

'''simple docstring'''




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

import datasets


_lowerCAmelCase            =		"\\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"

_lowerCAmelCase            =		"\\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"

_lowerCAmelCase            =		"\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			(					a						,		a					) ->      Tuple:






     '''simple docstring'''
     return float((preds == labels).mean()					)




def 		UpperCamelCase			(					a						,		a					) ->      Tuple:






     '''simple docstring'''
     __magic_name__		  =							simple_accuracy(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)
     __magic_name__		  =							float(fa_score(y_true=__SCREAMING_SNAKE_CASE						,		y_pred=__SCREAMING_SNAKE_CASE					)					)
     return {
         "accuracy": acc,
         "f1": fa,
     }




def 		UpperCamelCase			(					a						,		a					) ->      Optional[int]:






     '''simple docstring'''
     __magic_name__		  =							float(pearsonr(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE					)[0]					)
     __magic_name__		  =							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     _SCREAMING_SNAKE_CASE       (       datasets.Metric      ):



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      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						:       Optional[Any]					, a__						:       str					, a__						:       Dict     ):
      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 logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


A_			      =		logging.getLogger(__name__)






class        lowercase_      (						lowerCAmelCase_							):





     def __init__(     self     :					List[Any]     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Tuple     ,							__lowerCamelCase     :					int=None			):
           super().__init__(
               __lowerCamelCase     ,							question_encoder_tokenizer=__lowerCamelCase     ,							generator_tokenizer=__lowerCamelCase     ,							index=__lowerCamelCase     ,							init_retrieval=__lowerCamelCase     ,							)
           snake_case__       :   Optional[int]	      =    None





     def 			_lowerCAmelCase      (     self     :					List[str]     ,							__lowerCamelCase     :					int			):
           logger.info('initializing retrieval'			)

           # initializing a separate process group for retrieval as the default
           # nccl backend doesn't support gather/scatter operations while gloo
           # is too slow to replace nccl for the core gpu communication
           if dist.is_initialized():
                 logger.info('dist initialized'			)
                 # needs to be set manually
                 snake_case__       :   Any	      =    self._infer_socket_ifname()
                 # avoid clash with the NCCL port
                 snake_case__       :   int	      =    str(distributed_port + 1			)
                 snake_case__       :   Any	      =    dist.new_group(ranks=__lowerCamelCase     ,							backend='gloo'			)

           # initialize retriever only on the main worker
           if not dist.is_initialized() or self._is_main():
                 logger.info('dist not initialized / main'			)
                 self.index.init_index()

           # all processes wait untill the retriever is initialized by the main process
           if dist.is_initialized():
                 torch.distributed.barrier(group=self.process_group			)





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           return dist.get_rank(group=self.process_group			) == 0





     def 			_lowerCAmelCase      (     self     :					Any     ,							__lowerCamelCase     :					Dict     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]=torch.floataa			):
           snake_case__       :   Optional[int]	      =    torch.empty(__lowerCamelCase     ,							dtype=__lowerCamelCase			)
           dist.scatter(__lowerCamelCase     ,							src=0     ,							scatter_list=__lowerCamelCase     ,							group=self.process_group			)
           return target_tensor





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Union[str, Any]	      =    psutil.net_if_addrs()
           # a hacky way to deal with varying network interface names
           snake_case__       :   Any	      =    next((addr for addr in addrs if addr.startswith('e'			))     ,							__lowerCamelCase			)
           return ifname





     def 			_lowerCAmelCase      (     self     :					Tuple     ,							__lowerCamelCase     :					np.ndarray     ,							__lowerCamelCase     :					int			):

           # single GPU training
           if not dist.is_initialized():
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(__lowerCamelCase     ,							__lowerCamelCase			)
                 return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase			)

           # distributed training
           snake_case__       :   Optional[Any]	      =    dist.get_world_size(group=self.process_group			)

           # gather logic
           snake_case__       :   Optional[int]	      =    None
           if self._is_main():
                 snake_case__       :   Optional[int]	      =    [torch.empty(question_hidden_states.shape     ,							dtype=torch.floataa			) for _ in range(__lowerCamelCase			)]
           dist.gather(torch.tensor(__lowerCamelCase			)     ,							dst=0     ,							gather_list=__lowerCamelCase     ,							group=self.process_group			)

           # scatter logic
           snake_case__       :   Union[str, Any]	      =    question_hidden_states.shape[0]
           snake_case__       :   Tuple	      =    []
           snake_case__       :   List[Any]	      =    []
           if self._is_main():
                 assert len(__lowerCamelCase			) == world_size
                 snake_case__      ,							snake_case__       :   Optional[int]	      =    self._main_retrieve(torch.cat(__lowerCamelCase			).numpy()     ,							__lowerCamelCase			)
                 snake_case__      ,							snake_case__       :   Optional[Any]	      =    torch.tensor(__lowerCamelCase			), torch.tensor(__lowerCamelCase			)
                 snake_case__       :   str	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
                 snake_case__       :   Dict	      =    self._chunk_tensor(__lowerCamelCase     ,							__lowerCamelCase			)
           snake_case__       :   Tuple	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs]     ,							target_type=torch.intaa			)
           snake_case__       :   Dict	      =    self._scattered(__lowerCamelCase     ,							[n_queries, n_docs, question_hidden_states.shape[1]]			)

           return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCamelCase			)
