




'''simple docstring'''


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

import datasets


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

_snake_case				:				str       							=		'\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

_snake_case				:				Union[str, Any]       							=		'\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'
def 		snake_case_   (UpperCamelCase	:  str			,						UpperCamelCase	:  Tuple				):





							'''simple docstring'''


							return float((preds == labels).mean()				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


							_a     					=    simple_accuracy(UpperCamelCase			,						UpperCamelCase				)
							_a     					=    float(fa_score(y_true=UpperCamelCase			,						y_pred=UpperCamelCase				)				)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    float(pearsonr(UpperCamelCase			,						UpperCamelCase				)[0]				)
							_a     					=    float(spearmanr(UpperCamelCase			,						UpperCamelCase				)[0]				)
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}

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







							def        __lowerCAmelCase      (  self	:  int )      ->							Tuple:


														"""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        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  str )      ->							List[Any]:


														"""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"]''' )







'''simple docstring'''


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


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    '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       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







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


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    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
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

