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

import datasets


lowercase						:      Optional[Any]									= '\\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'

lowercase						:      Optional[int]									= '\\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'

lowercase						:      Optional[int]									= '\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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    List[str])      ->					str:






							'''simple docstring'''





							return float((preds == labels).mean())




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Optional[int])      ->					Dict:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      simple_accuracy(_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase			:     Tuple         =      float(fa_score(y_true=_lowerCamelCase		,						y_pred=_lowerCamelCase))
							return {
							    "accuracy": acc,
							    "f1": fa,
							}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    Optional[int])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      float(pearsonr(_lowerCamelCase		,						_lowerCamelCase)[0])
							__UpperCamelCase			:     Optional[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):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				List[str]:
								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	:Union[str, Any]  ,     a	:List[str]  ,     a	:List[Any]		)	->				Union[str, Any]:
								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\"]"		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase						:      Optional[Any]									= {
    'configuration_blip_2': [
        'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Blip2Config',
        'Blip2QFormerConfig',
        'Blip2VisionConfig',
    ],
    'processing_blip_2': ['Blip2Processor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Tuple									= [
		    'BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Blip2Model',
		    'Blip2QFormerModel',
		    'Blip2PreTrainedModel',
		    'Blip2ForConditionalGeneration',
		    'Blip2VisionModel',
		]

if TYPE_CHECKING:
		from .configuration_blip_a import (
		    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlipaConfig,
		    BlipaQFormerConfig,
		    BlipaVisionConfig,
		)
		from .processing_blip_a import BlipaProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip_a import (
				    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipaForConditionalGeneration,
				    BlipaModel,
				    BlipaPreTrainedModel,
				    BlipaQFormerModel,
				    BlipaVisionModel,
				)

else:
		import sys

		lowercase						:      Dict									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)