



'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__a  			=						"\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

__a  			=						"\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

__a  			=						"\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      return float((preds == labels).mean()						)





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase="binary"						)						->					str:
      snake_case__    :       Union[str, Any]       		= simple_accuracy(_lowerCAmelCase	,  _lowerCAmelCase						)
      snake_case__    :       Optional[Any]       		= float(fa_score(y_true=_lowerCAmelCase	,  y_pred=_lowerCAmelCase	,  average=_lowerCAmelCase						)						)
      return {
          "accuracy": acc,
          "f1": fa,
      }





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Dict:
      snake_case__    :       List[str]       		= {}
      for id_pred, label in zip(_lowerCAmelCase	,  _lowerCAmelCase						):
            snake_case__    :       List[Any]       		= f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
            snake_case__    :       List[str]       		= id_pred["""prediction"""]
            if question_id in question_map:
                  question_map[question_id].append((pred, label)						)
            else:
                  snake_case__    :       int       		= [(pred, label)]
      snake_case__		,							snake_case__    :       Tuple       		= [], []
      for question, preds_labels in question_map.items():
            snake_case__		,							snake_case__    :       Optional[int]       		= zip(*_lowerCAmelCase						)
            snake_case__    :       int       		= fa_score(y_true=_lowerCAmelCase	,  y_pred=_lowerCAmelCase	,  average="""macro"""						)
            fas.append(_lowerCAmelCase						)
            snake_case__    :       Union[str, Any]       		= int(sum(pred == label for pred, label in preds_labels						) == len(_lowerCAmelCase						)						)
            ems.append(_lowerCAmelCase						)
      snake_case__    :       Union[str, Any]       		= float(sum(_lowerCAmelCase						) / len(_lowerCAmelCase						)						)
      snake_case__    :       int       		= sum(_lowerCAmelCase						) / len(_lowerCAmelCase						)
      snake_case__    :       Any       		= float(fa_score(y_true=_lowerCAmelCase	,  y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]						)						)
      return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,						_KWARGS_DESCRIPTION      )
class        UpperCAmelCase_			(			datasets.Metric      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Optional[int]		):
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
                raise KeyError(
                    """You should supply a configuration name selected in """
                    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""		)
          return datasets.MetricInfo(
              description=_DESCRIPTION					,	citation=_CITATION					,	inputs_description=_KWARGS_DESCRIPTION					,	features=datasets.Features(self._get_feature_types()		)					,	codebase_urls=[]					,	reference_urls=[]					,	format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None					,	)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          if self.config_name == "record":
                return {
                    "predictions": {
                        "idx": {
                            "passage": datasets.Value("""int64"""		),
                            "query": datasets.Value("""int64"""		),
                        },
                        "prediction_text": datasets.Value("""string"""		),
                    },
                    "references": {
                        "idx": {
                            "passage": datasets.Value("""int64"""		),
                            "query": datasets.Value("""int64"""		),
                        },
                        "answers": datasets.Sequence(datasets.Value("""string"""		)		),
                    },
                }
          elif self.config_name == "multirc":
                return {
                    "predictions": {
                        "idx": {
                            "answer": datasets.Value("""int64"""		),
                            "paragraph": datasets.Value("""int64"""		),
                            "question": datasets.Value("""int64"""		),
                        },
                        "prediction": datasets.Value("""int64"""		),
                    },
                    "references": datasets.Value("""int64"""		),
                }
          else:
                return {
                    "predictions": datasets.Value("""int64"""		),
                    "references": datasets.Value("""int64"""		),
                }




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Any					,	snake_case_   :	List[Any]		):
          if self.config_name == "axb":
                return {"matthews_correlation": matthews_corrcoef(snake_case_					,	snake_case_		)}
          elif self.config_name == "cb":
                return acc_and_fa(snake_case_					,	snake_case_					,	fa_avg="""macro"""		)
          elif self.config_name == "record":
                snake_case__    :       Optional[int]       		= [
                    {
                        """qas""": [
                            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                            for ref in references
                        ]
                    }
                ]
                snake_case__    :       List[str]       		= {pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
                return evaluate_record(snake_case_					,	snake_case_		)[0]
          elif self.config_name == "multirc":
                return evaluate_multirc(snake_case_					,	snake_case_		)
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                return {"accuracy": simple_accuracy(snake_case_					,	snake_case_		)}
          else:
                raise KeyError(
                    """You should supply a configuration name selected in """
                    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""		)





'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__a  			=						"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
       import torch

       print("Torch version:", torch.__version__)
       print("Cuda available:", torch.cuda.is_available())
       print("Cuda version:", torch.version.cuda)
       print("CuDNN version:", torch.backends.cudnn.version())
       print("Number of GPUs available:", torch.cuda.device_count())
       print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
       print("Torch version:", None)

try:
       import deepspeed

       print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
       print("DeepSpeed version:", None)

try:
       import tensorflow as tf

       print("TensorFlow version:", tf.__version__)
       print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
       print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
       print("TensorFlow version:", None)
