


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\\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'

lowerCAmelCase	=							'\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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return float((preds == labels).mean()					)
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="binary"					):


		"""simple docstring"""




		lowercase__						=				simple_accuracy(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		lowercase__						=				float(fa_score(y_true=SCREAMING_SNAKE_CASE						,    y_pred=SCREAMING_SNAKE_CASE						,    average=SCREAMING_SNAKE_CASE					)					)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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





									"""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 				lowerCamelCase_			(							self:     str							)      ->       Optional[Any]:





									"""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 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict							)      ->       str:





									"""simple docstring"""







									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":
											lowercase__						=				[
											    {
											        '''qas''': [
											            {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
											            for ref in references
											        ]
											    }
											]
											lowercase__						=				{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",]'''							)






import copy
import os
from typing import Union

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


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      int  				=			'''git_vision_model'''

							def __init__(							self:     Tuple   ,						UpperCamelCase_:     Optional[int]=768   ,						UpperCamelCase_:     Optional[int]=3_072   ,						UpperCamelCase_:     Optional[int]=12   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3   ,						UpperCamelCase_:     str=224   ,						UpperCamelCase_:     int=16   ,						UpperCamelCase_:     Any="quick_gelu"   ,						UpperCamelCase_:     Union[str, Any]=1E-5   ,						UpperCamelCase_:     Tuple=0.0   ,						UpperCamelCase_:     Tuple=0.02   ,						**UpperCamelCase_:     str   ,						)      ->       Optional[int]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				hidden_size
									lowercase__						=				intermediate_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				num_channels
									lowercase__						=				patch_size
									lowercase__						=				image_size
									lowercase__						=				initializer_range
									lowercase__						=				attention_dropout
									lowercase__						=				layer_norm_eps
									lowercase__						=				hidden_act


							@classmethod
							def 				lowerCamelCase_			(							cls:     Optional[Any]   ,						UpperCamelCase_:     Union[str, os.PathLike]   ,						**UpperCamelCase_:     List[Any]							)      ->       "PretrainedConfig":





									"""simple docstring"""







									cls._set_token_in_kwargs(UpperCamelCase_							)

									lowercase__					,		lowercase__						=				cls.get_config_dict(UpperCamelCase_   ,						**UpperCamelCase_							)

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''							) == "git":
											lowercase__						=				config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls   ,						'''model_type'''							) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'							)

									return cls.from_dict(UpperCamelCase_   ,						**UpperCamelCase_							)


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[int]  				=			'''git'''

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     Optional[Any]=None   ,						UpperCamelCase_:     List[str]=30_522   ,						UpperCamelCase_:     List[Any]=768   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Union[str, Any]=12   ,						UpperCamelCase_:     Any=3_072   ,						UpperCamelCase_:     Any="gelu"   ,						UpperCamelCase_:     List[Any]=0.1   ,						UpperCamelCase_:     Dict=0.1   ,						UpperCamelCase_:     str=1_024   ,						UpperCamelCase_:     Any=0.02   ,						UpperCamelCase_:     str=1E-1_2   ,						UpperCamelCase_:     Optional[int]=0   ,						UpperCamelCase_:     Optional[Any]="absolute"   ,						UpperCamelCase_:     Optional[int]=True   ,						UpperCamelCase_:     List[Any]=False   ,						UpperCamelCase_:     List[Any]=101   ,						UpperCamelCase_:     int=102   ,						UpperCamelCase_:     List[str]=None   ,						**UpperCamelCase_:     int   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									super().__init__(bos_token_id=UpperCamelCase_   ,						eos_token_id=UpperCamelCase_   ,						pad_token_id=UpperCamelCase_   ,						**UpperCamelCase_							)

									if vision_config is None:
											lowercase__						=				{}
											logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''							)

									lowercase__						=				GitVisionConfig(**UpperCamelCase_							)
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				position_embedding_type
									lowercase__						=				use_cache
									lowercase__						=				tie_word_embeddings
									lowercase__						=				num_image_with_embedding

									lowercase__						=				bos_token_id
									lowercase__						=				eos_token_id


							def 				lowerCamelCase_			(							self:     Dict							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				copy.deepcopy(self.__dict__							)
									lowercase__						=				self.vision_config.to_dict()
									lowercase__						=				self.__class__.model_type
									return output


