from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__magic_name__								='''\
@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}
}
'''

__magic_name__								='''\
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.
'''

__magic_name__								='''
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  __UpperCamelCase    (						A ,							A							):
     return float((preds == labels).mean()							)







def  __UpperCamelCase    (						A ,							A ,							A="binary"							):
     UpperCamelCase__										=      simple_accuracy(A ,							A							)
     UpperCamelCase__										=      float(fa_score(y_true=A ,							y_pred=A ,							average=A							)							)
     return {
         "accuracy": acc,
         "f1": fa,
     }







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      {}
     for id_pred, label in zip(A ,							A							):
          UpperCamelCase__										=      f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
          UpperCamelCase__										=      id_pred['''prediction''']
          if question_id in question_map:
               question_map[question_id].append((pred, label)							)
          else:
               UpperCamelCase__										=      [(pred, label)]
     UpperCamelCase__						,       UpperCamelCase__										=      [], []
     for question, preds_labels in question_map.items():
          UpperCamelCase__						,       UpperCamelCase__										=      zip(*A							)
          UpperCamelCase__										=      fa_score(y_true=A ,							y_pred=A ,							average='''macro'''							)
          fas.append(A							)
          UpperCamelCase__										=      int(sum(pred == label for pred, label in preds_labels							) == len(A							)							)
          ems.append(A							)
     UpperCamelCase__										=      float(sum(A							) / len(A							)							)
     UpperCamelCase__										=      sum(A							) / len(A							)
     UpperCamelCase__										=      float(fa_score(y_true=A ,							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     _A   (    datasets.Metric							):






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       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 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       if self.config_name == "axb":
            return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)}
       elif self.config_name == "cb":
            return acc_and_fa(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				fa_avg='''macro'''	)
       elif self.config_name == "record":
            UpperCamelCase__										=      [
                {
                    '''qas''': [
                        {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                        for ref in references
                    ]
                }
            ]
            UpperCamelCase__										=      {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions}
            return evaluate_record(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)[0]
       elif self.config_name == "multirc":
            return evaluate_multirc(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
            return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE_   ,				SCREAMING_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",]'''	)




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__								=logging.get_logger(__name__)  # pylint: disable=invalid-name



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=768	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      proj_size

       UpperCamelCase__										=      CLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      PaintByExampleMapper(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      nn.LayerNorm(config.hidden_size	)
       UpperCamelCase__										=      nn.Linear(config.hidden_size   ,				self.proj_size	)

       # uncondition for scaling
       UpperCamelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(pixel_values=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      clip_output.pooler_output
       UpperCamelCase__										=      self.mapper(latent_states[:, None]	)
       UpperCamelCase__										=      self.final_layer_norm(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.proj_out(SCREAMING_SNAKE_CASE_	)
       if return_uncond_vector:
            return latent_states, self.uncond_vector

       return latent_states



class     _A   (    nn.Module							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      (config.num_hidden_layers + 1) // 5
       UpperCamelCase__										=      config.hidden_size
       UpperCamelCase__										=      1
       UpperCamelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				activation_fn='''gelu'''   ,				attention_bias=SCREAMING_SNAKE_CASE_	)
               for _ in range(SCREAMING_SNAKE_CASE_	)
           ]	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)

       return hidden_states



