



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 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					return float((preds == labels).mean()			)


def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case="binary"			):





					"""simple docstring"""

					_lowerCAmelCase						=    simple_accuracy(__UpperCAmelCase	,						__UpperCAmelCase			)
					_lowerCAmelCase						=    float(fa_score(y_true=__UpperCAmelCase	,						y_pred=__UpperCAmelCase	,						average=__UpperCAmelCase			)			)
					return {
					    "accuracy": acc,
					    "f1": fa,
					}


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    {}
					for id_pred, label in zip(__UpperCAmelCase	,						__UpperCAmelCase			):
										_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						=    [], []
					for question, preds_labels in question_map.items():
										_lowerCAmelCase						=    zip(*__UpperCAmelCase			)
										_lowerCAmelCase						=    fa_score(y_true=__UpperCAmelCase	,						y_pred=__UpperCAmelCase	,						average="""macro"""			)
										fas.append(__UpperCAmelCase			)
										_lowerCAmelCase						=    int(sum(pred == label for pred, label in preds_labels			) == len(__UpperCAmelCase			)			)
										ems.append(__UpperCAmelCase			)
					_lowerCAmelCase						=    float(sum(__UpperCAmelCase			) / len(__UpperCAmelCase			)			)
					_lowerCAmelCase						=    sum(__UpperCAmelCase			) / len(__UpperCAmelCase			)
					_lowerCAmelCase						=    float(fa_score(y_true=__UpperCAmelCase	,						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 					__lowerCAmelCase (     datasets.Metric       ):




				def 							snake_case   (							self		):



									"""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 							snake_case   (							self		):



									"""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 							snake_case   (							self						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									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":
														_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(_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\",]"""		)










from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		"informer"
   a						:							Tuple   		=		{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }




   def __init__(    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = "student_t",		__magic_name__ = "nll",		__magic_name__ = 1,		__magic_name__ = None,		__magic_name__ = "mean",		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 64,		__magic_name__ = 32,		__magic_name__ = 32,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = True,		__magic_name__ = "gelu",		__magic_name__ = 0.05,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 100,		__magic_name__ = 0.02,		__magic_name__=True,		__magic_name__ = "prob",		__magic_name__ = 5,		__magic_name__ = True,		**__magic_name__,		) ->      Optional[int]:






       """simple docstring"""
       # time series specific configuration
       UpperCamelCase__							:				List[Any]          =      prediction_length
       UpperCamelCase__							:				Any          =      context_length or prediction_length
       UpperCamelCase__							:				Optional[int]          =      distribution_output
       UpperCamelCase__							:				Union[str, Any]          =      loss
       UpperCamelCase__							:				Optional[Any]          =      input_size
       UpperCamelCase__							:				Dict          =      num_time_features
       UpperCamelCase__							:				Dict          =      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       UpperCamelCase__							:				Optional[int]          =      scaling
       UpperCamelCase__							:				Any          =      num_dynamic_real_features
       UpperCamelCase__							:				Optional[int]          =      num_static_real_features
       UpperCamelCase__							:				Optional[Any]          =      num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The cardinality should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				str          =      cardinality
       else:
           UpperCamelCase__							:				int          =      [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				Any          =      embedding_dimension
       else:
           UpperCamelCase__							:				Union[str, Any]          =      [min(50,		(cat + 1) // 2							) for cat in self.cardinality]

       UpperCamelCase__							:				Optional[Any]          =      num_parallel_samples

       # Transformer architecture configuration
       UpperCamelCase__							:				Union[str, Any]          =      input_size * len(self.lags_sequence							) + self._number_of_features
       UpperCamelCase__							:				Optional[Any]          =      d_model
       UpperCamelCase__							:				Tuple          =      encoder_attention_heads
       UpperCamelCase__							:				Any          =      decoder_attention_heads
       UpperCamelCase__							:				Dict          =      encoder_ffn_dim
       UpperCamelCase__							:				Optional[Any]          =      decoder_ffn_dim
       UpperCamelCase__							:				str          =      encoder_layers
       UpperCamelCase__							:				Optional[int]          =      decoder_layers

       UpperCamelCase__							:				Optional[Any]          =      dropout
       UpperCamelCase__							:				List[Any]          =      attention_dropout
       UpperCamelCase__							:				Any          =      activation_dropout
       UpperCamelCase__							:				Optional[int]          =      encoder_layerdrop
       UpperCamelCase__							:				Union[str, Any]          =      decoder_layerdrop

       UpperCamelCase__							:				Tuple          =      activation_function
       UpperCamelCase__							:				List[str]          =      init_std

       UpperCamelCase__							:				int          =      use_cache

       # Informer
       UpperCamelCase__							:				Optional[int]          =      attention_type
       UpperCamelCase__							:				Optional[int]          =      sampling_factor
       UpperCamelCase__							:				Dict          =      distil

       super().__init__(is_encoder_decoder=__magic_name__,		**__magic_name__							)




   @property
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       return (
           sum(self.embedding_dimension							)
           + self.num_dynamic_real_features
           + self.num_time_features
           + self.num_static_real_features
           + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
       )

