






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






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






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   Optional[Any] 							=   float(pearsonr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)[0]				)
							_lowercase							:   int 							=   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 				lowerCAmelCase_	(		datasets.Metric			):


				def 						__a					(					self      ):
											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 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											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"]'      )











'''simple docstring'''






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				argparse.ArgumentParser(
          description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""    )
      parser.add_argument("""--file_path"""				,      type=lowerCAmelCase				,      default="""data/dump.txt"""				,      help="""The path to the data."""    )
      parser.add_argument("""--tokenizer_type"""				,      type=lowerCAmelCase				,      default="""bert"""				,      choices=["""bert""", """roberta""", """gpt2"""]    )
      parser.add_argument("""--tokenizer_name"""				,      type=lowerCAmelCase				,      default="""bert-base-uncased"""				,      help="""The tokenizer to use."""    )
      parser.add_argument("""--dump_file"""				,      type=lowerCAmelCase				,      default="""data/dump"""				,      help="""The dump file prefix."""    )
      _lowerCAmelCase					=				parser.parse_args()

      logger.info(f"Loading Tokenizer ({args.tokenizer_name})"    )
      if args.tokenizer_type == "bert":
            _lowerCAmelCase					=				BertTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            _lowerCAmelCase					=				RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            _lowerCAmelCase					=				GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

      logger.info(f"Loading text from {args.file_path}"    )
      with open(args.file_path				,      """r"""				,      encoding="""utf8"""    ) as fp:
            _lowerCAmelCase					=				fp.readlines()

      logger.info("""Start encoding"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples to process."    )

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

            iter += 1
            if iter % interval == 0:
                  _lowerCAmelCase					=				time.time()
                  logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"    )
                  _lowerCAmelCase					=				time.time()
      logger.info("""Finished binarization"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples processed."    )

      _lowerCAmelCase					=				f"{args.dump_file}.{args.tokenizer_name}.pickle"
      _lowerCAmelCase					=				tokenizer.vocab_size
      if vocab_size < (1 << 16):
            _lowerCAmelCase					=				[np.uintaa(lowerCAmelCase    ) for d in rslt]
      else:
            _lowerCAmelCase					=				[np.intaa(lowerCAmelCase    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(f"Dump to {dp_file}"    )
      with open(lowerCAmelCase				,      """wb"""    ) as handle:
            pickle.dump(rslt_				,      lowerCAmelCase				,      protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
      main()






