





'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class 							__lowercase       (      __lowercase ,      __lowercase						):







 '''simple docstring'''

 a    :				Any											=       '''nat'''

 a    :				List[str]											=       {
     '''num_attention_heads''': '''num_heads''',
     '''num_hidden_layers''': '''num_layers''',
 }


 def __init__(self     ,_lowerCamelCase=4     ,_lowerCamelCase=3     ,_lowerCamelCase=64     ,_lowerCamelCase=[3, 4, 6, 5]     ,_lowerCamelCase=[2, 4, 8, 16]     ,_lowerCamelCase=7     ,_lowerCamelCase=3.0     ,_lowerCamelCase=True     ,_lowerCamelCase=0.0     ,_lowerCamelCase=0.0     ,_lowerCamelCase=0.1     ,_lowerCamelCase="gelu"     ,_lowerCamelCase=0.0_2     ,_lowerCamelCase=1E-5     ,_lowerCamelCase=0.0     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,**_lowerCamelCase     ,)							->							List[str]:







    '''simple docstring'''





    super().__init__(**__a )

    __lowercase										=							patch_size
    __lowercase										=							num_channels
    __lowercase										=							embed_dim
    __lowercase										=							depths
    __lowercase										=							len(__a )
    __lowercase										=							num_heads
    __lowercase										=							kernel_size
    __lowercase										=							mlp_ratio
    __lowercase										=							qkv_bias
    __lowercase										=							hidden_dropout_prob
    __lowercase										=							attention_probs_dropout_prob
    __lowercase										=							drop_path_rate
    __lowercase										=							hidden_act
    __lowercase										=							layer_norm_eps
    __lowercase										=							initializer_range
    # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    __lowercase										=							int(embed_dim * 2 ** (len(__a ) - 1) )
    __lowercase										=							layer_scale_init_value
    __lowercase										=							["""stem"""] + [f"stage{idx}" for idx in range(1     ,len(__a ) + 1 )]
    __lowercase										=							get_aligned_output_features_output_indices(
        out_features=__a     ,out_indices=__a     ,stage_names=self.stage_names )












'''simple docstring'''



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

import datasets


_SCREAMING_SNAKE_CASE					=    '''\
@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}
}
'''

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

_SCREAMING_SNAKE_CASE					=    '''
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    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  int				):
			return float((preds == labels).mean()				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str				):
			__lowercase										=							simple_accuracy(lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase										=							float(fa_score(y_true=lowerCamelCase_		,       y_pred=lowerCamelCase_				)				)
			return {
			    "accuracy": acc,
			    "f1": fa,
			}




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Any				):
			__lowercase										=							float(pearsonr(lowerCamelCase_		,       lowerCamelCase_				)[0]				)
			__lowercase										=							float(spearmanr(lowerCamelCase_		,       lowerCamelCase_				)[0]				)
			return {
			    "pearson": pearson_corr,
			    "spearmanr": spearman_corr,
			}






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







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							str:







				'''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 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				if self.config_name == "cola":
							return {"matthews_correlation": matthews_corrcoef(_lowerCamelCase     ,_lowerCamelCase )}
				elif self.config_name == "stsb":
							return pearson_and_spearman(_lowerCamelCase     ,_lowerCamelCase )
				elif self.config_name in ["mrpc", "qqp"]:
							return acc_and_fa(_lowerCamelCase     ,_lowerCamelCase )
				elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
							return {"accuracy": simple_accuracy(_lowerCamelCase     ,_lowerCamelCase )}
				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"]''' )





