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

import datasets


UpperCamelCase_   	=	"\\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"

UpperCamelCase_   	=	"\\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"

UpperCamelCase_   	=	"\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 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


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






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	__lowerCAmelCase         =							simple_accuracy(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							float(fa_score(y_true=UpperCamelCase			,							y_pred=UpperCamelCase  )  )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}






def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


	__lowerCAmelCase         =							float(pearsonr(UpperCamelCase			,							UpperCamelCase  )[0]  )
	__lowerCAmelCase         =							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       UpperCAmelCase__					(						self  :  int ):
								"""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  :  Any      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								if self.config_name == "cola":
									return {"matthews_correlation": matthews_corrcoef(snake_case__      ,				snake_case__ )}
								elif self.config_name == "stsb":
									return pearson_and_spearman(snake_case__      ,				snake_case__ )
								elif self.config_name in ["mrpc", "qqp"]:
									return acc_and_fa(snake_case__      ,				snake_case__ )
								elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
									return {"accuracy": simple_accuracy(snake_case__      ,				snake_case__ )}
								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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						str   							=  'distilbert'
							lowercase_		:						Any   							=  {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(						self  :  Optional[int]      ,				snake_case__  :  int=30_522      ,				snake_case__  :  str=512      ,				snake_case__  :  Tuple=False      ,				snake_case__  :  Tuple=6      ,				snake_case__  :  Any=12      ,				snake_case__  :  Dict=768      ,				snake_case__  :  Any=4 * 768      ,				snake_case__  :  Union[str, Any]=0.1      ,				snake_case__  :  str=0.1      ,				snake_case__  :  Tuple="gelu"      ,				snake_case__  :  str=0.0_2      ,				snake_case__  :  Optional[Any]=0.1      ,				snake_case__  :  Optional[Any]=0.2      ,				snake_case__  :  str=0      ,				**snake_case__  :  Dict      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							sinusoidal_pos_embds
								__lowerCAmelCase         =							n_layers
								__lowerCAmelCase         =							n_heads
								__lowerCAmelCase         =							dim
								__lowerCAmelCase         =							hidden_dim
								__lowerCAmelCase         =							dropout
								__lowerCAmelCase         =							attention_dropout
								__lowerCAmelCase         =							activation
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							qa_dropout
								__lowerCAmelCase         =							seq_classif_dropout
								super().__init__(**snake_case__      ,				pad_token_id=snake_case__ )

class       a		(   __UpperCAmelCase					):







							@property
							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								if self.task == "multiple-choice":
									__lowerCAmelCase         =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
									__lowerCAmelCase         =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ] )






