



'''simple docstring'''





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

import datasets


snake_case__					:     int    		=   """\
@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}
}
"""

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

snake_case__					:     Any    		=   """
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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	return float((preds == labels).mean()   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			simple_accuracy(_lowerCamelCase  ,	_lowerCamelCase   )
	__lowercase		       =			float(fa_score(y_true=_lowerCamelCase  ,	y_pred=_lowerCamelCase   )   )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__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   _A   ( datasets.Metric	):


						'''simple docstring'''





						def     _snake_case	(	self	:							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     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							int							,      lowerCamelCase	:							Tuple	):






							'''simple docstring'''



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








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


snake_case__					:     Any    		=   logging.get_logger(__name__)

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """maskformer-swin"""

						_snake_case			:     List[str]      							=      {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any=224							,      lowerCamelCase	:							Optional[Any]=4							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							Tuple=96							,      lowerCamelCase	:							str=[2, 2, 6, 2]							,      lowerCamelCase	:							Dict=[3, 6, 12, 24]							,      lowerCamelCase	:							Optional[Any]=7							,      lowerCamelCase	:							Any=4.0							,      lowerCamelCase	:							Union[str, Any]=True							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							Optional[int]=0.0							,      lowerCamelCase	:							List[str]=0.1							,      lowerCamelCase	:							int="gelu"							,      lowerCamelCase	:							Optional[int]=False							,      lowerCamelCase	:							List[Any]=0.02							,      lowerCamelCase	:							Tuple=1e-5							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=None							,      **lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

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



