

'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 lowercase__			    =			None

lowercase__			    =			logging.get_logger(__name__)
lowercase__			    =			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase__			    =			{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__			    =			{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


lowercase__			    =			"▁"




class        A_ (				_UpperCAmelCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	Tuple  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:	Union[str, Any]  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:	Tuple  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:	int  		=  BigBirdTokenizer
     UpperCAmelCase_						:	List[Any]  		=  ["""input_ids""", """attention_mask"""]
     UpperCAmelCase_						:	Union[str, Any]  		=  []


     def __init__(		self :     str     ,			lowercase_ :     List[Any]=None     ,			lowercase_ :     Union[str, Any]=None     ,			lowercase_ :     Dict="<unk>"     ,			lowercase_ :     Dict="<s>"     ,			lowercase_ :     Dict="</s>"     ,			lowercase_ :     List[str]="<pad>"     ,			lowercase_ :     Optional[Any]="[SEP]"     ,			lowercase_ :     List[Any]="[MASK]"     ,			lowercase_ :     str="[CLS]"     ,			**lowercase_ :     str     ,			)						->	Any:
      UpperCAmelCase       :	Optional[int]											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else bos_token
      UpperCAmelCase       :	Tuple											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else eos_token
      UpperCAmelCase       :	str											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else unk_token
      UpperCAmelCase       :	Any											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else pad_token
      UpperCAmelCase       :	Optional[Any]											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else cls_token
      UpperCAmelCase       :	Any											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCAmelCase       :	Union[str, Any]											= AddedToken(lowercase_     ,			lstrip=lowercase_     ,			rstrip=lowercase_       ) if isinstance(lowercase_     ,			lowercase_       ) else mask_token

      super().__init__(
          lowercase_     ,			tokenizer_file=lowercase_     ,			bos_token=lowercase_     ,			eos_token=lowercase_     ,			unk_token=lowercase_     ,			sep_token=lowercase_     ,			pad_token=lowercase_     ,			cls_token=lowercase_     ,			mask_token=lowercase_     ,			**lowercase_     ,			)

      UpperCAmelCase       :	List[str]											= vocab_file
      UpperCAmelCase       :	Optional[Any]											= False if not self.vocab_file else True


     def    UpperCAmelCase_       (		self :     Optional[Any]     ,			lowercase_ :     str     ,			lowercase_ :     List[str] = None       )						->	List[int]:
      UpperCAmelCase       :	List[str]											= [self.sep_token_id]
      UpperCAmelCase       :	List[str]											= [self.cls_token_id]
      if token_ids_a is None:
       return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep


     def    UpperCAmelCase_       (		self :     Any     ,			lowercase_ :     List[str]     ,			lowercase_ :     Optional[int] = None     ,			lowercase_ :     Dict = False       )						->	List[int]:

      if already_has_special_tokens:
       if token_ids_a is not None:
        raise ValueError(
            'You should not supply a second sequence if the provided sequence of '
            'ids is already formatted with special tokens for the model.'       )
       return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is None:
       return [1] + ([0] * len(lowercase_       )) + [1]
      return [1] + ([0] * len(lowercase_       )) + [1] + ([0] * len(lowercase_       )) + [1]


     def    UpperCAmelCase_       (		self :     Optional[int]     ,			lowercase_ :     Optional[Any]     ,			lowercase_ :     Dict = None       )						->	List[int]:
      UpperCAmelCase       :	Tuple											= [self.sep_token_id]
      UpperCAmelCase       :	List[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       ) * [0] + len(token_ids_a + sep       ) * [1]




     def    UpperCAmelCase_       (		self :     Optional[Any]     ,			lowercase_ :     Union[str, Any]     ,			lowercase_ :     str = None       )						->	Tuple[str]:
      if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'       )

      if not os.path.isdir(lowercase_       ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
       return
      UpperCAmelCase       :	List[str]											= os.path.join(
          lowercase_     ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

      if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase_       ):
       copyfile(self.vocab_file     ,			lowercase_       )

      return (out_vocab_file,)











"""simple docstring"""





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

import datasets


SCREAMING_SNAKE_CASE__					=   "\\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__					=   "\\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__					=   "\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__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       float(pearsonr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      lowerCAmelCase  						=       float(spearmanr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,  _KWARGS_DESCRIPTION					)
class 		lowercase					(					datasets.Metric					):





      def     _snake_case			(       self )		->     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     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Any:
            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\"]""" )






