrcoef(_A						,						_A )}
          elif self.config_name == "stsb":
              return pearson_and_spearman(_A						,						_A )
          elif self.config_name in ["mrpc", "qqp"]:
              return acc_and_fa(_A						,						_A )
          elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
              return {"accuracy": simple_accuracy(_A						,						_A )}
          else:
              raise KeyError(
                  '''You should supply a configuration name selected in '''
                  '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                  '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]''' )




'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
