


'''simple docstring'''




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
       import torch

       from transformers import (
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
           MraModel,
       )
       from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 		UpperCAmelCase_      :





     def __init__( self       :      Dict    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[str]=2    ,      UpperCAmelCase__       :      Optional[Any]=8    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      Tuple=True    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      Optional[Any]=9_9    ,      UpperCAmelCase__       :      int=1_6    ,      UpperCAmelCase__       :      Optional[int]=5    ,      UpperCAmelCase__       :      Any=2    ,      UpperCAmelCase__       :      int=3_6    ,      UpperCAmelCase__       :      Tuple="gelu"    ,      UpperCAmelCase__       :      Dict=0.0    ,      UpperCAmelCase__       :      str=0.0    ,      UpperCAmelCase__       :      Optional[int]=5_1_2    ,      UpperCAmelCase__       :      Union[str, Any]=1_6    ,      UpperCAmelCase__       :      Union[str, Any]=2    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      Dict=3    ,      UpperCAmelCase__       :      Optional[int]=4    ,      UpperCAmelCase__       :      Any=None    ,      )				->  int:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			seq_length
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_input_mask
        lowerCAmelCase     =			use_token_type_ids
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			num_labels
        lowerCAmelCase     =			num_choices
        lowerCAmelCase     =			scope





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size    )

        lowerCAmelCase     =			None
        if self.use_input_mask:
           lowerCAmelCase     =			random_attention_mask([self.batch_size, self.seq_length]    )

        lowerCAmelCase     =			None
        if self.use_token_type_ids:
           lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.type_vocab_size    )

        lowerCAmelCase     =			None
        lowerCAmelCase     =			None
        lowerCAmelCase     =			None
        if self.use_labels:
           lowerCAmelCase     =			ids_tensor([self.batch_size]    ,      self.type_sequence_label_size    )
           lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.num_labels    )
           lowerCAmelCase     =			ids_tensor([self.batch_size]    ,      self.num_choices    )

        lowerCAmelCase     =			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 							__UpperCAmelCase      ( self       :      Any    )				->  List[str]:
        return MraConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      max_position_embeddings=self.max_position_embeddings    ,      type_vocab_size=self.type_vocab_size    ,      is_decoder=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Dict:
        lowerCAmelCase     =			self.get_config()
        lowerCAmelCase     =			3_0_0
        return config





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			self.prepare_config_and_inputs()

        lowerCAmelCase     =			True
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2    )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      int    )				->  int:
        lowerCAmelCase     =			MraModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      )				->  List[str]:
        lowerCAmelCase     =			True
        lowerCAmelCase     =			MraModel(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      encoder_hidden_states=UpperCAmelCase__    ,      encoder_attention_mask=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      encoder_hidden_states=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			MraForMaskedLM(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.vocab_size)    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			MraForQuestionAnswering(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      start_positions=UpperCAmelCase__    ,      end_positions=UpperCAmelCase__    ,      )
        self.parent.assertEqual(result.start_logits.shape    ,      (self.batch_size, self.seq_length)    )
        self.parent.assertEqual(result.end_logits.shape    ,      (self.batch_size, self.seq_length)    )





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Any    )				->  List[str]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			MraForSequenceClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]    )				->  Optional[int]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			MraForTokenClassification(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    )				->  Optional[Any]:
        lowerCAmelCase     =			self.num_choices
        lowerCAmelCase     =			MraForMultipleChoice(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			input_ids.unsqueeze(1    ).expand(-1    ,      self.num_choices    ,      -1    ).contiguous()
        lowerCAmelCase     =			token_type_ids.unsqueeze(1    ).expand(-1    ,      self.num_choices    ,      -1    ).contiguous()
        lowerCAmelCase     =			input_mask.unsqueeze(1    ).expand(-1    ,      self.num_choices    ,      -1    ).contiguous()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    ,      )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_choices)    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[Any]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			config_and_inputs
        lowerCAmelCase     =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Optional[Any]		=     (
         (
             MraModel,
             MraForMaskedLM,
             MraForMultipleChoice,
             MraForQuestionAnswering,
             MraForSequenceClassification,
             MraForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase				:	Union[str, Any]		=     False
     lowerCamelCase				:	Dict		=     False
     lowerCamelCase				:	Union[str, Any]		=     False
     lowerCamelCase				:	int		=     False

     lowerCamelCase				:	List[Any]		=     ()





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Optional[Any]:
        lowerCAmelCase     =			MraModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        self.config_tester.run_common_tests()





     def 							__UpperCAmelCase      ( self       :      int    )				->  str:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[str]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           lowerCAmelCase     =			type
           self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Any:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  str:
        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			MraModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )





     @unittest.skip(reason='MRA does not output attentions'    )
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[Any]:
        return




@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        lowerCAmelCase     =			MraModel.from_pretrained('uw-madison/mra-base-512-4'    )
        lowerCAmelCase     =			torch.arange(2_5_6    ).unsqueeze(0    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			torch.Size((1, 2_5_6, 7_6_8)    )
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor(
            [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]    )

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4'    )
        lowerCAmelCase     =			torch.arange(2_5_6    ).unsqueeze(0    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			5_0_2_6_5

        lowerCAmelCase     =			torch.Size((1, 2_5_6, vocab_size)    )
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor(
            [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]    )

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3'    )
        lowerCAmelCase     =			torch.arange(4_0_9_6    ).unsqueeze(0    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			5_0_2_6_5

        lowerCAmelCase     =			torch.Size((1, 4_0_9_6, vocab_size)    )
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor(
            [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]    )

        self.assertTrue(torch.allclose(output[:, :3, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )    )









'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__snake_case    						="""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

__snake_case    						="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

__snake_case    						="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     Union[str, Any]				):
   return float((preds == labels).mean()				)





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str="binary"				):
   lowerCAmelCase     =			simple_accuracy(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			float(fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average=lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			{}
   for id_pred, label in zip(lowerCamelCase	, lowerCamelCase				):
      lowerCAmelCase     =			f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
      lowerCAmelCase     =			id_pred['prediction']
      if question_id in question_map:
         question_map[question_id].append((pred, label)				)
      else:
         lowerCAmelCase     =			[(pred, label)]
   lowerCAmelCase     ,  lowerCAmelCase     =			[], []
   for question, preds_labels in question_map.items():
      lowerCAmelCase     ,  lowerCAmelCase     =			zip(*lowerCamelCase				)
      lowerCAmelCase     =			fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average='macro'				)
      fas.append(lowerCamelCase				)
      lowerCAmelCase     =			int(sum(pred == label for pred, label in preds_labels				) == len(lowerCamelCase				)				)
      ems.append(lowerCamelCase				)
   lowerCAmelCase     =			float(sum(lowerCamelCase				) / len(lowerCamelCase				)				)
   lowerCAmelCase     =			sum(lowerCamelCase				) / len(lowerCamelCase				)
   lowerCAmelCase     =			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		):





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





