





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

import datasets


lowercase				:	str      						=  """\
@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}
}
"""

lowercase				:	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.
"""

lowercase				:	List[str]      						=  """
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 A_     (		A__		,				A__    )  ->		str:
       return float((preds == labels).mean()    )



def A_     (		A__		,				A__    )  ->		List[Any]:
       a__						:     Optional[int]			=    simple_accuracy(A__		,				A__    )
       a__						:     Tuple			=    float(fa_score(y_true=A__		,				y_pred=A__    )    )
       return {
           "accuracy": acc,
           "f1": fa,
       }



def A_     (		A__		,				A__    )  ->		List[Any]:
       a__						:     str			=    float(pearsonr(A__		,				A__    )[0]    )
       a__						:     List[str]			=    float(spearmanr(A__		,				A__    )[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 							__lowercase		( self)      ->						Dict:







          '''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 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowercase				:	List[Any]      						=  logging.get_logger(__name__)


lowercase				:	Any      						=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowercase				:	Optional[Any]      						=  {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

lowercase				:	List[str]      						=  {
    """RUCAIBox/mvp""": 1_0_2_4,
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Dict	  =  VOCAB_FILES_NAMES
   __A       :    str	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    Tuple	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Any	  =  ['''input_ids''', '''attention_mask''']
   __A       :    Tuple	  =  MvpTokenizer

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase="replace" ,  lowercase="<s>" ,  lowercase="</s>" ,  lowercase="</s>" ,  lowercase="<s>" ,  lowercase="<unk>" ,  lowercase="<pad>" ,  lowercase="<mask>" ,  lowercase=False ,  lowercase=True ,  **lowercase ,  )      ->						str:







          '''simple docstring'''

          super().__init__(
              lowercase ,  lowercase ,  tokenizer_file=lowercase ,  errors=lowercase ,  bos_token=lowercase ,  eos_token=lowercase ,  sep_token=lowercase ,  cls_token=lowercase ,  unk_token=lowercase ,  pad_token=lowercase ,  mask_token=lowercase ,  add_prefix_space=lowercase ,  trim_offsets=lowercase ,  **lowercase ,  )

          a__						:     Optional[int]			=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('add_prefix_space' ,  lowercase) != add_prefix_space:
                 a__						:     Dict			=    getattr(lowercase ,  pre_tok_state.pop('type'))
                 a__						:     str			=    add_prefix_space
                 a__						:     Union[str, Any]			=    pre_tok_class(**lowercase)

          a__						:     List[Any]			=    add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          a__						:     Optional[int]			=    'post_processor'
          a__						:     Optional[int]			=    getattr(self.backend_tokenizer ,  lowercase ,  lowercase)
          if tokenizer_component_instance:
                 a__						:     str			=    json.loads(tokenizer_component_instance.__getstate__())

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                        a__						:     Any			=    tuple(state['sep'])
                 if "cls" in state:
                        a__						:     str			=    tuple(state['cls'])

                 a__						:     List[str]			=    False

                 if state.get('add_prefix_space' ,  lowercase) != add_prefix_space:
                        a__						:     List[str]			=    add_prefix_space
                        a__						:     List[str]			=    True

                 if state.get('trim_offsets' ,  lowercase) != trim_offsets:
                        a__						:     Optional[int]			=    trim_offsets
                        a__						:     List[str]			=    True

                 if changes_to_apply:
                        a__						:     Optional[int]			=    getattr(lowercase ,  state.pop('type'))
                        a__						:     Tuple			=    component_class(**lowercase)
                        setattr(self.backend_tokenizer ,  lowercase ,  lowercase)

   @property
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          if self._mask_token is None:
                 if self.verbose:
                        logger.error('Using mask_token, but it is not set yet.')
                 return None
          return str(self._mask_token)

   @mask_token.setter
   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    AddedToken(lowercase ,  lstrip=lowercase ,  rstrip=lowercase) if isinstance(lowercase ,  lowercase) else value
          a__						:     str			=    value

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     Optional[Any]			=    kwargs.get('is_split_into_words' ,  lowercase)

          if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                     'to use it with pretokenized inputs.')

          return super()._batch_encode_plus(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     List[str]			=    kwargs.get('is_split_into_words' ,  lowercase)

          if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                     'to use it with pretokenized inputs.')

          return super()._encode_plus(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

          a__						:     List[str]			=    self._tokenizer.model.save(lowercase ,  name=lowercase)
          return tuple(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase=None)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     List[Any]			=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                 return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          a__						:     int			=    [self.sep_token_id]
          a__						:     Any			=    [self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


