


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

import datasets


SCREAMING_SNAKE_CASE       :      Optional[int]												= "\\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"

SCREAMING_SNAKE_CASE       :      List[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"

SCREAMING_SNAKE_CASE       :      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 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						int:
    return float((preds == labels).mean() )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Tuple:
    _lowercase       :							Optional[int]      		=		simple_accuracy(lowerCamelCase_							, lowerCamelCase_ )
    _lowercase       :							Any      		=		float(fa_score(y_true=lowerCamelCase_							, y_pred=lowerCamelCase_ ) )
    return {
        "accuracy": acc,
        "f1": fa,
    }



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    _lowercase       :							Tuple      		=		float(pearsonr(lowerCamelCase_							, lowerCamelCase_ )[0] )
    _lowercase       :							Optional[int]      		=		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   ):
       def 			UpperCamelCase     (					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 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   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"]')










import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Optional[Any]			     = {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	'mvp'
      a  : Optional[Any]							       =	['past_key_values']
      a  : int							       =	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
      def __init__(      self					:			Dict		,      __lowercase					:			Tuple=50267		,      __lowercase					:			Optional[int]=1024		,      __lowercase					:			Any=12		,      __lowercase					:			List[Any]=4096		,      __lowercase					:			Optional[Any]=16		,      __lowercase					:			List[str]=12		,      __lowercase					:			Optional[Any]=4096		,      __lowercase					:			Tuple=16		,      __lowercase					:			Union[str, Any]=0.0		,      __lowercase					:			str=0.0		,      __lowercase					:			Any="gelu"		,      __lowercase					:			Any=1024		,      __lowercase					:			List[str]=0.1		,      __lowercase					:			List[Any]=0.0		,      __lowercase					:			int=0.0		,      __lowercase					:			str=0.02		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Union[str, Any]=False		,      __lowercase					:			Dict=True		,      __lowercase					:			List[Any]=1		,      __lowercase					:			Optional[Any]=0		,      __lowercase					:			Union[str, Any]=2		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Dict=2		,      __lowercase					:			int=2		,      __lowercase					:			Union[str, Any]=False		,      __lowercase					:			Union[str, Any]=100		,      __lowercase					:			str=800		,      **__lowercase					:			Union[str, Any]		,      )							->      Any:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Union[str, Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	int           =  encoder_ffn_dim
        __UpperCAmelCase							:	Tuple           =  encoder_layers
        __UpperCAmelCase							:	List[str]           =  encoder_attention_heads
        __UpperCAmelCase							:	Any           =  decoder_ffn_dim
        __UpperCAmelCase							:	List[Any]           =  decoder_layers
        __UpperCAmelCase							:	str           =  decoder_attention_heads
        __UpperCAmelCase							:	Optional[int]           =  dropout
        __UpperCAmelCase							:	Tuple           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	List[Any]           =  activation_function
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	Any           =  encoder_layerdrop
        __UpperCAmelCase							:	Union[str, Any]           =  decoder_layerdrop
        __UpperCAmelCase							:	List[Any]           =  classifier_dropout
        __UpperCAmelCase							:	Optional[int]           =  use_cache
        __UpperCAmelCase							:	int           =  encoder_layers
        __UpperCAmelCase							:	Union[str, Any]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Tuple           =  use_prompt
        __UpperCAmelCase							:	str           =  prompt_length
        __UpperCAmelCase							:	Union[str, Any]           =  prompt_mid_dim

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      is_encoder_decoder=__lowercase		,      decoder_start_token_id=__lowercase		,      forced_eos_token_id=__lowercase		,      **__lowercase		,      )

        if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""		,      __lowercase					):
          __UpperCAmelCase							:	Optional[Any]           =  self.bos_token_id
          warnings.warn(
              f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
              """The config can simply be saved and uploaded again to be fixed."""					)


